Hussein Mohsen
About
Biography
Hussein is a Ph.D./M.A. Candidate in Computational Biology and Bioinformatics/History of
Science and Medicine at Yale. His research is at the confluence of machine learning and cancer genomics. Upon the recent eruption of data generation across scientific fields, Hussein developed interests in the social construction of scientific knowledge underlying the study of disease and the organic interface between science and society. Through his research, he aims to integrate qualitative approaches with quantitative methods that pertain to genomics.
Hussein received a B.S. in Computer Science from the Lebanese American University and an M.S. in Bioinformatics from Indiana University.
Research
Publications
2022
Cancer Relevance of Human Genes
Qing T, Mohsen H, Cannataro VL, Marczyk M, Rozenblit M, Foldi J, Murray M, Townsend J, Kluger Y, Gerstein M, Pusztai L. Cancer Relevance of Human Genes. Journal Of The National Cancer Institute 2022, 114: 988-995. PMID: 35417011, PMCID: PMC9275765, DOI: 10.1093/jnci/djac068.Peer-Reviewed Original ResearchConceptsCore cancer genesHuman genesFunctional importanceSomatic mutation frequencySelection pressureGene/protein networksCancer genesHigher somatic mutation frequencyNegative selection pressureGene-gene interaction networksMutation frequencyProtein-truncating variantsGenomic contextCell viabilityGenes decreasesCancer Genome AtlasInteraction networksProtein networkCancer relevanceCancer cell viabilityCell survivalGenesCancer biologyGenome AtlasSearch tools
2021
Network propagation-based prioritization of long tail genes in 17 cancer types
Mohsen H, Gunasekharan V, Qing T, Seay M, Surovtseva Y, Negahban S, Szallasi Z, Pusztai L, Gerstein MB. Network propagation-based prioritization of long tail genes in 17 cancer types. Genome Biology 2021, 22: 287. PMID: 34620211, PMCID: PMC8496153, DOI: 10.1186/s13059-021-02504-x.Peer-Reviewed Original ResearchConceptsCancer-relevant genesTail genesMobility genesNetwork propagation approachGenome-wide RNAiNetwork propagation methodCancer developmentPotential functional impactCancer cell survivalNew genesUnreported genesFunctional screeningCancer typesFunctional importanceCancer genesNovel potential therapeutic targetDriver genesCell survivalGenesMutational distributionsBiological interactionsPotential therapeutic targetFunctional impactGenomic alterationsInfrequent mutations
2020
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. PMID: 32415133, PMCID: PMC7228928, DOI: 10.1038/s41467-020-16293-7.Peer-Reviewed Original ResearchConceptsAge groupsGermline variantsSomatic mutationsLate-onset cancerEarly-onset cancersCancer hallmark genesSomatic mutation burdenMutation burdenMalignant transformationCancer genesYounger ageGermline alterationsCancerVariant burdenBurdenAverage numberHallmark genesAgeNegative correlationStrong negative correlationMutationsPatientsGroupRace and Genetics: Somber History, Troubled Present.
Mohsen H. Race and Genetics: Somber History, Troubled Present. The Yale Journal Of Biology And Medicine 2020, 93: 215-219. PMID: 32226350, PMCID: PMC7087058.Peer-Reviewed Original Research
2019
Exploring single-cell data with deep multitasking neural networks
Amodio M, van Dijk D, Srinivasan K, Chen WS, Mohsen H, Moon KR, Campbell A, Zhao Y, Wang X, Venkataswamy M, Desai A, Ravi V, Kumar P, Montgomery R, Wolf G, Krishnaswamy S. Exploring single-cell data with deep multitasking neural networks. Nature Methods 2019, 16: 1139-1145. PMID: 31591579, PMCID: PMC10164410, DOI: 10.1038/s41592-019-0576-7.Peer-Reviewed Original ResearchGRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner
Lou S, Cotter KA, Li T, Liang J, Mohsen H, Liu J, Zhang J, Cohen S, Xu J, Yu H, Rubin MA, Gerstein M. GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner. PLOS Genetics 2019, 15: e1007860. PMID: 31469829, PMCID: PMC6742416, DOI: 10.1371/journal.pgen.1007860.Peer-Reviewed Original ResearchConceptsNon-coding variantsTranscription factorsLarge linkage disequilibrium blocksCell type-specific mannerPhenotype of interestMolecular effectsFine-mapping causal variantsCell-specific mannerParticular cell typeLinkage disequilibrium blockEvolutionary conservationTF bindingChIP-seqRegulatory networksPerformance of GRAMCausal variantsReporter geneGene expressionExpression profilesGenomic variantsAssociated geneLuciferase assayCell typesK562 cell lineSpecific mannerGenomics and data science: an application within an umbrella
Navarro FCP, Mohsen H, Yan C, Li S, Gu M, Meyerson W, Gerstein M. Genomics and data science: an application within an umbrella. Genome Biology 2019, 20: 109. PMID: 31142351, PMCID: PMC6540394, DOI: 10.1186/s13059-019-1724-1.Peer-Reviewed Original Research
2017
Improving de novo metatranscriptome assembly via machine learning algorithms
Mohsen H, Ye Y, Tang H. Improving de novo metatranscriptome assembly via machine learning algorithms. International Journal Of Computational Biology And Drug Design 2017, 10: 91. DOI: 10.1504/ijcbdd.2017.10004575.Peer-Reviewed Original ResearchImproving de novo metatranscriptome assembly via machine learning algorithms
Mohsen H, Tang H, Ye Y. Improving de novo metatranscriptome assembly via machine learning algorithms. International Journal Of Computational Biology And Drug Design 2017, 10: 91. DOI: 10.1504/ijcbdd.2017.083877.Peer-Reviewed Original Research
2015
Red-RF: Reduced Random Forest for Big Data Using Priority Voting & Dynamic Data Reduction
Mohsen H, Kurban H, Zimmer K, Jenne M, Dalkilic M. Red-RF: Reduced Random Forest for Big Data Using Priority Voting & Dynamic Data Reduction. 2015, 118-125. DOI: 10.1109/bigdatacongress.2015.26.Peer-Reviewed Original Research