Network analysis reveals rare disease signatures across multiple levels of biological organization

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

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works

Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works

node2vec: Scalable Feature Learning for Networks

Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799 · see citing works

The igraph software package for complex network research

Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078 · see citing works

The mutational constraint spectrum quantified from variation in 141,456 humans

Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2020) · Nature · doi:10.1038/s41586-020-2308-7 · Cited by 9,434 · see citing works

dbSNP: the NCBI database of genetic variation

Sherry, S. T. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.308 · Cited by 7,502 · see citing works

A general framework for estimating the relative pathogenicity of human genetic variants

Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183 · see citing works

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works

Network medicine: a network-based approach to human disease

Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034 · see citing works

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., White, K. M., O’Meara, M. J., Rezelj, V. V., Guo, J. Z., Swaney, D. L., et al. (2020) · Nature · doi:10.1038/s41586-020-2286-9 · Cited by 4,405 · see citing works

ClinVar: improving access to variant interpretations and supporting evidence

Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1153 · Cited by 4,196 · see citing works

An Information-Theoretic Definition of Similarity

Lin, D. (1998) · Cited by 3,803 · see citing works

Mixing patterns in networks.

Newman, M. (2002) · Physical review. E, Statistical, nonlinear, and soft matter physics · doi:10.1103/physreve.67.026126 · Cited by 3,130 · see citing works

ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

Chen, J., Bardes, E. E., Aronow, B. J., & Jegga, A. G. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp427 · Cited by 2,952 · see citing works

A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome

Stelzl, U., Worm, U., Lalowski, M. M., Haenig, C., Brembeck, F. H., Goehler, H., Strödicke, M., Zenkner, M., Schoenherr, A., Koeppen, S., et al. (2005) · Cell · doi:10.1016/j.cell.2005.08.029 · Cited by 2,389 · see citing works

Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

Resnik, P. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.514 · Cited by 2,098 · see citing works

Graph embedding techniques, applications, and performance: A survey

Goyal, P., & Ferrara, E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.022 · Cited by 1,801 · see citing works

Interactome Networks and Human Disease

Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771 · see citing works

Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database

Nguengang Wakap, S., Lambert, D. M., Olry, A., Rodwell, C., Gueydan, C., Lanneau, V., Murphy, D., Le Cam, Y., & Rath, A. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-019-0508-0 · Cited by 1,752 · see citing works

Reactome: a database of reactions, pathways and biological processes

Croft, D., O'Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1018 · Cited by 1,751 · see citing works

Uncovering disease-disease relationships through the incomplete interactome

Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabási, A.-L. (2015) · Science · doi:10.1126/science.1257601 · Cited by 1,546 · see citing works

Architecture of the human interactome defines protein communities and disease networks

Huttlin, E. L., Bruckner, R. J., Paulo, J. A., Cannon, J. R., Ting, L., Baltier, K., Colby, G., Gebreab, F., Gygi, M. P., Parzen, H., et al. (2017) · Nature · doi:10.1038/nature22366 · Cited by 1,437 · see citing works

A Core Complex of BBS Proteins Cooperates with the GTPase Rab8 to Promote Ciliary Membrane Biogenesis

Nachury, M. V., Loktev, A. V., Zhang, Q., Westlake, C. J., Peränen, J., Merdes, A., Slusarski, D. C., Scheller, R. H., Bazan, J. F., Sheffield, V. C., et al. (2007) · Cell · doi:10.1016/j.cell.2007.03.053 · Cited by 1,384 · see citing works

A Proteome-Scale Map of the Human Interactome Network

Rolland, T., Taşan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., Yi, S. S., Lemmens, I. H., Fontanillo, C., Mosca, R., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.050 · Cited by 1,342 · 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

A reference map of the human binary protein interactome

Luck, K., Kim, D.-K., Lambourne, L., Spirohn, K., Begg, B. E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F. J., Charloteaux, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2188-x · Cited by 1,197 · see citing works

