# Yuval Kluger, PhD

## Research & Publications

## Biography

## News

## Locations

### 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

- Pair production in a strong electric field.Kluger Y, Eisenberg JM, Svetitsky B, Cooper F, Mottola E. Pair production in a strong electric field. Phys Rev Lett 1991, 67: 2427-2430. PMID: 10044423, DOI: 10.1103/PhysRevLett.67.2427.
- The age of bone marrow dictates the clonality of smooth muscle-derived cells in atherosclerotic plaquesKabir I, Zhang X, Dave J, Chakraborty R, Qu R, Chandran R, Ntokou A, Gallardo-Vara E, Aryal B, Rotllan N, Garcia-Milian R, Hwa J,
**Kluger Y**, Martin K, Fernández-Hernando C, Greif D. The age of bone marrow dictates the clonality of smooth muscle-derived cells in atherosclerotic plaques Nature Aging 2023, 1-18. DOI: 10.1038/s43587-022-00342-5. - Biwhitening Reveals the Rank of a Count MatrixLanda B, Zhang T,
**Kluger Y**. Biwhitening Reveals the Rank of a Count Matrix SIAM Journal On Mathematics Of Data Science 2022, 4: 1420-1446. DOI: 10.1137/21m1456807. - Longitudinal single-cell analysis of a patient receiving adoptive cell therapy reveals potential mechanisms of treatment failureQu R,
**Kluger Y**, Yang J, Zhao J, Hafler D, Krause D, Bersenev A, Bosenberg M, Hurwitz M, Lucca L, Kluger H. Longitudinal single-cell analysis of a patient receiving adoptive cell therapy reveals potential mechanisms of treatment failure Molecular Cancer 2022, 21: 219. PMID: 36514045, PMCID: PMC9749221, DOI: 10.1186/s12943-022-01688-5. - Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES.Raredon M, Yang J, Kothapalli N, Lewis W, Kaminski N, Niklason L,
**Kluger Y**. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 2022 PMID: 36458905, DOI: 10.1093/bioinformatics/btac775. - Mitochondrial Stress Induces an HRI-eIF2α Pathway Protective for CardiomyopathyZhu S, Nguyen A, Pang J, Zhao J, Chen Z, Liang Z, Gu Y, Huynh H, Bao Y, Lee S,
**Kluger Y**, Ouyang K, Evans SM, Fang X. Mitochondrial Stress Induces an HRI-eIF2α Pathway Protective for Cardiomyopathy Circulation 2022, 146: 1028-1031. PMID: 36154620, PMCID: PMC9523491, DOI: 10.1161/circulationaha.122.059594. - On the efficient evaluation of the azimuthal Fourier components of the Green's function for Helmholtz's equation in cylindrical coordinatesGarritano J,
**Kluger Y**, Rokhlin V, Serkh K. On the efficient evaluation of the azimuthal Fourier components of the Green's function for Helmholtz's equation in cylindrical coordinates Journal Of Computational Physics 2022, 471: 111585. PMID: 36171963, PMCID: PMC9512147, DOI: 10.1016/j.jcp.2022.111585. - Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populationsGirardi M, Ren J, Qu R, Rahman N, Lewis J, King A, Liao X, Mirza F, Carlson K, Huang Y, Gigante S, Evans B, Rajendran B, Xu S, Wang G, Foss F, Damsky W,
**Kluger Y**, Krishnaswamy S. Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations Blood Advances 2022 PMID: 35947128, DOI: 10.1182/bloodadvances.2022008168. - Deep unsupervised feature selection by discarding nuisance and correlated featuresShaham U, Lindenbaum O, Svirsky J,
**Kluger Y**. Deep unsupervised feature selection by discarding nuisance and correlated features Neural Networks 2022, 152: 34-43. PMID: 35500458, PMCID: PMC9526895, DOI: 10.1016/j.neunet.2022.04.002. - Zero-preserving imputation of single-cell RNA-seq dataLinderman GC, Zhao J, Roulis M, Bielecki P, Flavell RA, Nadler B,
**Kluger Y**. Zero-preserving imputation of single-cell RNA-seq data Nature Communications 2022, 13: 192. PMID: 35017482, PMCID: PMC8752663, DOI: 