Author

Lisa Gai

University of California, Los AngelesORCID 0009-0001-0803-5246Unclaimed 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:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:eQTL / molecular QTL mapping· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Dat DuongiD· 3 shared Eleazar EskiniD· 3 shared Ankith Uppunda· 2 shared Jingyi Jessica LiiD· 2 shared Kai-Wei ChangiD· 2 shared Buhm HaniD· 1 shared Chelsea J.‐T. JuiD· 1 shared D. Q. Le· 1 shared

Activity by Year

2017-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Dat DuongORCID

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3 papers

Recent·Most cited

Annotating Gene Ontology terms for protein sequences with the Transformer model

Dat Duong, Lisa Gai, Ankith Uppunda, D. Q. Le, Eleazar Eskin, Jingyi Jessica Li, Kai-Wei Chang

The paper introduces a Transformer-based model for annotating Gene Ontology terms in protein sequences, showing improved accuracy over CNN-based methods and incorporating additional features like BLAST motifs and structural data.

bioRxiv · Jan 31, 2020 · PreprintBioinformatics

Evaluating Representations for Gene Ontology Terms

Dat Duong, Ankith Uppunda, Lisa Gai, Chelsea J.‐T. Ju, James Zhang, Muhao Chen, Eleazar Eskin, Jingyi Jessica Li, Kai-Wei Chang

This paper evaluates neural network models for embedding Gene Ontology terms and introduces new models based on GCN, ELMo, and BERT, finding that BERT-based embeddings perform best for rare labels.

bioRxiv · Jan 31, 2020 · PreprintBioinformatics

Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLsand increase the number of eGenes

Dat Duong, Lisa Gai, Sagi Snir, Eun Yong Kang, Buhm Han, Jae Hoon Sul, Eleazar Eskin

The paper introduces a meta-analysis method to identify eQTLs from multi-tissue datasets, detecting more eQTLs and eGenes than previous approaches.

Bioinformatics · Apr 20, 2017Peer-reviewedBioinformatics