Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks

Gene Ontology: tool for the unification of biology

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works

An automated method for finding molecular complexes in large protein interaction networks

Bader, G. D., & Hogue, C. W. V. (2003) · BMC Bioinformatics · doi:10.1186/1471-2105-4-2 · Cited by 6,112 · see citing works

BioGRID: a general repository for interaction datasets

Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960 · see citing works

Evidence for dynamically organized modularity in the yeast protein–protein interaction network

Han, J. J., Bertin, N., Hao, T., Goldberg, D. S., Berriz, G. F., Zhang, L. V., Dupuy, D., Walhout, A. J. M., Cusick, M. E., Roth, F. P., et al. (2004) · Nature · doi:10.1038/nature02555 · Cited by 1,789 · see citing works

A network of protein–protein interactions in yeast

Schwikowski, B., Uetz, P. H., & Fields, S. (2000) · Nature Biotechnology · doi:10.1038/82360 · Cited by 1,371 · see citing works

Walking the Interactome for Prioritization of Candidate Disease Genes

Köhler, S., Bauer, S., Horn, D., & Robinson, P. N. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.02.013 · Cited by 1,268 · see citing works

The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes

Ruepp, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh894 · Cited by 1,134 · see citing works

Network‐based prediction of protein function

Sharan, R., Ulitsky, I., & Ron Shamir, A. (2007) · Molecular Systems Biology · doi:10.1038/msb4100129 · Cited by 1,077 · see citing works

Modeling interactome: scale-free or geometric?

Pržulj, N., Corneil, D. G., & Jurišica, I. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth436 · Cited by 709 · see citing works

Global protein function prediction from protein-protein interaction networks

Vázquez, A., Flammini, A., Maritan, A., & Vespignani, A. (2003) · Nature Biotechnology · doi:10.1038/nbt825 · Cited by 693 · see citing works

Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactions

Chua, H. N., Sung, W.-K. K., & Wong, L. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl145 · Cited by 571 · see citing works

Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps

Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., & Singh, M. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1054 · Cited by 494 · see citing works

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data

Tanay, A., Sharan, R., Kupiec, M., & Ron Shamir, A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308661100 · Cited by 377 · see citing works

Assessment of prediction accuracy of protein function from protein–protein interaction data

Hishigaki, H., Nakai, K., Ono, T., Tanigami, A., & Takagi, T. (2001) · Yeast · doi:10.1002/yea.706 · Cited by 362 · see citing works

KERNEL-BASED DATA FUSION AND ITS APPLICATION TO PROTEIN FUNCTION PREDICTION IN YEAST

Lanckriet, G. R. G., Deng, M., Cristianini, N., Jordan, M. I., & Noble, W. S. (2003) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812704856_0029 · Cited by 322 · see citing works

Whole-genome annotation by using evidence integration in functional-linkage networks

Karaöz, U., Murali, T. M., Letovsky, S., Zheng, Y., Ding, C., Cantor, C. R., & Kasif, S. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307326101 · Cited by 315 · see citing works

OBO-Edit—an ontology editor for biologists

Day-Richter, J., Harris, M. A., Haendel, M. A., The Gene Ontology OBO-Edit Working Group, & Lewis, S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm112 · Cited by 277 · see citing works

How Scale-Free Are Biological Networks

Khanin, R., & Wit, E. C. (2006) · Journal of Computational Biology · doi:10.1089/cmb.2006.13.810 · Cited by 250 · see citing works

Toward a Molecular Understanding of Pleiotropy

He, X., & Zhang, J. (2006) · Genetics · doi:10.1534/genetics.106.060269 · Cited by 219 · see citing works

Iterative Cluster Analysis of Protein Interaction Data

Arnau, V., Mars, S., & Marı́n, I. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti021 · Cited by 214 · see citing works

An automated decision‐tree approach to predicting protein interaction hot spots

Darnell, S. J., Page, D., & Mitchell, J. C. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21474 · Cited by 210 · see citing works

ASSESSMENT OF THE RELIABILITY OF PROTEIN-PROTEIN INTERACTIONS AND PROTEIN FUNCTION PREDICTION

Deng, M., Sun, F., & Chen, T. (2002) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812776303_0014 · Cited by 165 · see citing works

Mapping gene ontology to proteins based on protein–protein interaction data

Deng, M., Tu, Z., Sun, F., & Chen, T. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg500 · Cited by 156 · see citing works

How and when should interactome-derived clusters be used to predict functional modules and protein function?