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome

Huttlin, E. L., Bruckner, R. J., Navarrete-Perea, J., Cannon, J. R., Baltier, K., Gebreab, F., Gygi, M. P., Thornock, A., Zarraga, G., Tam, S., et al. (2020) · doi:10.1101/2020.01.19.905109 · Cited by 960 · see citing works

Extracting the multiscale backbone of complex weighted networks

Serrano, M. Á., Boguñá, M., & Vespignani, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808904106 · Cited by 898 · see citing works

The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

Köhler, S., Doelken, S., Mungall, C. J., Bauer, S., Firth, H., Bailleul-Forestier, I., Black, G., Brown, D. L., Brudno, M., Campbell, J., et al. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1026 · Cited by 840 · see citing works

Understanding multicellular function and disease with human tissue-specific networks

Greene, C. S., Krishnan, A., Wong, A. K., Ricciotti, E., Zelaya, R. A., Himmelstein, D. S., Zhang, R., Hartmann, B. M., Zaslavsky, E., Sealfon, S. C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3259 · Cited by 832 · see citing works

Network-based prediction of drug combinations

Cheng, F., Kovács, I. A., & Barabási, A.-Ĺ. (2019) · Nature Communications · doi:10.1038/s41467-019-09186-x · Cited by 812 · see citing works

Semantic Similarity in Biomedical Ontologies

Pesquita, C., Faria, D., Falcão, A. O., Lord, P., & Couto, F. M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000443 · Cited by 784 · see citing works

Rare-disease genetics in the era of next-generation sequencing: discovery to translation

Boycott, K. M., Vanstone, M. R., Bulman, D. E., & MacKenzie, A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3555 · Cited by 760 · 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

OmniPath: guidelines and gateway for literature-curated signaling pathway resources

Türei, D., Korcsmaros, T. D., & Sáez-Rodríguez, J. (2016) · Nature Methods · doi:10.1038/nmeth.4077 · Cited by 660 · see citing works

Human symptoms–disease network

Zhou, X., Menche, J., Barabási, A.-Ĺ., & Sharma, A. (2014) · Nature Communications · doi:10.1038/ncomms5212 · Cited by 649 · see citing works

Network-based in silico drug efficacy screening

Guney, E., Menche, J., Vidal, M., & Barábasi, A.-L. (2016) · Nature Communications · doi:10.1038/ncomms10331 · Cited by 625 · see citing works

Paediatric genomics: diagnosing rare disease in children

Wright, C. F., FitzPatrick, D. R., & Firth, H. V. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.116 · Cited by 616 · see citing works

PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels

Martin, A. R., Williams, E., Foulger, R. E., Leigh, S., Daugherty, L. C., Niblock, O., Leong, I. U. S., Smith, K. R., Gerasimenko, O., Haraldsdottir, E., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0528-2 · Cited by 559 · see citing works

Cell type–specific genetic regulation of gene expression across human tissues

Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2020) · Science · doi:10.1126/science.aaz8528 · Cited by 530 · see citing works

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

Alanis‐Lobato, G., Andrade-Navarro, M. A., & Schaefer, M. H. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw985 · Cited by 520 · see citing works

Next-generation diagnostics and disease-gene discovery with the Exomiser

Smedley, D., Jacobsen, J. O. B., Jäger, M., Köhler, S., Holtgrewe, M., Schubach, M., Siragusa, E., Zemojtel, T., Buske, O. J., Washington, N. L., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2015.124 · Cited by 445 · see citing works

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

Mungall, C. J., McMurry, J. A., Köhler, S., Balhoff, J. P., Borromeo, C., Brush, M., Carbon, S., Conlin, T., Dunn, N., Engelstad, M., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1128 · Cited by 441 · see citing works

Using deep learning to model the hierarchical structure and function of a cell

Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018) · Nature Methods · doi:10.1038/nmeth.4627 · Cited by 435 · see citing works

Gene ontology semantic similarity analysis using GOSemSim

Yu, G. (2020) · Methods in Molecular Biology · doi:10.1007/978-1-0716-0301-7_11 · Cited by 392 · see citing works

