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Yuval Kluger, PhD

Professor of Pathology

Contact Information

Yuval Kluger, PhD

Office Locations

Mailing Address

  • Pathology

    310 Cedar Street, PO Box 208023

    New Haven, CT 06520-8023

    United States

Kluger Lab

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Research Summary

The research activities in our computational biology and bioinformatics laboratory span diverse areas of data science such as spectral analysis, machine learning, AI, deep learning, signal processing and statistics of high-dimensional data.

We analyze data from high throughput experiments such as single cell RNA sequencing (scRNA-seq), spatial proteomics and transcriptomics, Exome-seq, ChIP-seq, cytometry, chromosome conformation capture sequencing as well as other multiplexed modalities.

Our group develops computational methods that fall into several categories: a) pre-processing tasks such as denoising, removal of batch effects and imputation of missing values, b) scalable algorithms of dimensional reduction techniques for compression and visualization of very large genomics datasets , c) differential analysis tasks for detecting differences across samples with different phenotype/state/condition with the aim of discovering biomarkers, d) bi-clustering and co-organization of large tabulated datasets, e) intra and inter regulation and communication between different cell types, f) signal analysis tools for analyzing data from spatial transcriptomics and proteomics modalities, and g) tree based models in the context of cancer and phylogeny.

We combine our methodological research with practical solutions to analytical tasks emerging in our collaborative projects with basic, translational and clinical researchers. Our collaborations include characterization of the immune system at the single cell level, molecular profiling of melanoma, kidney, breast and lung cancers, interrogating the cellular landscape of brain tissue from donors with HIV and substance use disorders at the single cell level, and studying hair follicle development and regeneration.


Extensive Research Description

We have been working in the broad fields of bioinformatics, and data science. Our main contributions to date all relate to development of spectral, machine learning and statistical methods for analysis of various types of data in genomics, proteomics, and biomarker discovery.

Spectral and graph-based methods for unsupervised & supervised learning: In the past two decades, a common approach to the analysis of data is to first represent it as a graph, and then apply spectral methods to analyze it. In some applications, the data is originally given as a graph (as in the connectivity of Facebook users, or a similarity graph between different proteins). Fundamental theoretical as well as practical questions are how should such data be analyzed, what are the properties of various spectral methods suggested in the literature, and how can multi-scale representations be developed and utilized to such data. We develop state of the art unsupervised spectral methodologies ideal for numerous applications: The first set of methods (Refs. 1-2) allows identification of complex patterns in large data tables by simultaneous organization of rows and columns . Our second set of spectral methods is concerned with ranking and combining multiple predictors without labeled data. This approach provides fundamental results in unsupervised ensemble learning and crowdsourcing (Refs. 3-6). The approach offers a principled way to rank or combine computational genomics pipelines. It is useful for numerous computational genomics tasks; it can remove confusion among end-users, as a substantial fraction of biological results inferred by different pipelines are often in disagreement. Our third set of spectral approaches is concerned with efficient methods for dimensionality reduction of Big Data (BD) matrices (Refs. 7-9). More recently we utilized spectral approaches to address challenges concerning the presence of heteroskedastic noise (Ref. 10), estimation of the rank of count matrices (Ref. 11), detecting significant differences between two high dimensional densities f1 and f0 satisfy the inequality f1>f0 or f1<f0 in the combined sample at different locations in the feature space(Ref. 12), inferring the tree structure of large scale phylogenetic datasets (Ref. 13-14), and phenotypic classification of samples measured in multiplexed spatial omics modalities (Ref. 15).

1. Kluger Y, Basri R, Chang JT, and Gerstein MB. Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions, Genome Research 2003; 13: 703-716. PMCID: PMC317287

2. Mishne, G., Talmon, R., Cohen, I., Coifman, R.R., Kluger, Y., Data-Driven Tree Transforms and Metrics, IEEE Transactions on Signal and Information Processing over Networks. 2017 Aug 23;4(3):451-66.

3. Parisi, F., Strino, F., Nadler, B., and Kluger, Y., Ranking and combining multiple predictors without labeled Data, PNAS (2014) 111(4): 1253-1258; PMID: 24474744; PMCID: PMC3910607

4. Jaffe, A., Nadler, B., Kluger, Y., Estimating the Accuracies of Multiple Classifiers Without Labeled Data, In Artificial Intelligence and Statistics, pp. 407-415. 2015

5. Jaffe, A., Fetaya, E., Nadler, B., Jiang, T., Kluger, Y., Unsupervised Ensemble Learning with Dependent Classifiers, In Artificial Intelligence and Statistics, pp. 351-360. 2016.

6. Shaham, U., Cheng, X., Dror, O., Jaffe, A., Nadler, B., Chang, J., and Kluger, Y. A Deep Learning Approach to Unsupervised Ensemble Learning. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48

7. Linderman,G.C, Rachh, M., Hoskins, J.G., Steinerberger, S., and Kluger, Y., Fast Interpolation-based t-SNE for Improved Visualization of Single-Cell RNA-Seq Data, Nature Methods 2019 Mar;16(3):243-5; PMID: 30742040

8. Li, H., Linderman, G.C., Szlam, A., Stanton, K.P., Kluger, Y., Tygert, M., Algorithm 971: An implementation of a randomized algorithm for principal component analysis, ACM Transactions on Mathematical Software (TOMS) 43.3 (2017): 28. PMCID: PMC5625842.

9. Shaham, U., Stanton, K.P., Li., F., Basri, R.., Nadler, B., Kluger, Y., Spectralnet: Spectral Clustering Using Deep Neural Networks. ICLR 2018

10. B. Landa, R. R. Coifman, and Y. Kluger, "Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise," SIAM Journal on Mathematics of Data Science, pp. 388-413, 2021/01/01 2021.

11. B. Landa, T. T. Zhang, and Y. Kluger, "Biwhitening Reveals the Rank of a Count Matrix," arXiv preprint arXiv:2103.13840, 2021.

12. B. Landa, R. Qu, J. Chang, and Y. Kluger, "Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions," arXiv preprint arXiv:2011.03418, 2020

13. A. Jaffe, N. Amsel, Y. Aizenbud, B. Nadler, J. T. Chang, and Y. Kluger, "Spectral Neighbor Joining for Reconstruction of Latent Tree Models," SIAM Journal on Mathematics of Data Science, vol. 3, pp. 113-141, 2021

14. Y. Aizenbud, A. Jaffe, M. Wang, A. Hu, N. Amsel, B. Nadler, J. T. Chang, and Y. Kluger, "Spectral Top-Down Recovery of Latent Tree Models," arXiv preprint arXiv:2102.13276, 2021.

15. Y.-W. E. Lin, T. Shnitzer, R. Talmon, F. Villarroel-Espindola, S. Desai, K. Schalper, and Y. Kluger, "Graph of graphs analysis for multiplexed data with application to imaging mass cytometry," PLOS Computational Biology, vol. 17, p. e1008741, 2021


Cell specific regulatory networks: In a series of papers (Refs. (16)-(19) our lab addressed the question of identifying cell specific regulatory networks and assessed differential transcriptional activity of known pathways. We were among the first to develop methods to generate condition specific regulatory networks and the first group to use supervised learning techniques to monitor key transcriptional circuitry alterations. Our work on pathway analysis preceded the popular Gene Set Enrichment Analysis tool and highlighted the limitations of interpreting pathway-based statistical analysis. We introduced a novel approach of looking at differences between cell types by analyzing the activity status of regulator-gene pairs, as well as more complex topological relationships between genes, rather than the expression level of individual genes. We were able to identify key transcriptional circuitry alterations by finding pairs of regulating-regulated genes whose coordinated expression activities undergo the most substantial modification from one class of patients to another.

