Author

Eugene Lane

Hong Kong Science and Technology Parks CorporationORCID 0000-0002-3012-9708Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:bulk RNA-seq· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:metagenomics· 1

Frequent Co-Authors

Alex A. ZhavoronkoviD· 3 shared Alexander M. AliperiD· 3 shared Polina MamoshinaiD· 2 shared Artem V. ArtemoviD· 1 shared Fedor GalkiniD· 1 shared Ivan V. OzeroviD· 1 shared Kirill Kochetov· 1 shared Quentin VanhaeleniD· 1 shared

Activity by Year

2016-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Alex A. ZhavoronkovORCID

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3 papers

Recent·Most cited

Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects

Fedor Galkin, Polina Mamoshina, Alex Aliper, Eugene Lane, Владимир Борисович Москалев, Vadim N. Gladyshev, Alex A. Zhavoronkov

The paper develops a deep learning model to predict human biological age from gut microbiome data and identifies key microbial biomarkers associated with aging.

iScience · May 23, 2020Peer-reviewedBioinformatics

Testing for batch effect through age predictors

Polina Mamoshina, Kirill Kochetov, Eugene Lane, Alex Aliper, Alex A. Zhavoronkov

The paper develops a deep-learned age predictor from transcriptomic data and shows that batch effects can obscure age-related changes.

bioRxiv · Jan 27, 2019 · PreprintBioinformatics

Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Artem V. Artemov, Eugene Lane, Quentin Vanhaelen, Alexander M. Aliper, Ivan V. Ozerov, Alex A. Zhavoronkov

The paper develops a deep learning pipeline to predict clinical trial outcomes using transcriptomic data, achieving 83% cross-validated accuracy and outperforming gene-based classifiers.

bioRxiv · Dec 29, 2016 · PreprintBioinformatics