A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome

Ghiassian, S. D., Menche, J., & Barabási, A.-Ĺ. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004120 · Cited by 388 · see citing works

Graph embedding on biomedical networks: methods, applications and evaluations

Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S. M., Zhang, W., Zhang, P., & Sun, H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz718 · Cited by 379 · see citing works

Metrics for GO based protein semantic similarity: a systematic evaluation

Pesquita, C., Faria, D., Bastos, H. P., Ferreira, A. E. N., Falcão, A. O., & Couto, F. M. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-s5-s4 · Cited by 361 · see citing works

The mammalian phenotype ontology: enabling robust annotation and comparative analysis

Smith, C. L., & Eppig, J. T. (2009) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.44 · Cited by 353 · see citing works

Random walk with restart on multiplex and heterogeneous biological networks

Valdeolivas, A., Tichit, L., Navarro, C. L., Perrin, S., Odelin, G., Lévy, N., Cau, P., Rémy, É., & Baudot, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty637 · Cited by 335 · see citing works

Network biology concepts in complex disease comorbidities

Hu, J. X., Thomas, C. E., & Brunak, S. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.87 · Cited by 328 · see citing works

Systematic Phenomics Analysis Deconvolutes Genes Mutated in Intellectual Disability into Biologically Coherent Modules

Kochinke, K., Zweier, C., Nijhof, B., Fencková, M., Čížek, P., Honti, F., Keerthikumar, S., Oortveld, M. A. W., Kleefstra, T., Kramer, J. M., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.024 · Cited by 327 · see citing works

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Huang, J. K., Carlin, D. E., Yu, M. K., Zhang, W., Kreisberg, J. F., Tamayo, P., & Ideker, T. (2018) · Cell Systems · doi:10.1016/j.cels.2018.03.001 · Cited by 289 · see citing works

Exome Sequencing and the Management of Neurometabolic Disorders

Tarailo-Graovac, M., Shyr, C., Ross, C. J., Horvath, G. A., Salvarinova, R., Ye, X. C., Zhang, L.-H., Bhavsar, A. P., Lee, J. J. Y., Drögemöller, B. I., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1515792 · Cited by 281 · see citing works

Bardet–Biedl syndrome: beyond the cilium

Tobin, J. L., & Beales, P. L. (2007) · Pediatric Nephrology · doi:10.1007/s00467-007-0435-0 · Cited by 241 · see citing works

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Costanzo, M., Kuzmin, E., van Leeuwen, J., Mair, B., Moffat, J., Boone, C., & Andrews, B. (2019) · Cell · doi:10.1016/j.cell.2019.01.033 · Cited by 241 · see citing works

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

Shefchek, K., Harris, N. L., Gargano, M. A., Matentzoglu, N., Unni, D. R., Brush, M., Keith, D., Conlin, T., Vasilevsky, N., Zhang, X., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz997 · Cited by 235 · see citing works

CD55 Deficiency, Early-Onset Protein-Losing Enteropathy, and Thrombosis

Ozen, A. O., Comrie, W. A., Ardy, R. C., Conde, C. D., Dalgıç, B., Beșer, Ö. F., Morawski, A. R., Karakoç-Aydıner, E., Tutar, E., Barış, S., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1615887 · Cited by 203 · see citing works

Inherited DOCK2 Deficiency in Patients with Early-Onset Invasive Infections

Dobbs, K., Domínguez Conde, C., Zhang, S.-Y., Parolini, S., Audry, M., Chou, J., Haapaniemi, E., Keles, S., Bilic, I., Okada, S., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1413462 · Cited by 201 · see citing works

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma

Sharma, A., Menche, J., Huang, C., Ort, T., Zhou, X., Kitsak, M., Sahni, N., Thibault, D. M., Voung, L., Guo, F., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv001 · Cited by 193 · see citing works

Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease The eGTEx Project

Stranger, B., Brigham, L., Hasz, R., Hunter, M., Johns, C., Johnson, M. D., Kopen, G., Leinweber, W. F., Lonsdale, J., McDonald, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3969 · Cited by 189 · see citing works

NGS Technologies as a Turning Point in Rare Disease Research , Diagnosis and Treatment

Marmiesse, A. F., Gouveia, S., & Couce, M. L. (2017) · Current Medicinal Chemistry · doi:10.2174/0929867324666170718101946 · Cited by 180 · see citing works

Sharing and Specificity of Co-expression Networks across 35 Human Tissues

Pierson, E., the GTEx Consortium, Koller, D., Battle, A., & Mostafavi, S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004220 · Cited by 177 · 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

Co-expression networks reveal the tissue-specific regulation of transcription and splicingPreprint

Saha, A., Kim, Y., Gewirtz, A. D., Jo, B., Gao, C., McDowell, I. C., Engelhardt, B. E., & Battle, A. J. (2017) · Genome Research · doi:10.1101/gr.216721.116 · Cited by 171 · see citing works

Biallelic Mutations in DNAJC12 Cause Hyperphenylalaninemia, Dystonia, and Intellectual Disability

Anikster, Y., Haack, T. B., Vilboux, T., Pode‐Shakked, B., Thöny, B., Shen, N., Guarani, V., Meißner, T., Mayatepek, E., Trefz, F. K., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.01.002 · Cited by 162 · see citing works

ontologyX: a suite of R packages for working with ontological data

Greene, D. J., Richardson, S. T., & Turro, E. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw763 · Cited by 161 · see citing works

Mechanisms of tissue and cell-type specificity in heritable traits and diseases

Hekselman, I., & Yeger‐Lotem, E. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0200-9 · Cited by 146 · see citing works

RASGRP1 deficiency causes immunodeficiency with impaired cytoskeletal dynamics

Salzer, E., Cağdaş, D. N., Hons, M., Mace, E. M., Garncarz, W., Petronczki, Ö. Y., Platzer, R., Pfajfer, L., Bilić, I., Ban, S. A., et al. (2016) · Nature Immunology · doi:10.1038/ni.3575 · Cited by 142 · see citing works

Mutations in EFL1, an SBDS partner, are associated with infantile pancytopenia, exocrine pancreatic insufficiency and skeletal anomalies in aShwachman-Diamond like syndrome

Stepensky, P., Chacón-Flores, M., Kim, K. H., Abuzaitoun, O., Bautista-Santos, A., Simanovsky, N., Siliqi, D., Altamura, D., Méndez-Godoy, A., Gijsbers, A., et al. (2017) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2016-104366 · Cited by 126 · see citing works

Erratum: Paediatric genomics: diagnosing rare disease in children

Wright, C. F., FitzPatrick, D. R., & Firth, H. V. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2018.12 · Cited by 103 · see citing works

Local assortativeness in scale-free networks

Piraveenan, M., Prokopenko, M., & Zomaya, A. Y. (2008) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/84/28002 · Cited by 99 · see citing works

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases

Zurek, B., Ellwanger, K., Vissers, L. E. L. M., Schüle, R., Synofzik, M., Töpf, A., de Voer, R. M., Laurie, S., Matalonga, L., Gilissen, C., et al. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00859-0 · Cited by 99 · see citing works

The stem cell factor/c-kit receptor pathway enhances proliferation and invasion of pancreatic cancer cells.