Song, J., & Singh, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp551 · Cited by 120 · see citing works

Modeling Interactome: Scale-Free or Geometric?Preprint

Pržulj, N., Corneil, D. G., & Jurišica, I. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0404017 · Cited by 94 · see citing works

V avien : An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks

Erten, S., Bebek, G., & Koyutürk, M. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2011.0154 · Cited by 90 · see citing works

Precision and recall estimates for two-hybrid screens

Huang, H., & Bader, J. S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn640 · Cited by 86 · see citing works

CombFunc: predicting protein function using heterogeneous data sources

Wass, M. N., Barton, G. R., & Sternberg, M. J. E. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks489 · Cited by 68 · see citing works

Coverage and error models of protein-protein interaction data by directed graph analysis

Chiang, T., Scholtens, D., Sarkar, D., Gentleman, R. C., & Huber, W. K. (2007) · Genome biology · doi:10.1186/gb-2007-8-9-r186 · Cited by 41 · see citing works

Toward the complete interactome

Friedel, C. C., & Zimmer, R. (2006) · Nature Biotechnology · doi:10.1038/nbt0606-614 · Cited by 19 · see citing works

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749 · see citing works

Network propagation: a universal amplifier of genetic associations

Cowen, L., Ideker, T. E., Raphael, B. J., & Sharan, R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.38 · Cited by 666 · see citing works

Assessment of network module identification across complex diseases

Choobdar, S., Ahsen, M. E., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., Lin, J., Hescott, B. J., Hu, X., Mercer, J., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0509-5 · Cited by 298 · see citing works

Graph representation learning in biomedicine and healthcare

Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works

A foundation model for clinician-centered drug repurposing

Huang, K., Chandak, P., Wang, Q., Havaldar, S., Vaid, A., Leskovec, J., Nadkarni, G. N., Glicksberg, B. S., Gehlenborg, N., & Zitnik, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03233-x · Cited by 249 · see citing works

D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions

Sledzieski, S., Singh, R., Cowen, L., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.08.010 · Cited by 208 · see citing works

Identification of disease treatment mechanisms through the multiscale interactome

Ruiz, C., Zitnik, M., & Leskovec, J. (2021) · Nature Communications · doi:10.1038/s41467-021-21770-8 · Cited by 172 · see citing works

To Embed or Not: Network Embedding as a Paradigm in Computational Biology

Nelson, W., Žitnik, M., Wang, B., Leskovec, J., Goldenberg, A., & Sharan, R. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00381 · Cited by 159 · see citing works

Network enhancement as a general method to denoise weighted biological networks

Wang, B., Pourshafeie, A., Žitnik, M., Zhu, J., Bustamante, C. D., Batzoglou, S., & Leskovec, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05469-x · Cited by 154 · see citing works

Hierarchical HotNet: identifying hierarchies of altered subnetworks

Reyna, M. A., Leiserson, M. D. M., & Raphael, B. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty613 · Cited by 140 · see citing works

New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence

Cao, M., Pietras, C. M., Feng, X., Doroschak, K. J., Schaffner, T., Park, J., Zhang, H., Cowen, L., & Hescott, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu263 · Cited by 120 · see citing works

Current and future directions in network biology

Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 118 · see citing works

Network medicine framework for identifying drug-repurposing opportunities for COVID-19

Morselli Gysi, D., do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S. D., Patten, J. J., Davey, R. A., Loscalzo, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025581118 · Cited by 107 · see citing works