16. Kluger Y, Tuck, DP, Chang, JT, Nakayama, Y, Poddar, R, Kohya, N, Lian, Z, Abdelhakim Ben Nasr H, Halaban, R, Krause, DS, Zhang X, Newburger PE, Weissman SM. Lineage Specificity of Gene Expression Patterns. PNAS 2004; 101:6508-6513. PMID:15096607; PMCID: PMC404075

17. Kim, H., Hu, W., Kluger, Y. Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae, BMC Bioinformatics 2006, 7:165. PMID:16551355; PMCID: PMC1488875

18. Tuck, D., Kluger, H., Kluger, Y., Characterizing disease states from topological properties of transcriptional regulatory networks, BMC Bioinformatics 2006, 7:236. PMID: 16670008; PMCID: PMC1482723

19. Zhou Y., Ferguson J., Chang J.T., Kluger Y., Inter and intra combinatorial regulation by transcription factors and MicroRNAs, BMC Genomics 2007;8(1):396. PMID: 17971223; PMCID: PMC2206040

Algorithms for analyzing genomics and epigenomics sequencing data: In recent years, we were involved in sequencing projects and publications in the fields of cancer genomics, epigenetics, transcriptional regulation and nuclear organization. Our work on picking peak detectors for ChIP-seq data analysis (Refs. (20,21)) provide top performing algorithms, more specific and sensitive than approaches used in the ENCODE project. We also developed an approach for organizing repositories of epigenetic marks using harmonic analysis. This organization reveals variety of binding patterns (Ref. (22)).

Models of cancer evolution assume that among all random mutations there are necessary aberrations that trigger tumor onset, metastatic processes and relapse. Recent efforts to provide a complete genealogical perspective of cancer evolution using experimental techniques have been limited to a small number of fluorescent markers or a small number of single cells. Computational methods can help overcome these limitations. In contrast to the typical phylogeny problems, where species are observed and measured separately, and to the problem of identifying the common cancer genealogy from a panel of samples, my lab addressed the problem of deconvolving a single aggregate signal from a single tumor sample into its subclonal components. Our algorithmic tool is among the very first algorithms addressing this difficult problem (Ref. (23)). It can be used not only in the context of cancer data but also in immunology or mixed cell populations with phylogenetic relationships.

Experiments involving chromosome conformation capture techniques provide support for simultaneous promoter activation, as enhancers often form contacts between each other and the target gene in the same cell. We introduced a bioinformatics novelty in a 4C-seq studies which allows us to detect not only pairwise interactions between different genomic loci but also multi-loci interactions from the same cell (Ref. (24))

20. M. Micsinai, F. Parisi, F. Strino, P. Asp, B.D. Dynlacht, and Y. Kluger, Picking Peak Detectors for Analyzing ChIP-seq experiments, NAR 2012, 1-16, PMID: 22307239; PMCID: PMC3351193

21. Stanton, K.P., Jin, J., Weissman, S.M. and Kluger, Y. Ritornello: High fidelity control-free chip seq peak calling, NAR (2017): gkx799. PMID: 28981893.

22. Stanton, K., Parisi, F., Strino, F., Rabin, N., Asp, P. and Kluger,Y., Arpeggio: Harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures, NAR 2013; 41(16):e161; doi: 10.1093/nar/gkt627. PMID: 23873955. PMCID: PMC3763565

23. Strino, F., Parisi, F., Micsinai, M., and Kluger,Y., TrAp: a Tree Approach for Fingerprinting Subclonal Tumor Composition, NAR 2013;41(17):e165 doi: 10.1093/nar/gkt641. PMID: 23892400. PMCID: PMC3783191

24. Jiang, T., Raviram,R., Snetkova, V., Rocha, P.P., Proudhon, C., Badri, S., Bonneau, R., Skok, J.A., and Kluger, Y., Identification of multi-loci hubs from 4C-seq demonstrates the functional importance of simultaneous interactions, Nucleic Acids Research 2016; doi: 10.1093/nar/gkw568 PMID: 27439714

Algorithms for analyzing omics and single cell sequencing data: Development of methods for analyzing high dimensional data is an important area of biomedical research. We developed methods for preprocessing data in high throughput data, which includes methodologies for data calibration. In recent years, we developed methods and were involved in projects for analyzing multidimensional proteomic data from tumors. In these projects feature extraction of relevant variables is challenging due to sample size and noise level considerations, and have been addressed in a series of papers. Examples include:

25. Shaham, U., Stanton, K.P., Zhao, J., Li., H., Raddassi, K., Montgomery, R., Kluger, Y., Removal of Batch Effects using Distribution-Matching Residual Networks, Bioinformatics (2017): btx196. PMID: 28419223.

26. Li, H., Shaham, U., Yao, Y., Montgomery, R. and Kluger, Y., Gating Mass Cytometry Data by Deep Learning. Bioinformatics (2017): btx448. PMID: 29036374

27. Yamada, Y., Lindenbaum, O., Negahban, S. and Kluger, Y., 2020. “Feature Selection using Stochastic Gates”, Proceedings of the 37th International Conference on Machine Learning (ICML), Vienna, Austria, PMLR 119, 2020

28. Katzman J, Shaham U, Bates J, Cloninger A, Jiang T, Kluger Y. Deep survival: A deep cox proportional hazards network. BMC Medical Research Methodology 18 (1), 24. PMID:29482517 PMCID:PMC5828433

29. J. Zhao, A. Jaffe, H. Li, O. Lindenbaum, E. Sefik, R. Jackson, X. Cheng, R. Flavell, and Y. Kluger, "Detection of differentially abundant cell subpopulations in scRNA-seq data," PNAS June 1, 2021 118 (22) e2100293118

30. G. C. Linderman, J. Zhao, and Y. Kluger, "Zero-preserving imputation of scRNA-seq data using low-rank approximation," bioRxiv, p. 397588, 2018.

31. J. Yang, O. Lindenbaum, and Y. Kluger, "Locally Sparse Networks for Interpretable Predictions" arXiv:2106.06468, 2021.

Coauthors

Research Interests

Artificial Intelligence; Classification; Hemic and Immune Systems; Immune System Diseases; Neoplasms; Neural Networks, Computer; Computational Biology; Data Compression; Machine Learning; Deep Learning; Data Science; Data Visualization

