Gherman Novakovsky
Overview
Explore the profile of Gherman Novakovsky including associated specialties, affiliations and a list of published articles.
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7
Citations
297
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Recent Articles
1.
Novakovsky G, Fornes O, Saraswat M, Mostafavi S, Wasserman W
Genome Biol
. 2023 Jun;
24(1):154.
PMID: 37370113
Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear...
2.
Novakovsky G, Sasaki S, Fornes O, Omur M, Huang H, Bayly C, et al.
Stem Cell Reports
. 2023 Feb;
18(3):765-781.
PMID: 36801003
Improving methods for human embryonic stem cell differentiation represents a challenge in modern regenerative medicine research. Using drug repurposing approaches, we discover small molecules that regulate the formation of definitive...
3.
Fornes O, Jia A, Kuehn H, Min Q, Pannicke U, Schleussner N, et al.
Sci Immunol
. 2023 Jan;
8(79):eade7953.
PMID: 36662884
Interferon regulatory factor 4 (IRF4) is a transcription factor (TF) and key regulator of immune cell development and function. We report a recurrent heterozygous mutation in IRF4, p.T95R, causing an...
4.
Novakovsky G, Dexter N, Libbrecht M, Wasserman W, Mostafavi S
Nat Rev Genet
. 2022 Oct;
24(2):125-137.
PMID: 36192604
Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models...
5.
Edgar R, Taylor B, Lin V, Altman T, Barbera P, Meleshko D, et al.
Nature
. 2022 Jan;
602(7895):142-147.
PMID: 35082445
Public databases contain a planetary collection of nucleic acid sequences, but their systematic exploration has been inhibited by a lack of efficient methods for searching this corpus, which (at the...
6.
Novakovsky G, Saraswat M, Fornes O, Mostafavi S, Wasserman W
Genome Biol
. 2021 Sep;
22(1):280.
PMID: 34579793
Background: Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required...
7.
Ng B, Casazza W, Patrick E, Tasaki S, Novakovsky G, Felsky D, et al.
Am J Hum Genet
. 2019 Aug;
105(3):562-572.
PMID: 31447098
Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a...