[find our papers on PubMed]
* co-first author; ¶ co-corresponding author

2020 (2)

Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning
Liang S, Yu H. Bioinformatics 2020 May 12 https://doi.org/10.1093/bioinformatics/btaa495.
First published on bioRxiv 2019 (DOI:10.1101/572420)
SAAMBE-3D: Predicting Effect of Mutations on Protein–Protein Interactions
Pahari S, Li G, Murthy A K, Liang S, Fragoza R, Yu H, and Alexov E. Int J Mol Sci 2020 21: 2563.

2019 (2)

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations
Fragoza R, Das J, Wierbowski S, Liang J, Tran T, Liang S, Beltran J, Rivera-Erick C, Ye K, Wang T, Yao L, Mort M, Stenson P, Cooper D, Wei X, Keinan A, Schimenti J, Clark A and Yu H. Nature Communications 2019 10(1):4141.
Leveraging genetic interaction for adverse drug-drug interaction prediction
Qian S, Liang S, Yu H. PLoS Comput Biol 2019 15(5):e1007068.

2018 (2)

Extracting complementary insights from molecular phenotypes for prioritization of disease-associated mutations
Wierbowski SD, Fragoza R, Liang S, Yu H. Current Opinion in Systems Biology 2018 11:107–116.
Interactome INSIDER: a structural interactome browser for genomic studies
Meyer M, Beltrán J, Liang S, Fragoza R, Rumack A, Liang J, Wei X, and Yu H. Nature Methods 2018 February 15(2):107-114.

2017 (1)

iRegNet3D: three-dimensional integrated regulatory network for the genomic analysis of coding and non-coding disease mutations
Liang S, Tippens ND, Zhou Y, Mort M, Stenson PD, Cooper DN, Yu H. Genome Biol 2017 Jan 18 18:10.