Selected Publications

  • Cyclin-Dependent Kinase 1 Activity Is a Driver of Cyst Growth in Polycystic Kidney Disease.Zhang C, Balbo B, Ma M, Zhao J, Tian X, Kluger Y, Somlo S. Cyclin-Dependent Kinase 1 Activity Is a Driver of Cyst Growth in Polycystic Kidney Disease. Journal Of The American Society Of Nephrology : JASN 2021, 32:41-51.
  • Alignment free identification of clones in B cell receptor repertoires.Lindenbaum O, Nouri N, Kluger Y, Kleinstein SH. Alignment free identification of clones in B cell receptor repertoires. Nucleic Acids Research 2021, 49:e21.
  • An in vivo screen of noncoding loci reveals that Daedalus is a gatekeeper of an Ikaros-dependent checkpoint during haematopoiesis.Harman CCD, Bailis W, Zhao J, Hill L, Qu R, Jackson RP, Shyer JA, Steach HR, Kluger Y, Goff LA, Rinn JL, Williams A, Henao-Mejia J, Flavell RA. An in vivo screen of noncoding loci reveals that Daedalus is a gatekeeper of an Ikaros-dependent checkpoint during haematopoiesis. Proceedings Of The National Academy Of Sciences Of The United States Of America 2021, 118.
  • PLEKHA5 regulates tumor growth in metastatic melanoma.Zhang H, Zhu H, Deng G, Zito CR, Oria VO, Rane CK, Zhang S, Weiss SA, Tran T, Adeniran A, Zhang F, Zhou J, Kluger Y, Bosenberg MW, Kluger HM, Jilaveanu LB. PLEKHA5 regulates tumor growth in metastatic melanoma. Cancer 2020, 126:1016-1030.
  • Enteric Nervous System-Derived IL-18 Orchestrates Mucosal Barrier Immunity.Jarret A, Jackson R, Duizer C, Healy ME, Zhao J, Rone JM, Bielecki P, Sefik E, Roulis M, Rice T, Sivanathan KN, Zhou T, Solis AG, Honcharova-Biletska H, Vélez K, Hartner S, Low JS, Qu R, de Zoete MR, Palm NW, Ring AM, Weber A, Moor AE, Kluger Y, Nowarski R, Flavell RA. Enteric Nervous System-Derived IL-18 Orchestrates Mucosal Barrier Immunity. Cell 2020, 180:50-63.e12.
  • Enteric Nervous System-Derived IL-18 Orchestrates Mucosal Barrier Immunity.Jarret A, Jackson R, Duizer C, Healy ME, Zhao J, Rone JM, Bielecki P, Sefik E, Roulis M, Rice T, Sivanathan KN, Zhou T, Solis AG, Honcharova-Biletska H, Vélez K, Hartner S, Low JS, Qu R, de Zoete MR, Palm NW, Ring AM, Weber A, Moor AE, Kluger Y, Nowarski R, Flavell RA. Enteric Nervous System-Derived IL-18 Orchestrates Mucosal Barrier Immunity. Cell 2020, 180:813-814.
  • Pembrolizumab for management of patients with NSCLC and brain metastases: long-term results and biomarker analysis from a non-randomised, open-label, phase 2 trial.Goldberg SB, Schalper KA, Gettinger SN, Mahajan A, Herbst RS, Chiang AC, Lilenbaum R, Wilson FH, Omay SB, Yu JB, Jilaveanu L, Tran T, Pavlik K, Rowen E, Gerrish H, Komlo A, Gupta R, Wyatt H, Ribeiro M, Kluger Y, Zhou G, Wei W, Chiang VL, Kluger HM. Pembrolizumab for management of patients with NSCLC and brain metastases: long-term results and biomarker analysis from a non-randomised, open-label, phase 2 trial. The Lancet. Oncology 2020, 21:655-663.
  • Paracrine orchestration of intestinal tumorigenesis by a mesenchymal niche.Roulis M, Kaklamanos A, Schernthanner M, Bielecki P, Zhao J, Kaffe E, Frommelt LS, Qu R, Knapp MS, Henriques A, Chalkidi N, Koliaraki V, Jiao J, Brewer JR, Bacher M, Blackburn HN, Zhao X, Breyer RM, Aidinis V, Jain D, Su B, Herschman HR, Kluger Y, Kollias G, Flavell RA. Paracrine orchestration of intestinal tumorigenesis by a mesenchymal niche. Nature 2020, 580:524-529.
  • Germline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden.Qing T, Mohsen H, Marczyk M, Ye Y, O'Meara T, Zhao H, Townsend JP, Gerstein M, Hatzis C, Kluger Y, Pusztai L. Germline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden. Nature Communications 2020, 11:2438.
  • Impact of healthcare worker shift scheduling on workforce preservation during the COVID-19 pandemic.Kluger DM, Aizenbud Y, Jaffe A, Parisi F, Aizenbud L, Minsky-Fenick E, Kluger JM, Farhadian S, Kluger HM, Kluger Y. Impact of healthcare worker shift scheduling on workforce preservation during the COVID-19 pandemic. Infection Control And Hospital Epidemiology 2020, 41:1443-1445.
  • Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells.Marczyk M, Patwardhan GA, Zhao J, Qu R, Li X, Wali VB, Gupta AK, Pillai MM, Kluger Y, Yan Q, Hatzis C, Pusztai L, Gunasekharan V. Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells. Cancers 2020, 12.
  • Randomized near-neighbor graphs, giant components and applications in data science.Linderman GC, Mishne G, Jaffe A, Kluger Y, Steinerberger S. Randomized near-neighbor graphs, giant components and applications in data science. Journal Of Applied Probability 2020, 57:458-476.
  • Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.Kobak D, Linderman G, Steinerberger S, Kluger Y, Berens P. Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations. Machine Learning And Knowledge Discovery In Databases : European Conference, ECML PKDD ... : Proceedings. ECML PKDD (Conference) 2020, 11906:124-139.
  • Long-Term Survival of Patients With Melanoma With Active Brain Metastases Treated With Pembrolizumab on a Phase II Trial.Kluger HM, Chiang V, Mahajan A, Zito CR, Sznol M, Tran T, Weiss SA, Cohen JV, Yu J, Hegde U, Perrotti E, Anderson G, Ralabate A, Kluger Y, Wei W, Goldberg SB, Jilaveanu LB. Long-Term Survival of Patients With Melanoma With Active Brain Metastases Treated With Pembrolizumab on a Phase II Trial. Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology 2019, 37:52-60.
  • Single-Cell Analysis Reveals a Hair Follicle Dermal Niche Molecular Differentiation Trajectory that Begins Prior to Morphogenesis.Gupta K, Levinsohn J, Linderman G, Chen D, Sun TY, Dong D, Taketo MM, Bosenberg M, Kluger Y, Choate K, Myung P. Single-Cell Analysis Reveals a Hair Follicle Dermal Niche Molecular Differentiation Trajectory that Begins Prior to Morphogenesis. Developmental Cell 2019, 48:17-31.e6.
  • A highly efficient and faithful MDS patient-derived xenotransplantation model for pre-clinical studies.Song Y, Rongvaux A, Taylor A, Jiang T, Tebaldi T, Balasubramanian K, Bagale A, Terzi YK, Gbyli R, Wang X, Fu X, Gao Y, Zhao J, Podoltsev N, Xu M, Neparidze N, Wong E, Torres R, Bruscia EM, Kluger Y, Manz MG, Flavell RA, Halene S. A highly efficient and faithful MDS patient-derived xenotransplantation model for pre-clinical studies. Nature Communications 2019, 10:366.
  • Multiplexed (18-Plex) Measurement of Signaling Targets and Cytotoxic T Cells in Trastuzumab-Treated Patients using Imaging Mass Cytometry.Carvajal-Hausdorf DE, Patsenker J, Stanton KP, Villarroel-Espindola F, Esch A, Montgomery RR, Psyrri A, Kalogeras KT, Kotoula V, Foutzilas G, Schalper KA, Kluger Y, Rimm DL. Multiplexed (18-Plex) Measurement of Signaling Targets and Cytotoxic T Cells in Trastuzumab-Treated Patients using Imaging Mass Cytometry. Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research 2019, 25:3054-3062.
  • Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data.Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nature Methods 2019, 16:243-245.
  • Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function.Bailis W, Shyer JA, Zhao J, Canaveras JCG, Al Khazal FJ, Qu R, Steach HR, Bielecki P, Khan O, Jackson R, Kluger Y, Maher LJ, Rabinowitz J, Craft J, Flavell RA. Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function. Nature 2019, 571:403-407.
  • Effector TH17 Cells Give Rise to Long-Lived TRM Cells that Are Essential for an Immediate Response against Bacterial Infection.Amezcua Vesely MC, Pallis P, Bielecki P, Low JS, Zhao J, Harman CCD, Kroehling L, Jackson R, Bailis W, Licona-Limón P, Xu H, Iijima N, Pillai PS, Kaplan DH, Weaver CT, Kluger Y, Kowalczyk MS, Iwasaki A, Pereira JP, Esplugues E, Gagliani N, Flavell RA. Effector TH17 Cells Give Rise to Long-Lived TRM Cells that Are Essential for an Immediate Response against Bacterial Infection. Cell 2019, 178:1176-1188.e15.
  • Author Correction: Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function.Bailis W, Shyer JA, Zhao J, Canaveras JCG, Al Khazal FJ, Qu R, Steach HR, Bielecki P, Khan O, Jackson R, Kluger Y, Maher LJ, Rabinowitz J, Craft J, Flavell RA. Author Correction: Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function. Nature 2019, 573:E2.
  • The Proximal Humeral Ossification System Improves Assessment of Maturity in Patients with Scoliosis.Li DT, Linderman GC, Cui JJ, DeVries S, Nicholson AD, Li E, Petit L, Kahan JB, Talty R, Kluger Y, Cooperman DR, Smith BG. The Proximal Humeral Ossification System Improves Assessment of Maturity in Patients with Scoliosis. The Journal Of Bone And Joint Surgery. American Volume 2019, 101:1868-1874.
  • Single-cell connectomic analysis of adult mammalian lungs.Raredon MSB, Adams TS, Suhail Y, Schupp JC, Poli S, Neumark N, Leiby KL, Greaney AM, Yuan Y, Horien C, Linderman G, Engler AJ, Boffa DJ, Kluger Y, Rosas IO, Levchenko A, Kaminski N, Niklason LE. Single-cell connectomic analysis of adult mammalian lungs. Science Advances 2019, 5:eaaw3851.
  • DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology 2018, 18:24.
  • A Non-canonical BCOR-PRC1.1 Complex Represses Differentiation Programs in Human ESCs.Wang Z, Gearhart MD, Lee YW, Kumar I, Ramazanov B, Zhang Y, Hernandez C, Lu AY, Neuenkirchen N, Deng J, Jin J, Kluger Y, Neubert TA, Bardwell VJ, Ivanova NB. A Non-canonical BCOR-PRC1.1 Complex Represses Differentiation Programs in Human ESCs. Cell Stem Cell 2018, 22:235-251.e9.
  • KLRG1+ Effector CD8+ T Cells Lose KLRG1, Differentiate into All Memory T Cell Lineages, and Convey Enhanced Protective Immunity.Herndler-Brandstetter D, Ishigame H, Shinnakasu R, Plajer V, Stecher C, Zhao J, Lietzenmayer M, Kroehling L, Takumi A, Kometani K, Inoue T, Kluger Y, Kaech SM, Kurosaki T, Okada T, Flavell RA. KLRG1+ Effector CD8+ T Cells Lose KLRG1, Differentiate into All Memory T Cell Lineages, and Convey Enhanced Protective Immunity. Immunity 2018, 48:716-729.e8.
  • Genomic Heterogeneity and the Small Renal Mass.Ueno D, Xie Z, Boeke M, Syed J, Nguyen KA, McGillivray P, Adeniran A, Humphrey PA, Dancik GM, Kluger Y, Liu Z, Kluger HM, Shuch B. Genomic Heterogeneity and the Small Renal Mass. Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research 2018, 24:4137-4144.
  • Data-Driven Tree Transforms and Metrics.Mishne G, Talmon R, Cohen I, Coifman RR, Kluger Y. Data-Driven Tree Transforms and Metrics. IEEE Transactions On Signal And Information Processing Over Networks 2018, 4:451-466.
  • Erratum to: Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA).Sartelli M, Weber DG, Ruppé E, Bassetti M, Wright BJ, Ansaloni L, Catena F, Coccolini F, Abu-Zidan FM, Coimbra R, Moore EE, Moore FA, Maier RV, De Waele JJ, Kirkpatrick AW, Griffiths EA, Eckmann C, Brink AJ, Mazuski JE, May AK, Sawyer RG, Mertz D, Montravers P, Kumar A, Roberts JA, Vincent JL, Watkins RR, Lowman W, Spellberg B, Abbott IJ, Adesunkanmi AK, Al-Dahir S, Al-Hasan MN, Agresta F, Althani AA, Ansari S, Ansumana R, Augustin G, Bala M, Balogh ZJ, Baraket O, Bhangu A, Beltrán MA, Bernhard M, Biffl WL, Boermeester MA, Brecher SM, Cherry-Bukowiec JR, Buyne OR, Cainzos MA, Cairns KA, Camacho-Ortiz A, Chandy SJ, Che Jusoh A, Chichom-Mefire A, Colijn C, Corcione F, Cui Y, Curcio D, Delibegovic S, Demetrashvili Z, De Simone B, Dhingra S, Diaz JJ, Di Carlo I, Dillip A, Di Saverio S, Doyle MP, Dorj G, Dogjani A, Dupont H, Eachempati SR, Enani MA, Egiev VN, Elmangory MM, Ferrada P, Fitchett JR, Fraga GP, Guessennd N, Giamarellou H, Ghnnam W, Gkiokas G, Goldberg SR, Gomes CA, Gomi H, Guzmán-Blanco M, Haque M, Hansen S, Hecker A, Heizmann WR, Herzog T, Hodonou AM, Hong SK, Kafka-Ritsch R, Kaplan LJ, Kapoor G, Karamarkovic A, Kees MG, Kenig J, Kiguba R, Kim PK, Kluger Y, Khokha V, Koike K, Kok KY, Kong V, Knox MC, Inaba K, Isik A, Iskandar K, Ivatury RR, Labbate M, Labricciosa FM, Laterre PF, Latifi R, Lee JG, Lee YR, Leone M, Leppaniemi A, Li Y, Liang SY, Loho T, Maegele M, Malama S, Marei HE, Martin-Loeches I, Marwah S, Massele A, McFarlane M, Melo RB, Negoi I, Nicolau DP, Nord CE, Ofori-Asenso R, Omari AH, Ordonez CA, Ouadii M, Pereira Júnior GA, Piazza D, Pupelis G, Rawson TM, Rems M, Rizoli S, Rocha C, Sakakushev B, Sanchez-Garcia M, Sato N, Segovia Lohse HA, Sganga G, Siribumrungwong B, Shelat VG, Soreide K, Soto R, Talving P, Tilsed JV, Timsit JF, Trueba G, Trung NT, Ulrych J, van Goor H, Vereczkei A, Vohra RS, Wani I, Uhl W, Xiao Y, Yuan KC, Zachariah SK, Zahar JR, Zakrison TL, Corcione A, Melotti RM, Viscoli C, Viale P. Erratum to: Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA). World Journal Of Emergency Surgery : WJES 2017, 12:35.
  • Nlrp9b inflammasome restricts rotavirus infection in intestinal epithelial cells.Zhu S, Ding S, Wang P, Wei Z, Pan W, Palm NW, Yang Y, Yu H, Li HB, Wang G, Lei X, de Zoete MR, Zhao J, Zheng Y, Chen H, Zhao Y, Jurado KA, Feng N, Shan L, Kluger Y, Lu J, Abraham C, Fikrig E, Greenberg HB, Flavell RA. Nlrp9b inflammasome restricts rotavirus infection in intestinal epithelial cells. Nature 2017, 546:667-670.
  • Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling.Stanton KP, Jin J, Lederman RR, Weissman SM, Kluger Y. Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling. Nucleic Acids Research 2017, 45:e173.
  • PD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors.Kluger HM, Zito CR, Turcu G, Baine M, Zhang H, Adeniran A, Sznol M, Rimm DL, Kluger Y, Chen L, Cohen JV, Jilaveanu LB. PD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors. Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research 2017, 23:4270-4279.
  • Gating mass cytometry data by deep learning.Li H, Shaham U, Stanton KP, Yao Y, Montgomery RR, Kluger Y. Gating mass cytometry data by deep learning. Bioinformatics (Oxford, England) 2017, 33:3423-3430.