10.1038/s41467-021-27729-z. - Detection of differentially abundant cell subpopulations in scRNA-seq dataZhao 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: e2100293118. PMID: 34001664, PMCID: PMC8179149, DOI: 10.1073/pnas.2100293118. - Graph of graphs analysis for multiplexed data with application to imaging mass cytometryLin 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. PMID: 33780435, PMCID: PMC8032202, DOI: 10.1371/journal.pcbi.1008741. - Abstract S03-03: Cancer patients display diminished viral RNA clearance and altered T cell responses during SARS-CoV-2 infectionChiorazzi M, Silva E, Brower K, Wong P, Lucas C, Klein J, Liu F, Nakahata M, Zhao J, Rahman N, Odio C, Bermejo S, Farhadian S, Dela Cruz C, Casanovas-Massana A, Fournier J, Muenker C, Wyllie A, Vogels C, Kalinich C, Petrone M, Ott I, Watkins A, Moore A, Alpert T,
**Kluger Y**, Ring A, Grubaugh N, Iwasaki A, Ko A, Herbst R. Abstract S03-03: Cancer patients display diminished viral RNA clearance and altered T cell responses during SARS-CoV-2 infection Clinical Cancer Research 2021, 27: s03-03-s03-03. DOI: 10.1158/1557-3265.covid-19-21-s03-03. - 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. PMID: 34124606, PMCID: PMC8194222, DOI: 10.1137/20m1365715. - 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. PMID: 34124607, PMCID: PMC8194191, DOI: 10.1137/20m1342124. - The Spectral Underpinning of word2vecJaffe A,
**Kluger Y**, Lindenbaum O, Patsenker J, Peterfreund E, Steinerberger S. The Spectral Underpinning of word2vec Frontiers In Applied Mathematics And Statistics 2020, 6: 593406. PMID: 34504892, PMCID: PMC8425479, DOI: 10.3389/fams.2020.593406. - Abstract 6333: Genomic, transcriptomic, and epigenetic profiling of triple-negative breast cancer cells after Navitoclax treatmentMarczyk M, Gunasekharan V, Zhao J, Qu R, Li X, Patwardhan G, Wali V, Gupta A, Pillai M,
**Kluger Y**, Hatzis C, Pusztai L. Abstract 6333: Genomic, transcriptomic, and epigenetic profiling of triple-negative breast cancer cells after Navitoclax treatment Cancer Research 2020, 80: 6333-6333. DOI: 10.1158/1538-7445.am2020-6333. - 19. PLEKHA5 REGULATES TUMOR GROWTH IN METASTATIC MELANOMAOria V, Oria V, Zhang H, Zhang H, Zhu H, Zhu H, Deng G, Deng G, Zito C, Zito C, Rane C, Rane C, Zhang S, Zhang S, Weiss S, Tran T, Adeniran A, Zhang F, Zhang F, Zhou J,
**Kluger Y**, Bosenberg M, Kluger H, Jilaveanu L. 19. PLEKHA5 REGULATES TUMOR GROWTH IN METASTATIC MELANOMA Neuro-Oncology Advances 2020, 2: ii3-ii3. PMCID: PMC7401364, DOI: 10.1093/noajnl/vdaa073.009. - Randomized near-neighbor graphs, giant components and applications in data scienceJaffe A,
**Kluger Y**, Linderman G, Mishne G, Steinerberger S. Randomized near-neighbor graphs, giant components and applications in data science Journal Of Applied Probability 2020, 57: 458-476. PMID: 32913373, PMCID: PMC7480951, DOI: 10.1017/jpr.2020.21. - Correction to: Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE VisualisationsKobak D, Linderman G, Steinerberger S,
**Kluger Y**, Berens P. Correction to: Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations 2020, 11906: c1-c1. DOI: 10.1007/978-3-030-46150-8_44. - Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq dataLinderman 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. PMID: 30742040, PMCID: PMC6402590, DOI: 10.1038/s41592-018-0308-4. - Mahalanobis distance informed by clusteringLahav A, Talmon R,
**Kluger Y**. Mahalanobis distance informed by clustering Information And Inference A Journal Of The IMA 2018, 8: 377-406. DOI: 10.1093/imaiai/iay011. - DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural