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Mark Gerstein

Researcher at Yale University

Publications -  802
Citations -  172183

Mark Gerstein is an academic researcher from Yale University. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 168, co-authored 751 publications receiving 149578 citations. Previous affiliations of Mark Gerstein include Rutgers University & Structural Genomics Consortium.

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Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood.

TL;DR: The authors develop the algorithm, sigLASSO, that provides confidence in assigning mutational signatures when the mutation count is low and the samples used are variable, and fine-tunes model complexity, informed by data scale and biological priors.
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Segmental duplications in the human genome reveal details of pseudogene formation

TL;DR: A comparison of nucleotide substitutions per site in a pseudogene with its surrounding SD region allows us to estimate the time difference between duplication and disablement events, and this suggests that most duplicated pseudogenes in SDs were likely disabled around the time of the original duplication.
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Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique

TL;DR: This study implements the first application of positive-unlabeled (PU) learning to a protein function prediction problem: identification of peripheral domains and suggests that the protocol can be used for predicting membrane-binding properties of a wide variety of modular domains.
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Manually structured digital abstracts: a scaffold for automatic text mining.

TL;DR: The structured abstract will provide valuable context to mining algorithms by presenting clearly the main points of each article (as defined by authors and editors), so additional facts gleaned can be correctly categorized as either supporting or detracting from the main Points.
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Transcription factor binding site identification in yeast: a comparison of high-density oligonucleotide and PCR-based microarray platforms.

TL;DR: The HDO array platform provides a far more robust array system by all measures than PCR-based arrays, all of which is directly attributable to the large number of probes available.