  • Proceedings of resources for optimal care of acute care and emergency surgery consensus summit Donegal Ireland.Sugrue M, Maier R, Moore EE, Boermeester M, Catena F, Coccolini F, Leppaniemi A, Peitzman A, Velmahos G, Ansaloni L, Abu-Zidan F, Balfe P, Bendinelli C, Biffl W, Bowyer M, DeMoya M, De Waele J, Di Saverio S, Drake A, Fraga GP, Hallal A, Henry C, Hodgetts T, Hsee L, Huddart S, Kirkpatrick AW, Kluger Y, Lawler L, Malangoni MA, Malbrain M, MacMahon P, Mealy K, O'Kane M, Loughlin P, Paduraru M, Pearce L, Pereira BM, Priyantha A, Sartelli M, Soreide K, Steele C, Thomas S, Vincent JL, Woods L. Proceedings of resources for optimal care of acute care and emergency surgery consensus summit Donegal Ireland. World Journal Of Emergency Surgery : WJES 2017, 12:47.
  • Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis.Li H, Linderman GC, Szlam A, Stanton KP, Kluger Y, Tygert M. Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis. ACM Transactions On Mathematical Software. Association For Computing Machinery 2017, 43.
  • m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways.Li HB, Tong J, Zhu S, Batista PJ, Duffy EE, Zhao J, Bailis W, Cao G, Kroehling L, Chen Y, Wang G, Broughton JP, Chen YG, Kluger Y, Simon MD, Chang HY, Yin Z, Flavell RA. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 2017, 548:338-342.
  • Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors.Yang J, Tanaka Y, Seay M, Li Z, Jin J, Garmire LX, Zhu X, Taylor A, Li W, Euskirchen G, Halene S, Kluger Y, Snyder MP, Park IH, Pan X, Weissman SM. Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors. Nucleic Acids Research 2017, 45:1281-1296.
  • Removal of batch effects using distribution-matching residual networks.Shaham U, Stanton KP, Zhao J, Li H, Raddassi K, Montgomery R, Kluger Y. Removal of batch effects using distribution-matching residual networks. Bioinformatics (Oxford, England) 2017, 33:2539-2546.
  • Non-malignant respiratory epithelial cells preferentially proliferate from resected non-small cell lung cancer specimens cultured under conditionally reprogrammed conditions.Gao B, Huang C, Kernstine K, Pelekanou V, Kluger Y, Jiang T, Peters-Hall JR, Coquelin M, Girard L, Zhang W, Huffman K, Oliver D, Kinose F, Haura E, Teer JK, Rix U, Le AT, Aisner DL, Varella-Garcia M, Doebele RC, Covington KR, Hampton OA, Doddapaneni HV, Jayaseelan JC, Hu J, Wheeler DA, Shay JW, Rimm DL, Gazdar A, Minna JD. Non-malignant respiratory epithelial cells preferentially proliferate from resected non-small cell lung cancer specimens cultured under conditionally reprogrammed conditions. Oncotarget 2017, 8:11114-11126.
  • Identification of multi-loci hubs from 4C-seq demonstrates the functional importance of simultaneous interactions.Jiang T, Raviram R, Snetkova V, Rocha PP, Proudhon C, Badri S, Bonneau R, Skok JA, Kluger Y. Identification of multi-loci hubs from 4C-seq demonstrates the functional importance of simultaneous interactions. Nucleic Acids Research 2016, 44:8714-8725.
  • A Molecular Chipper technology for CRISPR sgRNA library generation and functional mapping of noncoding regions.Cheng J, Roden CA, Pan W, Zhu S, Baccei A, Pan X, Jiang T, Kluger Y, Weissman SM, Guo S, Flavell RA, Ding Y, Lu J. A Molecular Chipper technology for CRISPR sgRNA library generation and functional mapping of noncoding regions. Nature Communications 2016, 7:11178.
  • Active and Inactive Enhancers Cooperate to Exert Localized and Long-Range Control of Gene Regulation.Proudhon C, Snetkova V, Raviram R, Lobry C, Badri S, Jiang T, Hao B, Trimarchi T, Kluger Y, Aifantis I, Bonneau R, Skok JA. Active and Inactive Enhancers Cooperate to Exert Localized and Long-Range Control of Gene Regulation. Cell Reports 2016, 15:2159-2169.
  • PLEKHA5 as a Biomarker and Potential Mediator of Melanoma Brain Metastasis.Jilaveanu LB, Parisi F, Barr ML, Zito CR, Cruz-Munoz W, Kerbel RS, Rimm DL, Bosenberg MW, Halaban R, Kluger Y, Kluger HM. PLEKHA5 as a Biomarker and Potential Mediator of Melanoma Brain Metastasis. Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research 2015, 21:2138-47.
  • MET Expression in Primary and Metastatic Clear Cell Renal Cell Carcinoma: Implications of Correlative Biomarker Assessment to MET Pathway Inhibitors.Shuch B, Falbo R, Parisi F, Adeniran A, Kluger Y, Kluger HM, Jilaveanu LB. MET Expression in Primary and Metastatic Clear Cell Renal Cell Carcinoma: Implications of Correlative Biomarker Assessment to MET Pathway Inhibitors. BioMed Research International 2015, 2015:192406.
  • Preanalytical variables and phosphoepitope expression in FFPE tissue: quantitative epitope assessment after variable cold ischemic time.Vassilakopoulou M, Parisi F, Siddiqui S, England AM, Zarella ER, Anagnostou V, Kluger Y, Hicks DG, Rimm DL, Neumeister VM. Preanalytical variables and phosphoepitope expression in FFPE tissue: quantitative epitope assessment after variable cold ischemic time. Laboratory Investigation; A Journal Of Technical Methods And Pathology 2015, 95:334-41.
  • Regulation of glutamine carrier proteins by RNF5 determines breast cancer response to ER stress-inducing chemotherapies.Jeon YJ, Khelifa S, Ratnikov B, Scott DA, Feng Y, Parisi F, Ruller C, Lau E, Kim H, Brill LM, Jiang T, Rimm DL, Cardiff RD, Mills GB, Smith JW, Osterman AL, Kluger Y, Ronai ZA. Regulation of glutamine carrier proteins by RNF5 determines breast cancer response to ER stress-inducing chemotherapies. Cancer Cell 2015, 27:354-69.
  • Comparative FAIRE-seq analysis reveals distinguishing features of the chromatin structure of ground state- and primed-pluripotent cells.Murtha M, Strino F, Tokcaer-Keskin Z, Sumru Bayin N, Shalabi D, Xi X, Kluger Y, Dailey L. Comparative FAIRE-seq analysis reveals distinguishing features of the chromatin structure of ground state- and primed-pluripotent cells. Stem Cells (Dayton, Ohio) 2015, 33:378-91.
  • Interactions with RNA direct the Polycomb group protein SCML2 to chromatin where it represses target genes.Bonasio R, Lecona E, Narendra V, Voigt P, Parisi F, Kluger Y, Reinberg D. Interactions with RNA direct the Polycomb group protein SCML2 to chromatin where it represses target genes. ELife 2014, 3:e02637.
  • NY-ESO-1 as a potential immunotherapeutic target in renal cell carcinoma.Giesen E, Jilaveanu LB, Parisi F, Kluger Y, Camp RL, Kluger HM. NY-ESO-1 as a potential immunotherapeutic target in renal cell carcinoma. Oncotarget 2014, 5:5209-17.
  • PTEN functions as a melanoma tumor suppressor by promoting host immune response.Dong Y, Richards JA, Gupta R, Aung PP, Emley A, Kluger Y, Dogra SK, Mahalingam M, Wajapeyee N. PTEN functions as a melanoma tumor suppressor by promoting host immune response. Oncogene 2014, 33:4632-42.
  • Ranking and combining multiple predictors without labeled data.Parisi F, Strino F, Nadler B, Kluger Y. Ranking and combining multiple predictors without labeled data. Proceedings Of The National Academy Of Sciences Of The United States Of America 2014, 111:1253-8.
  • Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer.Jiang T, Shi W, Natowicz R, Ononye SN, Wali VB, Kluger Y, Pusztai L, Hatzis C. Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer. BMC Genomics 2014, 15:876.