SARS-CoV-2 variants evolve convergent strategies to remodel the host response

Bouhaddou, M., Reuschl, A., Polacco, B. J., Thorne, L., Ummadi, M. R., Ye, C., Rosales, R., Pelin, A., Batra, J., Jang, G. Μ., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.026 · Cited by 116 · see citing works

Exploiting ontology graph for predicting sparsely annotated gene function

Wang, S., Cho, H., Zhai, C., Berger, B., & Peng, J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv260 · Cited by 101 · see citing works

An integrative machine learning approach for prediction of toxicity-related drug safety

Lysenko, A., Sharma, A., Boroevich, K. A., & Tsunoda, T. (2018) · Life Science Alliance · doi:10.26508/lsa.201800098 · Cited by 88 · see citing works

Genes with High Network Connectivity Are Enriched for Disease Heritability

Kim, S. S., Dai, C. L., Hormozdiari, F., van de Geijn, B., Gazal, S., Park, Y., O’Connor, L. J., Amariuta, T., Loh, P., Finucane, H. K., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.03.020 · Cited by 54 · see citing works

Machine learning and network medicine approaches for drug repositioning for COVID-19

Santos, S. D. S., Torres, M., Galeano, D., Sánchez, M. D. M., Cernuzzi, L., & Paccanaro, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100396 · Cited by 55 · see citing works

Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks

Tripathi, B., Parthasarathy, S., Sinha, H., Raman, K., & Ravindran, B. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00164 · Cited by 53 · see citing works

Computational biology in the 21st century

Berger, B., Daniels, N. M., & Yu, Y. W. (2016) · Communications of the ACM · doi:10.1145/2957324 · Cited by 52 · see citing works

Network Approaches for Dissecting the Immune System

Shi, H., Yan, K., Ding, L., Qian, C., Chi, H., & Yu, J. (2020) · iScience · doi:10.1016/j.isci.2020.101354 · Cited by 50 · see citing works

HiDeF: identifying persistent structures in multiscale ‘omics data

Zheng, F., Zhang, S., Churas, C., Pratt, D. R., Bahar, İ., & Ideker, T. E. (2021) · Genome biology · doi:10.1186/s13059-020-02228-4 · Cited by 48 · see citing works

Network-Based Coverage of Mutational Profiles Reveals Cancer Genes

Hristov, B. H., & Singh, M. (2017) · Cell Systems · doi:10.1016/j.cels.2017.09.003 · Cited by 45 · see citing works

Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data

Thistlethwaite, L. R., Li, X., Burrage, L. C., Riehle, K. P., Hacia, J. G., Braverman, N., Wangler, M. F., Miller, M. J., Elsea, S. H., & Milosavljevic, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-10415-5 · Cited by 44 · see citing works

A Survey of Computational Intelligence Techniques in Protein Function Prediction

Tiwari, A. K., & Srivastava, R. (2014) · International Journal of Proteomics · doi:10.1155/2014/845479 · Cited by 43 · see citing works

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Sharma, A. K., Lysenko, A., Boroevich, K. A., & Tsunoda, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-29644-3 · Cited by 42 · see citing works

An efficient method for protein function annotation based on multilayer protein networks

Zhao, B., Hu, S., Li, X., Zhang, F., Tian, Q., & Ni, W. (2016) · Human Genomics · doi:10.1186/s40246-016-0087-x · Cited by 35 · see citing works

Protein function prediction for newly sequenced organisms

Torres, M., Yang, H., Romero, A. E., & Paccanaro, A. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00419-7 · Cited by 33 · see citing works

Computational biology approaches for mapping transcriptional regulatory networks

Saint‐André, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.028 · Cited by 32 · see citing works

Efficient Processing of Network Proximity Queries via Chebyshev Acceleration

Coşkun, M., Grama, A. Y., & Koyutürk, M. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939828 · Cited by 29 · see citing works

Functional protein representations from biological networks enable diverse cross-species inference

Fan, J., Cannistra, A. F., Fried, I., Lim, T., Schaffner, T., Crovella, M. E., Hescott, B. J., & Leiserson, M. D. M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz132 · Cited by 28 · see citing works

