LINKS
Research Lab
EnzymeX
GPU Cluster
DataX Github
DLB2H Workshop
FEATURED PUBLICATIONS
#Enzyme Function Prediction
Kim & Avinash et al., "Prediction of bacterial Cytochrome P450-compound interactions based on positive-unlabeled deep learning",
Bioinformatics, 2026
 
#Enzyme Function Prediction
Dumontet et al., "Trustworthy prediction of enzyme commission numbers using a hierarchical interpretable transformer", Nature Communications, 2026
#LASSO
Baek et al., "Stochastic LASSO for extremely high-dimensional genomic data", Scientific Reports, 2026
#Digital Pathology
Kosaraju et al., "ALK-expression screening using H&E-stained histopathological images via evidential deep learning", npj Digital Medicine, 2025
#Fairness ML #Sexual Dimorphism
Ko et al., "SPIN: Sex-specific and Pathway-based Interpretable Neural Network for Sexual Dimorphism Analysis",
Briefings in Bioinformatics, 2024
#Enzyme Function Prediction
Han et al., "Evidential deep learning for trustworthy prediction of enzyme commission number",
Briefings in Bioinformatics, 2023
#Digital Pathology
Kosaraju et al., "Deep learning-based framework for slide-based histopathological image analysis",
Scientific Reports, 2022
#Multi-omics Data Analysis
Kang et al., "A Roadmap for Multi-Omics Data Integration using Deep Learning",
Briefings in Bioinformatics, 2021
#Survival Analysis #Pathway-informed NN
Oh et al., "PathCNN: Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma",
International conference on Intelligent Systems for Molecular Biology (ISMB), Published in Bioinformatics, 2021
#Digital Pathology
Kosaraju et al., "Deep-Hipo: Multi-scale Receptive Field Deep Learning for Histopathological Image Analysis",
Methods, 2020
#Digital Pathology #Survival Analysis #Pathway-Informed NN
Hao et al., "PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data",
Pacific Symposium on Biocomputing (PSB), 2020
#Survival Analysis #Pathway-Informed NN
Hao et al., "Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data",
BMC Medical Genomics, 2019
#Genomics #Epistasis
Kang et al., "eQTL epistasis: detecting epistatic effects and inferring hierarchical relationships of genes in biological pathways",
Bioinformatics, 2015
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