Yasuda, A., Sawai, H., Takahashi, H., Ochi, N., Matsuo, Y., Funahashi, H., Sato, M., Okada, Y., Takeyama, H., & Manabe, T. (2006) · Molecular Cancer · doi:10.1186/1476-4598-5-46 · Cited by 91 · see citing works

Shwachman-Diamond Syndrome: Molecular Mechanisms and Current Perspectives

Bezzerri, V., & Cipolli, M. (2018) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-018-0368-2 · Cited by 88 · see citing works

A network of human functional gene interactions from knockout fitness screens in cancer cells

Kim, E., Dede, M., Lenoir, W. F., Wang, G., Srinivasan, S., Colic, M., & Hart, T. (2019) · Life Science Alliance · doi:10.26508/lsa.201800278 · Cited by 84 · see citing works

Genetic interaction between Bardet-Biedl syndrome genes and implications for limb patterning

Tayeh, M. K., Yen, H.-J., Beck, J. S., Searby, C. C., Westfall, T. A., Griesbach, H. L., Sheffield, V. C., & Slusarski, D. C. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn093 · Cited by 78 · see citing works

Next-generation drug repurposing using human genetics and network biology

Nabirotchkin, S., Peluffo, A. E., Rinaudo, P., Yu, J., Hajj, R., & Cohen, D. (2020) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2019.12.004 · Cited by 76 · see citing works

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

Paulson, J. N., Chen, C.-Y., Lopes‐Ramos, C. M., Kuijjer, M. L., Platig, J., Sonawane, A. R., Fagny, M., Glass, K., & Quackenbush, J. F. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1847-x · Cited by 70 · see citing works

Diagnosing rare diseases after the exomePreprint

Frésard, L., & Montgomery, S. B. (2018) · Molecular Case Studies · doi:10.1101/mcs.a003392 · Cited by 61 · see citing works

Principled approach to the selection of the embedding dimension of networks

Gu, W., Tandon, A., Ahn, Y., & Radicchi, F. (2021) · Nature Communications · doi:10.1038/s41467-021-23795-5 · Cited by 60 · see citing works

Mapping the perturbome network of cellular perturbations

Caldera, M., Müller, F., Kaltenbrunner, I., Licciardello, M. P., Lardeau, C.-H., Kubicek, S., & Menche, J. (2019) · Nature Communications · doi:10.1038/s41467-019-13058-9 · Cited by 56 · see citing works

Semantic Similarity in the Gene Ontology

Pesquita, C. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3743-1_12 · Cited by 54 · see citing works

Map of synthetic rescue interactions for the Fanconi anemia DNA repair pathway identifies USP48

Velimezi, G., Robinson-Garcia, L., Muñoz‐Martínez, F., Wiegant, W. W., da Silva, J. F., Owusu, M., Moder, M., Wiedner, M., Rosenthal, S. B., Fisch, K. M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04649-z · Cited by 54 · see citing works

Towards a data-integrated cell

Malod‐Dognin, N., Petschnigg, J., Windels, S. F. L., Povh, J., Hemingway, H., Ketteler, R., & Pržulj, N. (2019) · Nature Communications · doi:10.1038/s41467-019-08797-8 · Cited by 54 · see citing works

HGCS: an online tool for prioritizing disease-causing gene variants by biological distance

Itan, Y., Mazel, M., Mazel, B., Abhyankar, A., Nitschké, P., Quintana‐Murci, L., Boisson‐Dupuis, S., Boisson, B., Abel, L., Zhang, S., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-256 · Cited by 49 · see citing works

HACE1 deficiency leads to structural and functional neurodevelopmental defects

Nagy, V., Hollstein, R., Pai, T., Herde, M. K., Buphamalai, P., Möseneder, P., Lenartowicz, E. M., Kavirayani, A. M., Korenke, G. C., Kozieradzki, I., et al. (2019) · Neurology Genetics · doi:10.1212/nxg.0000000000000330 · Cited by 36 · see citing works

Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes

Basha, O., Argov, C. M., Artzy, R., Zoabi, Y., Hekselman, I., Alfandari, L., Caspi, V. C., & Yeger‐Lotem, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa034 · Cited by 35 · see citing works

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box ModelPreprint

Wang, T., & Lin, Q. (2021) · arXiv (Cornell University) · Cited by 26 · see citing works

Unveiling new disease, pathway, and gene associations via multi-scale neural network