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

Wan, C., Cozzetto, D., Fa, R., & Jones, D. T. (2019) · PLoS ONE · doi:10.1371/journal.pone.0209958 · Cited by 27 · see citing works

uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes

Hristov, B. H., Chazelle, B., & Singh, M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.008 · Cited by 23 · see citing works

An Adaptive Multigrid Method Based on Path Cover

Hu, X., Lin, J., & Zikatanov, L. (2019) · SIAM Journal on Scientific Computing · doi:10.1137/18m1194493 · Cited by 23 · see citing works

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

(2025) · doi:10.1038/s41467-025-58439-5 · Cited by 21 · see citing works

Link Prediction in Large Networks by Comparing the Global View of Nodes in the Network

Coşkun, M., & Koyutürk, M. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.195 · Cited by 21 · see citing works

Diffusion geometry of multiplex and interdependent systems

Bertagnolli, G., & Domenico, M. D. (2021) · Physical review. E · doi:10.1103/physreve.103.042301 · Cited by 19 · see citing works

GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

Devkota, K., Murphy, J. M., & Cowen, L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa459 · Cited by 20 · see citing works

Autism genes converge on microtubule biology and RNA-binding proteins during excitatory neurogenesisPreprint

Sun, N., Teyssier, N., Wang, B., Drake, S., Seyler, M., Zaltsman, Y., Everitt, A., Teerikorpi, N., Willsey, H. R., Goodarzi, H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.22.573108 · Cited by 19 · see citing works

Network propagation-based prioritization of long tail genes in 17 cancer types

Mohsen, H., Gunasekharan, V., Qing, T., Seay, M., Surovtseva, Y. V., Negahban, S. N., Szállási, Z., Pusztai, L., & Gerstein, M. (2021) · Genome biology · doi:10.1186/s13059-021-02504-x · Cited by 16 · see citing works

Open Community Challenge Reveals Molecular Network Modules with Key Roles in DiseasesPreprint

Choobdar, S., Ahsen, M. E., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., Lin, J., Hescott, B., Hu, X., Mercer, J., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/265553 · Cited by 18 · see citing works

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19

Desvaux, E., Hamon, A., Hubert, S., Boudjeniba, C., Chassagnol, B., Swindle, J., Aussy, A., Laigle, L., Laplume, J., Soret, P., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254374 · Cited by 17 · see citing works

Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis

Zhou, T.-M., Wang, S., & Xu, J. (2017) · doi:10.1101/240754 · Cited by 25 · see citing works

KiRNet: Kinase-centered network propagation of pharmacological screen results

Bello, T., Chan, M., Golkowski, M. G., Xue, A. G., Khasnavis, N., Ceribelli, M., Ong, S., Thomas, C. J., & Gujral, T. S. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100007 · Cited by 16 · see citing works

Complex intuitionistic fuzzy ordered weighted distance measure

Yang, K., Shu, L., & Yang, G. (2022) · Computational and Applied Mathematics · doi:10.1007/s40314-022-02061-4 · Cited by 16 · see citing works

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures

Saqi, M. A. S., Lysenko, A., Guo, Y., Tsunoda, T., & Auffray, C. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby025 · Cited by 16 · see citing works

Graphlet Laplacians for topology-function and topology-disease relationships

Windels, S. F. L., Malod‐Dognin, N., & Pržulj, N. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz455 · Cited by 15 · see citing works

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

Cui, H., Srinivasan, S., & Korkin, D. (2019) · Genes · doi:10.3390/genes10110933 · Cited by 15 · see citing works

Protein complex prediction via dense subgraphs and false positive analysis

Hernández, C., Mella, C., Navarro, G., Olivera‐Nappa, Á., & Araya, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183460 · Cited by 14 · see citing works

Prediction of Synergistic Toxicity of Binary Mixtures to Vibrio fischeri Based on Biomolecular Interaction Networks

Kim, J., Fischer, M., & Helms, V. (2018) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00164 · Cited by 14 · see citing works