  • PD-L1 Expression in Clear Cell Renal Cell Carcinoma: An Analysis of Nephrectomy and Sites of Metastases.Jilaveanu LB, Shuch B, Zito CR, Parisi F, Barr M, Kluger Y, Chen L, Kluger HM. PD-L1 Expression in Clear Cell Renal Cell Carcinoma: An Analysis of Nephrectomy and Sites of Metastases. Journal Of Cancer 2014, 5:166-72.
  • A tissue quality index: an intrinsic control for measurement of effects of preanalytical variables on FFPE tissue.Neumeister VM, Parisi F, England AM, Siddiqui S, Anagnostou V, Zarrella E, Vassilakopolou M, Bai Y, Saylor S, Sapino A, Kluger Y, Hicks DG, Bussolati G, Kwei S, Rimm DL. A tissue quality index: an intrinsic control for measurement of effects of preanalytical variables on FFPE tissue. Laboratory Investigation; A Journal Of Technical Methods And Pathology 2014, 94:467-74.
  • Oct-1 regulates IL-17 expression by directing interchromosomal associations in conjunction with CTCF in T cells.Kim LK, Esplugues E, Zorca CE, Parisi F, Kluger Y, Kim TH, Galjart NJ, Flavell RA. Oct-1 regulates IL-17 expression by directing interchromosomal associations in conjunction with CTCF in T cells. Molecular Cell 2014, 54:56-66.
  • FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells.Murtha M, Tokcaer-Keskin Z, Tang Z, Strino F, Chen X, Wang Y, Xi X, Basilico C, Brown S, Bonneau R, Kluger Y, Dailey L. FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells. Nature Methods 2014, 11:559-65.
  • The RAG2 C-terminus and ATM protect genome integrity by controlling antigen receptor gene cleavage.Chaumeil J, Micsinai M, Ntziachristos P, Roth DB, Aifantis I, Kluger Y, Deriano L, Skok JA. The RAG2 C-terminus and ATM protect genome integrity by controlling antigen receptor gene cleavage. Nature Communications 2013, 4:2231.
  • TrAp: a tree approach for fingerprinting subclonal tumor composition.Strino F, Parisi F, Micsinai M, Kluger Y. TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Research 2013, 41:e165.
  • Higher-order looping and nuclear organization of Tcra facilitate targeted rag cleavage and regulated rearrangement in recombination centers.Chaumeil J, Micsinai M, Ntziachristos P, Deriano L, Wang JM, Ji Y, Nora EP, Rodesch MJ, Jeddeloh JA, Aifantis I, Kluger Y, Schatz DG, Skok JA. Higher-order looping and nuclear organization of Tcra facilitate targeted rag cleavage and regulated rearrangement in recombination centers. Cell Reports 2013, 3:359-70.
  • Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures.Stanton KP, Parisi F, Strino F, Rabin N, Asp P, Kluger Y. Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures. Nucleic Acids Research 2013, 41:e161.
  • Expression of drug targets in primary and matched metastatic renal cell carcinoma tumors.Aziz SA, Sznol JA, Adeniran A, Parisi F, Kluger Y, Camp RL, Kluger HM. Expression of drug targets in primary and matched metastatic renal cell carcinoma tumors. BMC Clinical Pathology 2013, 13:3.
  • SFMBT1 functions with LSD1 to regulate expression of canonical histone genes and chromatin-related factors.Zhang J, Bonasio R, Strino F, Kluger Y, Holloway JK, Modzelewski AJ, Cohen PE, Reinberg D. SFMBT1 functions with LSD1 to regulate expression of canonical histone genes and chromatin-related factors. Genes & Development 2013, 27:749-66.
  • Response to Casellas et al.Rocha PP, Micsinai M, Kluger Y, Skok JA. Response to Casellas et al. Molecular Cell 2013, 51:277-8.
  • Integrated analysis of tumor samples sheds light on tumor heterogeneity.Parisi F, Micsinai M, Strino F, Ariyan S, Narayan D, Bacchiocchi A, Cheng E, Xu F, Li P, Kluger H, Halaban R, Kluger Y. Integrated analysis of tumor samples sheds light on tumor heterogeneity. The Yale Journal Of Biology And Medicine 2012, 85:347-61.
  • Close proximity to Igh is a contributing factor to AID-mediated translocations.Rocha PP, Micsinai M, Kim JR, Hewitt SL, Souza PP, Trimarchi T, Strino F, Parisi F, Kluger Y, Skok JA. Close proximity to Igh is a contributing factor to AID-mediated translocations. Molecular Cell 2012, 47:873-85.
  • IL-7 functionally segregates the pro-B cell stage by regulating transcription of recombination mediators across cell cycle.Johnson K, Chaumeil J, Micsinai M, Wang JM, Ramsey LB, Baracho GV, Rickert RC, Strino F, Kluger Y, Farrar MA, Skok JA. IL-7 functionally segregates the pro-B cell stage by regulating transcription of recombination mediators across cell cycle. Journal Of Immunology (Baltimore, Md. : 1950) 2012, 188:6084-92.
  • Picking ChIP-seq peak detectors for analyzing chromatin modification experiments.Micsinai M, Parisi F, Strino F, Asp P, Dynlacht BD, Kluger Y. Picking ChIP-seq peak detectors for analyzing chromatin modification experiments. Nucleic Acids Research 2012, 40:e70.
  • PCGF homologs, CBX proteins, and RYBP define functionally distinct PRC1 family complexes.Gao Z, Zhang J, Bonasio R, Strino F, Sawai A, Parisi F, Kluger Y, Reinberg D. PCGF homologs, CBX proteins, and RYBP define functionally distinct PRC1 family complexes. Molecular Cell 2012, 45:344-56.
  • Quantitative assessment of effect of preanalytic cold ischemic time on protein expression in breast cancer tissues.Neumeister VM, Anagnostou V, Siddiqui S, England AM, Zarrella ER, Vassilakopoulou M, Parisi F, Kluger Y, Hicks DG, Rimm DL. Quantitative assessment of effect of preanalytic cold ischemic time on protein expression in breast cancer tissues. Journal Of The National Cancer Institute 2012, 104:1815-24.
  • Plasma markers for identifying patients with metastatic melanoma.Kluger HM, Hoyt K, Bacchiocchi A, Mayer T, Kirsch J, Kluger Y, Sznol M, Ariyan S, Molinaro A, Halaban R. Plasma markers for identifying patients with metastatic melanoma. Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research 2011, 17:2417-25.
  • VDA, a method of choosing a better algorithm with fewer validations.Strino F, Parisi F, Kluger Y. VDA, a method of choosing a better algorithm with fewer validations. PloS One 2011, 6:e26074.
  • Genome-wide remodeling of the epigenetic landscape during myogenic differentiation.Asp P, Blum R, Vethantham V, Parisi F, Micsinai M, Cheng J, Bowman C, Kluger Y, Dynlacht BD. Genome-wide remodeling of the epigenetic landscape during myogenic differentiation. Proceedings Of The National Academy Of Sciences Of The United States Of America 2011, 108:E149-58.
  • Detecting copy number status and uncovering subclonal markers in heterogeneous tumor biopsies.Parisi F, Ariyan S, Narayan D, Bacchiocchi A, Hoyt K, Cheng E, Xu F, Li P, Halaban R, Kluger Y. Detecting copy number status and uncovering subclonal markers in heterogeneous tumor biopsies. BMC Genomics 2011, 12:230.
  • RUNX transcription factor-mediated association of Cd4 and Cd8 enables coordinate gene regulation.Collins A, Hewitt SL, Chaumeil J, Sellars M, Micsinai M, Allinne J, Parisi F, Nora EP, Bolland DJ, Corcoran AE, Kluger Y, Bosselut R, Ellmeier W, Chong MM, Littman DR, Skok JA. RUNX transcription factor-mediated association of Cd4 and Cd8 enables coordinate gene regulation. Immunity 2011, 34:303-14.
