Author

Kevin Tian

MIT Computer Science and Artificial Intelligence LaboratoryORCID 0000-0003-0109-9201Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 2 Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Organism:human· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Andrew Lamb· 1 shared David Kenneth GiffordiD· 1 shared Haoyang ZengiD· 1 shared Irving Hsu· 1 shared Joe Paggi· 1 shared Pierre-Louis Cedoz· 1 shared Prasad Kawthekar· 1 shared Xiaoyun GuoiD· 1 shared

Activity by Year

2017-2018 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 16 mutual co-author links

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1 shared paperGenomics

Andrew Lamb

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2 papers

Recent·Most cited

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction

Yuchun Guo, Kevin Tian, Haoyang Zeng, Xiaoyun Guo, David Kenneth Gifford

This paper introduces a novel KSM motif representation and KMAC method for de novo motif discovery, showing improved accuracy in predicting TF binding sites and regulatory variant effects.

Genome Research · Jun 1, 2018Peer-reviewedGenomics

Predicting Transcriptional Regulatory Activities with Deep Convolutional Networks

Joe Paggi, Andrew Lamb, Kevin Tian, Irving Hsu, Pierre-Louis Cedoz, Prasad Kawthekar

The paper trains a multitask convolutional neural network on MPRA data to predict transcriptional regulatory activity across cell types and promoters, finding that importance scores correlate with conservation and transcription factor binding.

bioRxiv · Jan 12, 2017 · PreprintGenomics