Gaudelet, T., Malod-Dognin, N., Sánchez-Valle, J., Pancaldi, V., Valencia, A., & Pržulj, N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231059 · Cited by 25 · see citing works

Expression of Survivin in pancreatic cancer and its correlation to expression of Bcl-2

Qiao, J.-G. (2004) · World Journal of Gastroenterology · doi:10.3748/wjg.v10.i18.2759 · Cited by 24 · see citing works

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine

Zanin, M., Chorbev, I., Stres, B., Stalidzāns, E., Vera, J., Tieri, P., Castiglione, F., Groen, D., Zheng, H., Baumbach, J., et al. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx160 · Cited by 19 · see citing works

Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields

Robinson, S., Nevalainen, J., Pinna, G., Campalans, A., Radicella, J. P., & Guyon, L. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx244 · Cited by 16 · see citing works

menchelab/MultiOme: v0.1.0

Buphamalai, P. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5562923 · Cited by 1 · see citing works

AI-powered therapeutic target discovery

Pun, F. W., Ozerov, I. V., & Zhavoronkov, A. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.06.010 · Cited by 341 · 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

From Homeostasis to Neuroinflammation: Insights into Cellular and Molecular Interactions and Network Dynamics

Müller, L., Benedetto, S. D., & Müller, V. (2025) · Cells · doi:10.3390/cells14010054 · Cited by 98 · see citing works

Advancements in risk stratification and management strategies in primary cardiovascular prevention.

Barkas, F., Şener, Y., Golforoush, P., Kheirkhah, A., Rodríguez-Sánchez, E., Novák, J., Apellániz-Ruiz, M., Akyea, R., Bianconi, V., Ceasovschih, A., et al. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.117579 · Cited by 56 · see citing works

An evaluation tool for backbone extraction techniques in weighted complex networks

Yassin, A., Haidar, A., Cherifi, H., Seba, H., & Togni, O. (2023) · Scientific Reports · doi:10.1038/s41598-023-42076-3 · Cited by 32 · see citing works

Next-generation sequencing and bioinformatics in rare movement disorders

Zech, M., & Winkelmann, J. (2024) · Nature Reviews Neurology · doi:10.1038/s41582-023-00909-9 · Cited by 32 · see citing works

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification

Guo, H., Lv, X., Li, Y., & Li, M. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad013 · Cited by 29 · see citing works

Neuroimmune dynamics and brain aging: mechanisms and consequences

Müller, L., Benedetto, S. D., & Müller, V. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1715045 · Cited by 22 · see citing works

Applications of graph theory in studying protein structure, dynamics, and interactions

Zhou, Z., & Hu, G. (2023) · Journal of Mathematical Chemistry · doi:10.1007/s10910-023-01511-6 · Cited by 22 · see citing works

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Guthrie, J., Bal, S. K., Lombardo, S. D., Müller, F., Sin, C., Hütter, C. V. R., Menche, J., & Boztuǧ, K. (2023) · Science Advances · doi:10.1126/sciadv.adg6375 · Cited by 21 · see citing works

Resources and tools for rare disease variant interpretation

Licata, L., Via, A., Turina, P., Babbi, G., Benevenuta, S., Carta, C., Casadio, R., Cicconardi, A., Facchiano, A. M., Fariselli, P., et al. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1169109 · Cited by 21 · see citing works

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery

Trisciuzzi, D., Villoutreix, B. O., Siragusa, L., Baroni, M., Cruciani, G., & Nicolotti, O. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2218641 · Cited by 19 · see citing works

Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes

Núñez‐Carpintero, I., Rigau, M., Bosio, M., O’Connor, E., Spendiff, S., Azuma, Y., Töpf, A., Thompson, R., Hoen, P. A. C. ’., Chamova, T., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45099-0 · Cited by 17 · see citing works

TCM network pharmacology: new perspective integrating network target with artificial intelligence and multi-modal multi-omics technologies

Wang, Z., Zhang, T., Wang, B., & Li, S. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60986-1 · Cited by 18 · see citing works