  • PLX4032, a selective BRAF(V600E) kinase inhibitor, activates the ERK pathway and enhances cell migration and proliferation of BRAF melanoma cells.Halaban R, Zhang W, Bacchiocchi A, Cheng E, Parisi F, Ariyan S, Krauthammer M, McCusker JP, Kluger Y, Sznol M. PLX4032, a selective BRAF(V600E) kinase inhibitor, activates the ERK pathway and enhances cell migration and proliferation of BRAF melanoma cells. Pigment Cell & Melanoma Research 2010, 23:190-200.
  • Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models.Parisi F, Gonzalez AM, Nadler Y, Camp RL, Rimm DL, Kluger HM, Kluger Y. Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models. Breast Cancer Research : BCR 2010, 12:R66.
  • The mammalian Sin3 proteins are required for muscle development and sarcomere specification.van Oevelen C, Bowman C, Pellegrino J, Asp P, Cheng J, Parisi F, Micsinai M, Kluger Y, Chu A, Blais A, David G, Dynlacht BD. The mammalian Sin3 proteins are required for muscle development and sarcomere specification. Molecular And Cellular Biology 2010, 30:5686-97.
  • Integrative analysis of epigenetic modulation in melanoma cell response to decitabine: clinical implications.Halaban R, Krauthammer M, Pelizzola M, Cheng E, Kovacs D, Sznol M, Ariyan S, Narayan D, Bacchiocchi A, Molinaro A, Kluger Y, Deng M, Tran N, Zhang W, Picardo M, Enghild JJ. Integrative analysis of epigenetic modulation in melanoma cell response to decitabine: clinical implications. PloS One 2009, 4:e4563.
  • Phosphatidylinositol-3-kinase as a therapeutic target in melanoma.Aziz SA, Davies M, Pick E, Zito C, Jilaveanu L, Camp RL, Rimm DL, Kluger Y, Kluger HM. Phosphatidylinositol-3-kinase as a therapeutic target in melanoma. Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research 2009, 15:3029-36.
  • Expression patterns and prognostic value of Bag-1 and Bcl-2 in breast cancer.Nadler Y, Camp RL, Giltnane JM, Moeder C, Rimm DL, Kluger HM, Kluger Y. Expression patterns and prognostic value of Bag-1 and Bcl-2 in breast cancer. Breast Cancer Research : BCR 2008, 10:R35.
  • Expression of Aurora A (but not Aurora B) is predictive of survival in breast cancer.Nadler Y, Camp RL, Schwartz C, Rimm DL, Kluger HM, Kluger Y. Expression of Aurora A (but not Aurora B) is predictive of survival in breast cancer. Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research 2008, 14:4455-62.
  • A role for mammalian Sin3 in permanent gene silencing.van Oevelen C, Wang J, Asp P, Yan Q, Kaelin WG, Kluger Y, Dynlacht BD. A role for mammalian Sin3 in permanent gene silencing. Molecular Cell 2008, 32:359-70.
  • XBP1 controls diverse cell type- and condition-specific transcriptional regulatory networks.Acosta-Alvear D, Zhou Y, Blais A, Tsikitis M, Lents NH, Arias C, Lennon CJ, Kluger Y, Dynlacht BD. XBP1 controls diverse cell type- and condition-specific transcriptional regulatory networks. Molecular Cell 2007, 27:53-66.
  • High HSP90 expression is associated with decreased survival in breast cancer.Pick E, Kluger Y, Giltnane JM, Moeder C, Camp RL, Rimm DL, Kluger HM. High HSP90 expression is associated with decreased survival in breast cancer. Cancer Research 2007, 67:2932-7.
  • Characterizing disease states from topological properties of transcriptional regulatory networks.Tuck DP, Kluger HM, Kluger Y. Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinformatics 2006, 7:236.
  • Association between pathways in regulatory networks.Kluger Y, Kluger H, Tuck D. Association between pathways in regulatory networks. Conference Proceedings : ... Annual International Conference Of The IEEE Engineering In Medicine And Biology Society. IEEE Engineering In Medicine And Biology Society. Annual Conference 2006, 2006:2036-40.
  • Two types of precursor cells in a multipotential hematopoietic cell line.Ye ZJ, Kluger Y, Lian Z, Weissman SM. Two types of precursor cells in a multipotential hematopoietic cell line. Proceedings Of The National Academy Of Sciences Of The United States Of America 2005, 102:18461-6.
  • Using a xenograft model of human breast cancer metastasis to find genes associated with clinically aggressive disease.Kluger HM, Chelouche Lev D, Kluger Y, McCarthy MM, Kiriakova G, Camp RL, Rimm DL, Price JE. Using a xenograft model of human breast cancer metastasis to find genes associated with clinically aggressive disease. Cancer Research 2005, 65:5578-87.
  • Lineage specificity of gene expression patterns.Kluger Y, Tuck DP, Chang JT, Nakayama Y, Poddar R, Kohya N, Lian Z, Ben Nasr A, Halaban HR, Krause DS, Zhang X, Newburger PE, Weissman SM. Lineage specificity of gene expression patterns. Proceedings Of The National Academy Of Sciences Of The United States Of America 2004, 101:6508-13.
  • A panorama of lineage-specific transcription in hematopoiesis.Kluger Y, Lian Z, Zhang X, Newburger PE, Weissman SM. A panorama of lineage-specific transcription in hematopoiesis. BioEssays : News And Reviews In Molecular, Cellular And Developmental Biology 2004, 26:1276-87.
  • cDNA microarray analysis of invasive and tumorigenic phenotypes in a breast cancer model.Kluger HM, Kluger Y, Gilmore-Hebert M, DiVito K, Chang JT, Rodov S, Mironenko O, Kacinski BM, Perkins AS, Sapi E. cDNA microarray analysis of invasive and tumorigenic phenotypes in a breast cancer model. Laboratory Investigation; A Journal Of Technical Methods And Pathology 2004, 84:320-31.
  • Gene expression in mature neutrophils: early responses to inflammatory stimuli.Zhang X, Kluger Y, Nakayama Y, Poddar R, Whitney C, DeTora A, Weissman SM, Newburger PE. Gene expression in mature neutrophils: early responses to inflammatory stimuli. Journal Of Leukocyte Biology 2004, 75:358-72.
  • Gene expression in human neutrophils during activation and priming by bacterial lipopolysaccharide.Tsukahara Y, Lian Z, Zhang X, Whitney C, Kluger Y, Tuck D, Yamaga S, Nakayama Y, Weissman SM, Newburger PE. Gene expression in human neutrophils during activation and priming by bacterial lipopolysaccharide. Journal Of Cellular Biochemistry 2003, 89:848-61.
  • Spectral biclustering of microarray data: coclustering genes and conditions.Kluger Y, Basri R, Chang JT, Gerstein M. Spectral biclustering of microarray data: coclustering genes and conditions. Genome Research 2003, 13:703-16.
  • A Bayesian networks approach for predicting protein-protein interactions from genomic data.Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science (New York, N.Y.) 2003, 302:449-53.
  • Relationship between gene co-expression and probe localization on microarray slides.Kluger Y, Yu H, Qian J, Gerstein M. Relationship between gene co-expression and probe localization on microarray slides. BMC Genomics 2003, 4:49.
  • Identification and correction of spurious spatial correlations in microarray data.Qian J, Kluger Y, Yu H, Gerstein M. Identification and correction of spurious spatial correlations in microarray data. BioTechniques 2003, 35:42-4, 46, 48.