Multiple Order Local Information model for link prediction in complex networks

Yu, J., & Wu, L. (2022) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.127522 · Cited by 14 · see citing works

Construction and contextualization approaches for protein-protein interaction networks

Badkas, A., Landtsheer, S. D., & Sauter, T. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.040 · Cited by 14 · see citing works

Global landscape of the host response to SARS-CoV-2 variants reveals viral evolutionary trajectoriesPreprint

Bouhaddou, M., Reuschl, A., Polacco, B. J., Thorne, L., Ummadi, M. R., Ye, C., Rosales, R., Pelin, A., Batra, J., Jang, G. Μ., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.19.512927 · Cited by 14 · see citing works

Large-scale identification of human protein function using topological features of interaction network

Li, Z., Liu, Z., Zhong, W., Huang, M.-H., Wu, N., Xie, Y. X., Dai, Z., & Zou, X. (2016) · Scientific Reports · doi:10.1038/srep37179 · Cited by 13 · see citing works

Gaussian embedding for large-scale gene set analysis

Wang, S., Flynn, E., & Altman, R. B. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0193-2 · Cited by 13 · see citing works

Indexed fast network proximity querying

Coşkun, M., Grama, A. Y., & Koyutürk, M. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3204028.3204029 · Cited by 13 · see citing works

The Exploitation of Distance Distributions for Clustering

Thrun, M. C. (2021) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026821500164 · Cited by 11 · see citing works

Computing the diffusion state distance on graphs via algebraic multigrid and random projections

Lin, J., Cowen, L., Hescott, B. J., & Hu, X. (2018) · Numerical Linear Algebra with Applications · doi:10.1002/nla.2156 · Cited by 11 · see citing works

MELISSA: Semi-Supervised Embedding for Protein Function Prediction Across Multiple NetworksPreprint

Wu, K., Zhou, D., Slonim, D. K., Hu, X., & Cowen, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.09.552672 · Cited by 10 · see citing works

GLIDER: function prediction from GLIDE-based neighborhoods

Devkota, K., Schmidt, H., Werenski, M., Murphy, J. M., Erden, M., Arsenescu, V., & Cowen, L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac322 · Cited by 9 · see citing works

NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks

Chitra, U., Park, T. Y., & Raphael, B. J. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2022.0336 · Cited by 9 · see citing works

Linearity of network proximity measures: implications for set-based queries and significance testing

Maxwell, S., Chance, M. R., & Koyutürk, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw733 · Cited by 9 · see citing works

Revealing protein functions based on relationships of interacting proteins and GO terms

Teng, Z., Guo, M., Liu, X., Tian, Z., & Che, K. (2017) · Journal of Biomedical Semantics · doi:10.1186/s13326-017-0139-8 · Cited by 9 · see citing works

Stability Analysis of Biological Networks’ Diffusion State

Altuntaş, V., Gök, M., & Kahveci, T. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2881887 · Cited by 9 · see citing works

A new class of metrics for learning on real-valued and structured data

Yang, R., Jiang, Y., Mathews, S., Housworth, E. A., Hahn, M. W., & Predrag Radivojac, A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00622-6 · Cited by 8 · see citing works

The module triad: a novel network biology approach to utilize patients’ multi-omics data for target discovery in ulcerative colitis

Voitalov, I., Zhang, L., Kilpatrick, C. L., Withers, J. B., Saleh, A., Akmaev, V. R., & Ghiassian, S. D. (2022) · Scientific Reports · doi:10.1038/s41598-022-26276-x · Cited by 8 · see citing works

A Multi-Species Functional Embedding Integrating Sequence and Network StructurePreprint

Fan, J., Cannistra, A. F., Fried, I., Lim, T., Schaffner, T., Crovella, M. E., Hescott, B., & Leiserson, M. D. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/229211 · Cited by 7 · see citing works

Protein function annotation based on heterogeneous biological networks

Hu, S., Luo, Y., Zhang, Z., Xiong, H., Yan, W., Jiang, M., & Zhao, B. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05057-3 · Cited by 6 · see citing works