networkKatzman 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. PMID: 29482517, PMCID: PMC5828433, DOI: 10.1186/s12874-018-0482-1. - Abstract P2-09-18: Multiplexed (18-Plex) measurement of protein targets in trastuzumab-treated patients using imaging mass cytometryCarvajal-Hausdorf D, Stanton K, Patsenker J, Villarroel-Espindola F, Esch A, Montgomery R, Psyrri A, Kalogeras K, Kotoula V, Fountzilas G, Schalper K,
**Kluger Y**, Rimm D. Abstract P2-09-18: Multiplexed (18-Plex) measurement of protein targets in trastuzumab-treated patients using imaging mass cytometry Cancer Research 2018, 78: p2-09-18-p2-09-18. DOI: 10.1158/1538-7445.sabcs17-p2-09-18. - Ritornello: high fidelity control-free chromatin immunoprecipitation peak callingStanton KP, Jin J, Lederman RR, Weissman SM,
**Kluger Y**. Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling Nucleic Acids Research 2017, 45: gkx799-. PMID: 28981893, PMCID: PMC5716106, DOI: 10.1093/nar/gkx799. - 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 2017, 33: 3423-3430. PMID: 29036374, PMCID: PMC5860171, DOI: 10.1093/bioinformatics/btx448. - 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 2017, 33: 2539-2546. PMID: 28419223, PMCID: PMC5870543, DOI: 10.1093/bioinformatics/btx196. - Algorithm 971Li H, Linderman GC, Szlam A, Stanton KP,
**Kluger Y**, Tygert M. Algorithm 971 ACM Transactions On Mathematical Software 2017, 43: 1-14. PMID: 28983138, PMCID: PMC5625842, DOI: 10.1145/3004053. - Identification of multi-loci hubs from 4C-seq demonstrates the functional importance of simultaneous interactionsJiang 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. PMID: 27439714, PMCID: PMC5062970, DOI: 10.1093/nar/gkw568. - Efficient Engraftment and Disease Replication of Myelodysplastic Syndromes Using a Novel Humanized Mice ModelSong Y, Taylor A, Rongvaux A, Jiang T, Podoltsev N, Xu M, Neparidze N, Torres R, Barbarotta L, Balasubramanian K, Finberg K,
**Kluger Y**, Flavell R, Halene S. Efficient Engraftment and Disease Replication of Myelodysplastic Syndromes Using a Novel Humanized Mice Model Blood 2015, 126: 4100-4100. DOI: 10.1182/blood.v126.23.4100.4100. - Abstract 2440: RNF5 mediates ER stress-induced degradation of SLC1A5 in breast cancerJeon Y, Khelifa S, Feng Y, Lau E, Cardiff R, Kim H, Rimm D,
**Kluger Y**, Ronai Z. Abstract 2440: RNF5 mediates ER stress-induced degradation of SLC1A5 in breast cancer 2014, 2440-2440. DOI: 10.1158/1538-7445.am2014-2440. - Association between pathways in regulatory networks
**Kluger Y**, Kluger H, Tuck D. Association between pathways in regulatory networks Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2014, 2036-2040. DOI: 10.1109/iembs.2006.260730. - Ranking and combining multiple predictors without labeled dataParisi 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: 1253-1258. PMID: 24474744, PMCID: PMC3910607, DOI: 10.1073/pnas.1219097111. - TrAp: a tree approach for fingerprinting subclonal tumor compositionStrino F, Parisi F, Micsinai M,
**Kluger Y**. TrAp: a tree approach for fingerprinting subclonal tumor composition Nucleic Acids Research 2013, 41: e165-e165. PMID: 23892400, PMCID: PMC3783191, DOI: 10.1093/nar/gkt641. - Genomic alterations in matching primary tumors and metastasis in breast cancer.Jiang T, Szekely B, Szasz A,
**Kluger Y**, Kulka J, Pusztai L. Genomic alterations in matching primary tumors and metastasis in breast cancer. Journal Of Clinical Oncology 2013, 31: 1549-1549. DOI: 10.1200/jco.2013.31.15_suppl.1549. - Abstract 1569: Next generation cell line