Arjun Krishnan

发表

Understanding multicellular function and disease with human tissue-specific networks

Daniel S. Himmelstein, K. Dolinski, O. Troyanskaya, 2015, Nature Genetics.

Integrative Networks Illuminate Biological Factors Underlying Gene–Disease Associations

Jaclyn N. Taroni, C. Greene, Arjun Krishnan, 2016, bioRxiv.

Co-expression signatures of combinatorial gene regulation

E. Grotewold, Arjun Krishnan, S. Shiu, 2020, bioRxiv.

Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder

O. Troyanskaya, A. Lash, N. Volfovsky, 2016, bioRxiv.

Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder

Chandra L. Theesfeld, O. Troyanskaya, A. Lash, 2016, Nature Neuroscience.

Pervasive epistasis in cell proliferation pathways modulates neurodevelopmental defects of autism-associated 16p11.2 deletion

S. Girirajan, J. Badano, Qingyu Wang, 2017, bioRxiv.

A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

Jacob L Canfield, Arjun Krishnan, Deepak Singla, 2020, Nucleic acids research.

Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stress

L. Rahman, Arjun Krishnan, N. Baisakh, 2014, Nature Communications.

Drought responsive genes and their functional terms identified by GS FLX Pyro sequencing in maize.

Arjun Krishnan, Andy Pereira, Madana M. R. Ambavaram, 2014 .

SANe: The Seed Active Network for Discovering Transcriptional Regulatory Programs of Seed Development

Arjun Krishnan, E. Collakova, Andy Pereira, 2017, bioRxiv.

Coordinated Activation of Cellulose and Repression of Lignin Biosynthesis Pathways in Rice1[C][W][OA]

Arjun Krishnan, Andy Pereira, K. Trijatmiko, 2010, Plant Physiology.

Systems analysis of stress response in plants

Arjun Krishnan, 2010 .

Accurately Modeling Biased Random Walks on Weighted Graphs Using Node2vec+

M. Hirn, Arjun Krishnan, Renming Liu, 2021, ArXiv.

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Arjun Krishnan, Andy Pereira, Madana M. R. Ambavaram, 2017, bioRxiv.

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Arjun Krishnan, Andy Pereira, Madana M. R. Ambavaram, 2017, bioRxiv.

Identification of genes directly regulated by a transcription factor in rice

L. Rahman, Arjun Krishnan, Supratim Basu, 2014 .

GenePlexus: a web-server for gene discovery using network-based machine learning

Arjun Krishnan, Christopher A Mancuso, Renming Liu, 2022, Nucleic Acids Res..

PyGenePlexus: a Python package for gene discovery using network-based machine learning

Arjun Krishnan, Christopher A Mancuso, Renming Liu, 2022, bioRxiv.

Functional assessment of the “two-hit” model for neurodevelopmental defects in Drosophila and X. laevis

S. Girirajan, L. A. Lowery, Arjun Krishnan, 2020, bioRxiv.

A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities

Arjun Krishnan, Christopher A Mancuso, Stephanie L. Hickey, 2022, bioRxiv.

The topological shape of gene expression across the evolution of flowering plants

E. Munch, Arjun Krishnan, D. Chitwood, 2022, bioRxiv.

nleval: A Python Toolkit for Generating Benchmarking Datasets for Machine Learning with Biological Networks

Arjun Krishnan, Renming Liu, 2023, bioRxiv.

Stochastic modeling of dwell-time distributions during transcriptional pausing and initiation

Xiaohua Xu, Arjun Krishnan, Rahul V. Kulkarni, 2013, 52nd IEEE Conference on Decision and Control.

Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissue Revealed by RNA-Seq1[W][OA]

Arjun Krishnan, Akshay Kakumanu, R. Grene, 2012, Plant Physiology.

Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms

Qian Zhu, Olga G. Troyanskaya, Christopher Y. Park, 2015, Bioinform..

Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants

J. Rosenfeld, M. Fichera, C. Romano, 2018, Genetics in Medicine.

Rare variants in the genetic background modulate the expressivity of neurodevelopmental disorders

J. Rosenfeld, M. Fichera, C. Romano, 2018, bioRxiv.

A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

Deepak Singla, Arjun Krishnan, Christopher A Mancuso, 2020, bioRxiv.

Reconciling multiple connectivity scores for drug repurposing

Arjun Krishnan, Kewalin Samart, Phoebe Tuyishime, 2020, Briefings Bioinform..

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

Kayla A Johnson, Arjun Krishnan, Arjun Krishnan, 2020, Genome Biology.

Combinatorial patterns of gene expression changes contribute to variable expressivity of the developmental delay-associated 16p12.1 deletion

S. Girirajan, Arjun Krishnan, Matthew Jensen, 2021, Genome Medicine.

Pervasive genetic interactions modulate neurodevelopmental defects of the autism-associated 16p11.2 deletion in Drosophila melanogaster

S. Girirajan, J. Badano, Qingyu Wang, 2018, Nature Communications.

Molecular and Physiological Analysis of Drought Stress in Arabidopsis Reveals Early Responses Leading to Acclimation in Plant Growth1[C][W][OA]

Arjun Krishnan, Madana M. R. Ambavaram, A. Pereira, 2010, Plant Physiology.

Leveraging public transcriptome data with machine learning to infer pan-body age- and sex-specific molecular phenomena

Kayla A Johnson, Arjun Krishnan, 2023, bioRxiv.

Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene

A. Aharoni, Arjun Krishnan, K. Nataraja, 2007, Proceedings of the National Academy of Sciences.

Rice GROWTH UNDER DROUGHT KINASE Is Required for Drought Tolerance and Grain Yield under Normal and Drought Stress Conditions1[C][W][OPEN]

Arjun Krishnan, Andy Pereira, Supratim Basu, 2014, Plant Physiology.

Integrative networks illuminate biological factors underlying gene-disease associations

Jaclyn N. Taroni, C. Greene, Arjun Krishnan, 2016, bioRxiv.

Accurately modeling biased random walks on weighted networks using node2vec+

M. Hirn, Arjun Krishnan, Renming Liu, 2022, bioRxiv.

Accurately Modeling Biased Random Walks on Weighted Graphs Using Node2vec+ pdf

Arjun Krishnan, Renming Liu, Matthew Hirn, 2021, ArXiv.

IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks

Olga G. Troyanskaya, Arjun Krishnan, Aaron K. Wong, 2015, Nucleic Acids Res..

Supervised learning is an accurate method for network-based gene classification

Kayla A Johnson, Arjun Krishnan, Christopher A Mancuso, 2020, Bioinformatics.

A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.

Arjun Krishnan, Christie S. Chang, Young-Suk Lee, 2019, Cell systems.

Targeted exploration and analysis of large cross-platform human transcriptomic compendia

Kai Li, Moses Charikar, Olga G. Troyanskaya, 2015, Nature Methods.

PecanPy: a fast, efficient and parallelized Python implementation of node2vec

Arjun Krishnan, Renming Liu, Arjun Krishnan, 2020, bioRxiv.

Supervised learning is an accurate method for network-based gene classification

Kayla A Johnson, Anna Yannakopoulos, Arjun Krishnan, 2019, bioRxiv.

Systematic tissue annotations of genomics samples by modeling unstructured metadata

Arjun Krishnan, L. Guare, Nathaniel T. Hawkins, 2021, bioRxiv.

FNTM: a server for predicting functional networks of tissues in mouse

Olga G. Troyanskaya, Arjun Krishnan, Jonathan Goya, 2015, Nucleic Acids Res..

A network-based drug repurposing approach identifies new treatment opportunities for the systemic chronic inflammation underlying multiple complex diseases

Arjun Krishnan, Christopher A Mancuso, Stephanie L. Hickey, 2022 .

Mutant Resources in Rice for Functional Genomics of the Grasses[W]

H. Leung, Chang-deok Han, Qifa Zhang, 2009, Plant Physiology.

The Codex Aubin: Nahuatl Glyphic Writing in Post-Conquest Mexico

Arjun Krishnan, 2021 .

Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies

Qian Zhu, Olga G. Troyanskaya, Arjun Krishnan, 2013, Bioinform..

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues

Olga G. Troyanskaya, Arjun Krishnan, Aaron K. Wong, 2018, Nucleic Acids Res..

Integrative approaches for mining transcriptional regulatory programs in Arabidopsis.

Arjun Krishnan, Andy Pereira, Arjun Krishnan, 2008, Briefings in functional genomics & proteomics.

Joint representation of molecular networks from multiple species improves gene classification

Kayla A Johnson, Arjun Krishnan, Christopher A Mancuso, 2023, bioRxiv.

Low‐variance RNAs identify Parkinson's disease molecular signature in blood

O. Troyanskaya, S. Sealfon, Yongchao Ge, 2015, Movement disorders : official journal of the Movement Disorder Society.

Pervasive genetic interactions modulate neurodevelopmental defects of the autism-associated 16p11.2 deletion in Drosophila melanogaster

S. Girirajan, J. Badano, Qingyu Wang, 2018, Nature Communications.

Reconciling differential gene expression data with molecular interaction networks

T. M. Murali, Arjun Krishnan, Christopher L. Poirel, 2013, Bioinform..

A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data

Aaditya V. Rangan, Qian Zhu, Olga G. Troyanskaya, 2018, PLoS Comput. Biol..

Microarray data analysis.

Arjun Krishnan, Saroj K Mohapatra, Arjun Krishnan, 2011, Methods in molecular biology.

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

Kayla A Johnson, Arjun Krishnan, 2022, Genome Biology.

Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants

J. Rosenfeld, M. Fichera, C. Romano, 2018, Genetics in Medicine.

Current and future directions in network biology

Anna M. Ritz, Michelle M. Li, Danny Z. Chen, 2023, 2309.08478.

Understanding multicellular function and disease with human tissue-specific networks

Daniel S. Himmelstein, Boris M. Hartmann, K. Dolinski, 2015, Nature Genetics.

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues

O. Troyanskaya, Arjun Krishnan, A. Wong, 2018, Nucleic Acids Res..

Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissue Revealed by RNA-Seq1[W][OA]

Arjun Krishnan, Akshay Kakumanu, R. Grene, 2012, Plant Physiology.

Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms

Christopher Y. Park, O. Troyanskaya, Young-suk Lee, 2015, Bioinform..

Open Biomedical Network Benchmark A Python Toolkit for Benchmarking Datasets with Biomedical Networks

Arjun Krishnan, Renming Liu, 2023, bioRxiv.

CONE: COntext-specific Network Embedding via Contextualized Graph Attention

Kayla A Johnson, Arjun Krishnan, Renming Liu, 2023, bioRxiv.

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata

Kayla A Johnson, Arjun Krishnan, Mansooreh Ahmadian, 2024, bioRxiv.