Omics and rare diseases: challenges, applications, and future perspectives

Braconi, D., Nadwa, H., Bernardini, G., & Santucci, A. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2468300 · Cited by 15 · see citing works

Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships

Wang, Y., Sun, Z., He, Q., Li, J., Ni, M., & Yang, M. (2022) · Patterns · doi:10.1016/j.patter.2022.100651 · Cited by 13 · see citing works

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

Lanzer, J. D., Valdeolivas, A., Pepin, M. E., Hund, H., Backs, J., Frey, N., Friederich, H., Schultz, J., Sáez-Rodríguez, J., & Levinson, R. T. (2023) · BMC Medicine · doi:10.1186/s12916-023-02922-7 · Cited by 11 · see citing works

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

Schlüter, A., Vélez-Santamaría, V., Verdura, E., Rodríguez‐Palmero, A., Ruíz, M., Fourcade, S., Planas‐Serra, L., Launay, N., Guilera, C., Martínez, J. J., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01214-2 · Cited by 10 · see citing works

Dimensionality reduction methods for extracting functional networks from large‐scale CRISPR screens

Hassan, A. Z., Ward, H. N., Rahman, M., Billmann, M., Lee, Y., & Myers, C. L. (2023) · Molecular Systems Biology · doi:10.15252/msb.202311657 · Cited by 7 · see citing works

Integrating traditional medicine with network pharmacology for Alzheimer's treatment

Kadiri, S. K., & Tiwari, P. K. (2025) · Health Sciences Review · doi:10.1016/j.hsr.2025.100223 · Cited by 9 · see citing works

Network-based drug repurposing for psychiatric disorders using single-cell genomics

Gupta, C., Kalafut, N. C., Clarke, D., Choi, J. J., Arachchilage, K. H., Khullar, S., Xia, Y., Zhou, X., Dursun, C., Gerstein, M., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101003 · Cited by 10 · see citing works

Computational strategies for cross-species knowledge transfer

Yuan, H., Mancuso, C. A., Johnson, K. A., Braasch, I., & Krishnan, A. (2025) · Nature Methods · doi:10.1038/s41592-025-02931-9 · Cited by 11 · see citing works

Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions

Pakpahan, I., Sihombing, M., Liu, H., Wang, M., Su, Z., & Fang, M. (2026) · GigaScience · doi:10.1093/gigascience/giag004 · Cited by 10 · see citing works

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

Naghdibadi, M., Momeni, M., Yavari, P., Gholaminejad, A., & Roointan, A. (2023) · Kidney & Blood Pressure Research · doi:10.1159/000529861 · Cited by 9 · see citing works

Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

Faviez, C., Chen, X., Garcelon, N., Zaidan, M., Billot, K., Petzold, F., Faour, H., Douillet, M., Rozet, J., Cormier‐Daire, V., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02538-8 · Cited by 10 · see citing works

Mapping human disease-associated enzymes into Reactome allows characterization of disease groups and their interactions

Savojardo, C., Baldazzi, D., Babbi, G., Martelli, P. L., & Casadio, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-22818-5 · Cited by 9 · see citing works

The metabolic organ connectome: A novel approach to measure allostatic load during health-to-disease transition

Geist, B. K., Guthrie, J., Kertész, H., Kiefer, F. W., Kulterer, O. C., NAKUZ, T. S., Nics, L., Patronas, E., Rendeiro, A. F., Vraka, C., et al. (2025) · Med · doi:10.1016/j.medj.2025.100881 · Cited by 8 · see citing works

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models

Granata, I., Manipur, I., Giordano, M., Maddalena, L., & Guarracino, M. R. (2022) · Scientific Data · doi:10.1038/s41597-022-01702-x · Cited by 8 · see citing works

MedAI-SciTS: Enhancing Interdisciplinary Collaboration between AI Researchers and Medical Experts

Cao, C., Wu, Y., Fang, X. Z., Liang, Z., Mamykina, L., Sbaffi, L., & Xu, X. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713926 · Cited by 6 · see citing works