  • Genomic and proteomic analysis of the myeloid differentiation program: global analysis of gene expression during induced differentiation in the MPRO cell line.Lian Z, Kluger Y, Greenbaum DS, Tuck D, Gerstein M, Berliner N, Weissman SM, Newburger PE. Genomic and proteomic analysis of the myeloid differentiation program: global analysis of gene expression during induced differentiation in the MPRO cell line. Blood 2002, 100:3209-20.
  • RNA expression patterns change dramatically in human neutrophils exposed to bacteria.Subrahmanyam YV, Yamaga S, Prashar Y, Lee HH, Hoe NP, Kluger Y, Gerstein M, Goguen JD, Newburger PE, Weissman SM. RNA expression patterns change dramatically in human neutrophils exposed to bacteria. Blood 2001, 97:2457-68.
  • SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics.Bertone P, Kluger Y, Lan N, Zheng D, Christendat D, Yee A, Edwards AM, Arrowsmith CH, Montelione GT, Gerstein M. SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics. Nucleic Acids Research 2001, 29:2884-98.
  • Genomic and proteomic analysis of the myeloid differentiation program.Lian Z, Wang L, Yamaga S, Bonds W, Beazer-Barclay Y, Kluger Y, Gerstein M, Newburger PE, Berliner N, Weissman SM. Genomic and proteomic analysis of the myeloid differentiation program. Blood 2001, 98:513-24.
  • Structural proteomics: prospects for high throughput sample preparation.Christendat D, Yee A, Dharamsi A, Kluger Y, Gerstein M, Arrowsmith CH, Edwards AM. Structural proteomics: prospects for high throughput sample preparation. Progress In Biophysics And Molecular Biology 2000, 73:339-45.
  • A humanized mouse model of chronic COVID-19 to evaluate disease mechanisms and treatment options.Sefik E, Israelow B, Zhao J, Qu R, Song E, Mirza H, Kaffe E, Halene S, Meffre E, Kluger Y, Nussenzweig M, Wilen C, Iwasaki A, Flavell RA. A humanized mouse model of chronic COVID-19 to evaluate disease mechanisms and treatment options. Research Square 2021.
  • Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.Lin YE, Shnitzer T, Talmon R, Villarroel-Espindola F, Desai S, Schalper K, Kluger Y. Graph of graphs analysis for multiplexed data with application to imaging mass cytometry. PLoS Computational Biology 2021, 17:e1008741.
  • HSP90 as a marker of progression in melanoma.McCarthy MM, Pick E, Kluger Y, Gould-Rothberg B, Lazova R, Camp RL, Rimm DL, Kluger HM. HSP90 as a marker of progression in melanoma. Annals Of Oncology : Official Journal Of The European Society For Medical Oncology / ESMO 2008, 19:590-4.
  • Growth factor receptor-bound protein-7 (Grb7) as a prognostic marker and therapeutic target in breast cancer.Nadler Y, González AM, Camp RL, Rimm DL, Kluger HM, Kluger Y. Growth factor receptor-bound protein-7 (Grb7) as a prognostic marker and therapeutic target in breast cancer. Annals Of Oncology : Official Journal Of The European Society For Medical Oncology / ESMO 2010, 21:466-473.
  • A common set of gene regulatory networks links metabolism and growth inhibition.Cam H, Balciunaite E, Blais A, Spektor A, Scarpulla RC, Young R, Kluger Y, Dynlacht BD. A common set of gene regulatory networks links metabolism and growth inhibition. Molecular Cell 2004, 16:399-411.
  • An initial blueprint for myogenic differentiation.Blais A, Tsikitis M, Acosta-Alvear D, Sharan R, Kluger Y, Dynlacht BD. An initial blueprint for myogenic differentiation. Genes & Development 2005, 19:553-69.
  • Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae.Kim H, Hu W, Kluger Y. Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae. BMC Bioinformatics 2006, 7:165.
  • Inter- and intra-combinatorial regulation by transcription factors and microRNAs.Zhou Y, Ferguson J, Chang JT, Kluger Y. Inter- and intra-combinatorial regulation by transcription factors and microRNAs. BMC Genomics 2007, 8:396.
  • Anomalous transverse distribution of pions as a signal for the production of disoriented chiral condensates.Cooper F, Kluger Y, Mottola E. Anomalous transverse distribution of pions as a signal for the production of disoriented chiral condensates. Physical Review. C, Nuclear Physics 1996, 54:3298-3301.
  • Fermion pair production in a strong electric field.Kluger Y, Eisenberg JM, Svetitsky B, Cooper F, Mottola E. Fermion pair production in a strong electric field. Physical Review. D, Particles And Fields 1992, 45:4659-4671.
  • Particle production in the central rapidity region.Cooper F, Eisenberg JM, Kluger Y, Mottola E, Svetitsky B. Particle production in the central rapidity region. Physical Review. D, Particles And Fields 1993, 48:190-208.
  • Dissipation and decoherence in mean field theory.Habib S, Kluger Y, Mottola E, Paz JP. Dissipation and decoherence in mean field theory. Physical Review Letters 1996, 76:4660-4663.
  • Pazopanib ameliorates acute lung injuries via inhibition of MAP3K2 and MAP3K3.Yuan Q, Basit A, Liang W, Qu R, Luan Y, Ren C, Li A, Xu X, Liu X, Yang C, Kuo A, Pierce R, Zhang L, Turk B, Hu X, Li F, Cui W, Li R, Huang D, Mo L, Sessa WC, Lee PJ, Kluger Y, Su B, Tang W, He J, Wu D. Pazopanib ameliorates acute lung injuries via inhibition of MAP3K2 and MAP3K3. Science Translational Medicine 2021, 13.
  • Detection of differentially abundant cell subpopulations in scRNA-seq data.Zhao J, Jaffe A, Li H, Lindenbaum O, Sefik E, Jackson R, Cheng X, Flavell RA, Kluger Y. Detection of differentially abundant cell subpopulations in scRNA-seq data. Proceedings Of The National Academy Of Sciences Of The United States Of America 2021, 118.
  • Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.Irshaid L, Bleiberg J, Weinberger E, Garritano J, Shallis RM, Patsenker J, Lindenbaum O, Kluger Y, Katz SG, Xu ML. Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies. Archives Of Pathology & Laboratory Medicine 2021.
  • Spectral neighbor joining for reconstruction of latent tree Models.Jaffe A, Amsel N, Aizenbud Y, Nadler B, Chang JT, Kluger Y. Spectral neighbor joining for reconstruction of latent tree Models. SIAM Journal On Mathematics Of Data Science 2021, 3:113-141.
  • Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise.Landa B, Coifman RR, Kluger Y. Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise. SIAM Journal On Mathematics Of Data Science 2021, 3:388-413.
  • Randomized algorithms for distributed computation of principal component analysis and singular value decomposition.Li H, Kluger Y, Tygert M. Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. Advances In Computational Mathematics 2018, 44:1651-1672.
  • The Spectral Underpinning of word2vec.Jaffe A, Kluger Y, Lindenbaum O, Patsenker J, Peterfreund E, Steinerberger S. The Spectral Underpinning of word2vec. Frontiers In Applied Mathematics And Statistics 2020, 6.

Clinical Trials

ConditionsStudy Title
Diseases of the Nervous System; HIV/AIDS; Infectious Diseases; COVID-19 Inpatient; COVID-19 OutpatientHIV Associated Reservoirs and Comorbidities (The HARC Plus Study)
HIV/AIDSEvaluating the role of opioid medication assisted therapies in HIV-1 Persistence
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