Computing Diffusion State Distance Using Green’s Function and Heat Kernel on Graphs

Boehnlein, E., Chin, P. S., Sinha, A., & Lü, L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13123-8_7 · Cited by 6 · see citing works

TRENDY: gene regulatory network inference enhanced by transformer

Tian, X., Patel, Y., & Wang, Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf314 · Cited by 5 · see citing works

The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations

Cui, H., Srinivasan, S., Gao, Z., & Korkin, D. (2023) · Biomolecules · doi:10.3390/biom14010040 · Cited by 5 · see citing works

A Guided Network Propagation Approach to Identify Disease Genes that Combines Prior and New Information

Hristov, B. H., Chazelle, B., & Singh, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45257-5_25 · Cited by 5 · see citing works

Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks

Cowen, L., Devkota, K., Hu, X., Murphy, J. M., & Wu, K. (2021) · SIAM Journal on Mathematics of Data Science · doi:10.1137/20m1324089 · Cited by 5 · see citing works

A network embedding approach to identify active modules in biological interaction networks

Pasquier, C., Guerlais, V., Pallez, D., Rapetti‐Mauss, R., & Soriani, O. (2023) · Life Science Alliance · doi:10.26508/lsa.202201550 · Cited by 4 · see citing works

Common molecular links and therapeutic insights between type 2 diabetes and kidney cancer

Ahmmed, R., Islam, M. A., Hasan, M. M.-U., Sarker, A., Ali, M. A., Islam, M. S., Sultana, M., & Mollah, M. N. H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330619 · Cited by 4 · see citing works

Simplicity within biological complexity

Pržulj, N., & Malod‐Dognin, N. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae164 · Cited by 4 · see citing works

Design of affinity-aware encoding by embedding graph centrality for graph classification

Dong, W., Wu, J., Bai, Z., Li, W., & Qiao, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.010 · Cited by 4 · see citing works

Link prediction in large directed graphs

García-Gasulla, D. (2015) · doi:10.5821/dissertation-2117-95691 · Cited by 4 · see citing works

MUNDO: protein function prediction embedded in a multispecies world

Arsenescu, V., Devkota, K., Erden, M., Shpilker, P., Werenski, M., & Cowen, L. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab025 · Cited by 3 · see citing works

Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network

Altuntaş, V. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3185406 · Cited by 3 · see citing works

WeSA: a web server for improving analysis of affinity proteomics data

Shtetinska, M. M., González‐Sánchez, J. C., Beyer, T., Boldt, K., Ueffing, M., & Russell, R. B. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae423 · Cited by 3 · see citing works

Identification of active modules in interaction networks using node2vec network embeddingPreprint

Pasquier, C., Guerlais, V., Pallez, D., Rapetti‐Mauss, R., & Soriani, O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.22.461345 · Cited by 3 · see citing works

Simulation and Computational Study of RING Domain Mutants of BRCA1 and Ube2k in AD/PD Pathophysiology

Sahu, M., Rani, N., & Kumar, P. (2024) · Molecular Biotechnology · doi:10.1007/s12033-023-01006-4 · Cited by 2 · see citing works

Leveraging transcription factor physical proximity for enhancing gene regulation inference

Huang, X., Hullur, A. R., Jafari, E., Shridhar, K., Zhou, M., Mackie, K., Huang, K., & Wang, Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf186 · Cited by 2 · see citing works

Response of Lyapunov exponents to diffusion state of biological networks

Altuntaş, V., Gök, M., & Koçal, O. H. (2020) · International Journal of Applied Mathematics and Computer Science · doi:10.34768/amcs-2020-0051 · Cited by 2 · see citing works

Deep Learning the Protein Function in Protein Interaction Networks

Trivodaliev, K. V., Josifoski, M., & Kalajdziski, S. (2018) · Communications in computer and information science · doi:10.1007/978-3-030-00825-3_16 · Cited by 2 · see citing works