models: conditionally reprogrammed cells.Agarwal S, Hu J, Stanton K, Schalper K,
**Kluger Y**, Zarrella E, Liu X, Schlegel R, Rimm D. Abstract 1569: Next generation cell line models: conditionally reprogrammed cells. Cancer Research 2013, 73: 1569-1569. DOI: 10.1158/1538-7445.am2013-1569. - Personal Position Repertoire (PPR) from a Bird's Eye ViewKluger A, Nir D,
**Kluger Y**. Personal Position Repertoire (PPR) from a Bird's Eye View Journal Of Constructivist Psychology 2008, 21: 223-238. DOI: 10.1080/10720530802071518. - Characterizing disease states from topological properties of transcriptional regulatory networksTuck DP, Kluger HM,
**Kluger Y**. Characterizing disease states from topological properties of transcriptional regulatory networks BMC Bioinformatics 2006, 7: 236. PMID: 16670008, PMCID: PMC1482723, DOI: 10.1186/1471-2105-7-236. - OR.65. Melanoma Biomarker Discovery Through Serum Antibody Profiling On Protein MicroarraysMattoon D, Love B,
**Kluger Y**, Michaud G, Schweitzer B, Predki P, Ritter G, Halaban R. OR.65. Melanoma Biomarker Discovery Through Serum Antibody Profiling On Protein Microarrays Clinical Immunology 2006, 119: s28. DOI: 10.1016/j.clim.2006.04.221. - Lineage specificity of gene expression patterns
**Kluger Y**, Tuck DP, Chang JT, Nakayama Y, Poddar R, Kohya N, Lian Z, 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: 6508-6513. PMID: 15096607, PMCID: PMC404075, DOI: 10.1073/pnas.0401136101. - 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: 703-716. PMID: 12671006, PMCID: PMC430175, DOI: 10.1101/gr.648603. - Genomic and proteomic analysis of the myeloid differentiation programLian Z, Wang L, Yamaga S, Bonds W, Beazer-Barclay Y,
**Kluger Y**, Gerstein M, Newburger P, Berliner N, Weissman S. Genomic and proteomic analysis of the myeloid differentiation program Blood 2001, 98: 513-524. PMID: 11468144, DOI: 10.1182/blood.v98.3.513. - RNA expression patterns change dramatically in human neutrophils exposed to bacteriaSubrahmanyam Y, Yamaga S, Prashar Y, Lee H, Hoe N,
**Kluger Y**, Gerstein M, Goguen J, Newburger P, Weissman S. RNA expression patterns change dramatically in human neutrophils exposed to bacteria Blood 2001, 97: 2457-2468. PMID: 11290611, DOI: 10.1182/blood.v97.8.2457. - Structural proteomics of an archaeonChristendat D, Yee A, Dharamsi A,
**Kluger Y**, Savchenko A, Cort J, Booth V, Mackereth C, Saridakis V, Ekiel I, Kozlov G, Maxwell K, Wu N, McIntosh L, Gehring K, Kennedy M, Davidson A, Pai E, Gerstein M, Edwards A, Arrowsmith C. Structural proteomics of an archaeon Nature Structural & Molecular Biology 2000, 7: 903-909. PMID: 11017201, DOI: 10.1038/82823. - Structural proteomics: prospects for high throughput sample preparationChristendat D, Yee A, Dharamsi A,
**Kluger Y**, Gerstein M, Arrowsmith C, Edwards A. Structural proteomics: prospects for high throughput sample preparation Progress In Biophysics And Molecular Biology 2000, 73: 339-345. PMID: 11063779, DOI: 10.1016/s0079-6107(00)00010-9. - Quantum Vlasov equation and its Markov limit.Kluger, Y., Mottola, E., & Eisenberg, J. M. (1998). Quantum Vlasov equation and its Markov limit. Physical Review D, 58(12), 125015
- Dynamical chaos in SU (2)⊗ U (1) theoryBerman, G., Bulgakov, E., Holm, D. and Kluger, Y., 1994. Dynamical chaos in SU (2)⊗ U (1) theory. Physics Letters A, 194(4), pp.251-264.

### Clinical Trials

Conditions | Study Title |
---|---|

Diseases of the Nervous System; HIV/AIDS; Infectious Diseases; COVID-19 Inpatient; COVID-19 Outpatient | HIV Associated Reservoirs and Comorbidities (The HARC Plus Study) |