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Yuval Kluger

Researcher at Yale University

Publications -  229
Citations -  16806

Yuval Kluger is an academic researcher from Yale University. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 58, co-authored 195 publications receiving 13146 citations. Previous affiliations of Yuval Kluger include Boston Children's Hospital & Afeka College of Engineering.

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A Bayesian networks approach for predicting protein-protein interactions from genomic data.

TL;DR: This work develops an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast, and observes that at given levels of sensitivity, the predictions are more accurate than the existing high-throughput experimental data sets.
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DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

TL;DR: The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient’s characteristics on their risk of failure.
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PCGF homologs, CBX proteins, and RYBP define functionally distinct PRC1 family complexes.

TL;DR: A comprehensive proteomic and genomic analysis uncovered six major groups of PRC1 complexes, each containing a distinct PCGF subunit, a RING1A/B ubiquitin ligase, and a unique set of associated polypeptides.
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XBP1 Controls Diverse Cell Type- and Condition-Specific Transcriptional Regulatory Networks

TL;DR: A core group of genes involved in constitutive maintenance of ER function in all cell types and tissue- and condition-specific targets are identified and a cadre of unexpected targets that link XBP1 to neurodegenerative and myodegenerative diseases, as well as to DNA damage and repair pathways are identified.
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Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions

TL;DR: This work develops a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist, and applies it to a selection of publicly available cancer expression data sets.