NetMix2: Unifying Network Propagation and Altered Subnetworks

Chitra, U., Park, T. Y., & Raphael, B. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04749-7_12 · Cited by 2 · see citing works

Detangling PPI Networks to Uncover Functionally Meaningful Clusters

Hall-Swan, S., Crawford, J., Newman, R., & Cowen, L. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3110409 · Cited by 2 · see citing works

The stability and fragility of biological networks: Eukaryotic model organism Saccharomyces cerevisiae

Altuntaş, V., & Gök, M. (2017) · doi:10.1109/ubmk.2017.8093575 · Cited by 2 · see citing works

Majority Vote Cascading: A Semi-Supervised Framework for Improving Protein Function Prediction

Lazarsfeld, J., Rodriguez, J. D. A., Erden, M., Liu, Y., & Cowen, L. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3059812 · Cited by 2 · see citing works

A Note on Diffusion State DistancePreprint

Madras, N. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.07315 · Cited by 2 · see citing works

Graphlet Laplacians: graphlet-based neighbourhoods highlight topology-function and topology-disease relationshipsPreprint

Windels, S. F. L., Malod‐Dognin, N., & Pržulj, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/460964 · Cited by 2 · see citing works

Network Propagation-based Prioritization of Long Tail Genes in 17 Cancer TypesPreprint

Mohsen, H., Gunasekharan, V., Qing, T., Seay, M., Surovtseva, Y., Negahban, S., Szállási, Z., Pusztai, L., & Gerstein, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.05.429983 · Cited by 2 · see citing works

Computational Enhanced Hi-C data reveals the function of structural geometry in genomic regulationPreprint

He, Y., Xue, Y., Wang, J., Huang, Y., Liu, L., Huang, Y., & Gao, Y. Q. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.12.499232 · Cited by 2 · see citing works

Optimized Molecular Interaction Networks for the Study of Skeletal Muscle

Morgan, S., Malatras, A., Duguez, S., & Duddy, W. J. (2021) · Journal of Neuromuscular Diseases · doi:10.3233/jnd-210680 · Cited by 1 · see citing works

Gene prioritization based on random walks with restarts and absorbing states, to define gene sets regulating drug pharmacodynamics from single-cell analyses

de Queiroz, A. S., Cruz, G. S. S., Jean‐Marie, A., Mazauric, D., Roux, J., & Cazals, F. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268956 · Cited by 1 · see citing works

Random-Walk Based Approximate k-Nearest Neighbors Algorithm for Diffusion State Distance

Cowen, L., Hu, X., Lin, J., Shen, Y. G., & Wu, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97549-4_1 · Cited by 1 · see citing works

Combining spectral clustering and large cut algorithms to find compensatory functional modules from yeast physical and genetic interaction data with GLASS

Kolawole, B., & Cowen, L. (2022) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3535508.3545509 · Cited by 1 · see citing works

Are trait‐associated genes clustered together in a gene network?

Koo, H. J., & Pan, W. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22557 · Cited by 1 · see citing works

New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence

Cao, M., Pietras, C. M., Feng, X., Doroschak, K. J., Schaffner, T., Park, J., Zhang, H., Cowen, L., & Hescott, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu263 · Cited by 120 · see citing works

Novel Gene Ontology Based Distance Metric for Function Prediction via Clustering in Protein Interaction Networks

Trivodaliev, K. V., Ivanoska, I., Kalajdziski, S., & Kocarev, L. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-09879-1_17

Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network

Altuntaş, V. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3185406 · Cited by 3 · see citing works

PREDICTION OF PROTEIN FUNCTION FROM CONNECTIVITY OF PROTEIN INTERACTION NETWORKS

Shi, L., Cho, Y., & Zhang, A. (2010) · International Journal of Computational Bioscience · doi:10.2316/journal.210.2010.1.210-1009 · Cited by 8 · see citing works

FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis

Saha, S., Chatterjee, P., Basu, S., Kundu, M., & Nasipuri, M. (2014) · Cellular & Molecular Biology Letters · doi:10.2478/s11658-014-0221-5 · Cited by 33 · see citing works