Stochastic modeling of dwell-time distributions during transcriptional pausing and initiation. - Xiaohua Xu; Niraj Kumar 006; Arjun Krishnan; Rahul V. Kulkarni (CDC, 2013)

Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies. - Young-Suk Lee; Arjun Krishnan; Qian Zhu 005; Olga G. Troyanskaya (Bioinform., 2013)

Reconciling differential gene expression data with molecular interaction networks. - Christopher L. Poirel; Ahsanur Rahman 001; Richard R. Rodrigues; Arjun Krishnan; Jacqueline R. Addesa; T. M. Murali 001 (Bioinform., 2013)

Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms. - Christopher Y. Park; Arjun Krishnan; Qian Zhu 005; Aaron K. Wong; Young-Suk Lee; Olga G. Troyanskaya (Bioinform., 2015)

FNTM: a server for predicting functional networks of tissues in mouse. - Jonathan Goya; Aaron K. Wong; Victoria Yao; Arjun Krishnan; Max Homilius; Olga G. Troyanskaya (Nucleic Acids Res., 2015)

IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. - Aaron K. Wong; Arjun Krishnan; Victoria Yao; Alicja Tadych; Olga G. Troyanskaya (Nucleic Acids Res., 2015)

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues. - Aaron K. Wong; Arjun Krishnan; Olga G. Troyanskaya (Nucleic Acids Res., 2018)

A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data. - Aaditya V. Rangan; Caroline C. McGrouther; John Kelsoe; Nicholas J. Schork; Eli Stahl; Qian Zhu 005; Arjun Krishnan; Vicky Yao; Olga G. Troyanskaya; Seda Bilaloglu; Preeti Raghavan; Sarah Bergen; et al. (PLoS Comput. Biol., 2018)

Supervised learning is an accurate method for network-based gene classification. - Renming Liu; Christopher A. Mancuso; Anna Yannakopoulos; Kayla A. Johnson; Arjun Krishnan (Bioinform., 2020)

Reconciling multiple connectivity scores for drug repurposing. - Kewalin Samart; Phoebe Tuyishime; Arjun Krishnan; Janani Ravi (Briefings Bioinform., 2021)

PecanPy: a fast, efficient and parallelized Python implementation of node2vec. - Renming Liu; Arjun Krishnan (Bioinform., 2021)

GenePlexus: a web-server for gene discovery using network-based machine learning. - Christopher A. Mancuso; Patrick S. Bills; Douglas Krum; Jacob Newsted; Renming Liu; Arjun Krishnan (Nucleic Acids Res., 2022)

Open Biomedical Network Benchmark: A Python Toolkit for Benchmarking Datasets with Biomedical Networks. - Renming Liu; Arjun Krishnan (MLCB, 2023)

Accurately modeling biased random walks on weighted networks using node2vec+. - Renming Liu; Matthew J. Hirn; Arjun Krishnan (Bioinform., 2023)

PyGenePlexus: a Python package for gene discovery using network-based machine learning. - Christopher A. Mancuso; Renming Liu; Arjun Krishnan (Bioinform., 2023)

CONE: COntext-specific Network Embedding via Contextualized Graph Attention. - Renming Liu; Hao Yuan; Kayla A. Johnson; Arjun Krishnan (MLCB, 2024)

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata. - Hao Yuan; Parker Hicks; Mansooreh Ahmadian; Kayla A. Johnson; Lydia Valtadoros; Arjun Krishnan (Briefings Bioinform., 2024)

Joint representation of molecular networks from multiple species improves gene classification. - Christopher A. Mancuso; Kayla A. Johnson; Renming Liu; Arjun Krishnan (PLoS Comput. Biol., 2024)