The rare-to-common disease journey: a winding road to new therapies

Reinholdt, L. G., Chesler, E. J., Pera, M. F., & Rosenthal, N. A. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.05.003 · Cited by 8 · see citing works

Prioritization of oligogenic variant combinations in whole exomes

Gravel, B., Renaux, A., Papadimitriou, S., Smits, G., Nowé, A., & Lenaerts, T. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae184 · Cited by 6 · see citing works

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease

Lombardo, S. D., Wangsaputra, I., Menche, J., & Stevens, A. (2022) · Genes · doi:10.3390/genes13050764 · Cited by 7 · see citing works

HNRNPA2B1 as a potential therapeutic target for thymic epithelial tumor recurrence: An integrative network analysis

Zhou, Z., Lu, Y., Gu, Z., Sun, Q., Fang, W., Yan, W., Ku, X., Liang, Z. T., & Hu, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106665 · Cited by 6 · see citing works

Network modeling helps to tackle the complexity of drug–disease systems

Recanatini, M., & Menestrina, L. (2023) · WIREs Mechanisms of Disease · doi:10.1002/wsbm.1607 · Cited by 6 · see citing works

The regulatory network architecture of cardiometabolic diseases

Schmidt, H. H. H. W., & Menche, J. (2022) · Nature Genetics · doi:10.1038/s41588-021-00994-w · Cited by 5 · see citing works

Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis

Pagano-Márquez, R., Caballero, J., Martı́nez-Poveda, B., Quesada, A. R., Rojano, E., Seoane, P., Ranea, J. A. G., & Medina, M. Á. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac220 · Cited by 5 · see citing works

Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics

Netsey, E. K., Naandam, S. M., Asante, J., Kuukua, A. E., Yadem, A. C., Owusu, G., Shaffer, J. G., Srivastav, S. K., Doumbia, S., Owusu‐Dabo, E., et al. (2025) · JMIR Bioinformatics and Biotechnology · doi:10.2196/73637 · Cited by 7 · see citing works

Hunting for a coronary artery disease diagnosis in asymptomatic patients with diabetes mellitus: if, how and when

Santilli, F., Blaha, M. J., Ricci, F., & Simeone, P. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02966-4 · Cited by 5 · see citing works

Knowledge Graphs: Semantics, Machine Learning, and Languages

Acosta Maribel, P. S. (2023) · Studies on the semantic web · doi:10.3233/ssw56 · Cited by 4 · see citing works

Correlation analysis of combined layers in multiplex networks based on entropy

Wang, D., Tian, F., & Wei, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276344 · Cited by 4 · see citing works

A network view of human immune system and virus-human interaction

Tang, K. L., Tang, J. Y., Zeng, J., Shen, W., Zou, M., Zhang, C., Sun, Q., Ye, X., Li, C., Sun, C., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.997851 · Cited by 4 · see citing works

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Lucena-Padros, H., Bravo‐Gil, N., Tous, C., Rojano, E., Seoane, P., Fernández, R. M., Ranea, J. A. G., Antiñolo, G., & Borrego, S. (2024) · Biomolecules · doi:10.3390/biom14020164 · Cited by 4 · see citing works

A network-based map of the chemical exposome connects molecular interactions to public health

Lombardo, S. D., Hütter, C. V. R., Unterlass, M. M., & Menche, J. (2026) · Nature Communications · doi:10.1038/s41467-026-72402-y · Cited by 3 · see citing works

Economics, externalities and rare disease

Runge, C. F., Campbell, J. D., Runge, C. P., & Kartha, R. V. (2024) · Rare · doi:10.1016/j.rare.2024.100036 · Cited by 3 · see citing works

Red-Wine Gene Networks Linked to Exceptional Longevity in Humans

Lacayo, P., Martignoni, A., Park, K. S., Castro, C., & Murakami, S. (2025) · Biomolecules · doi:10.3390/biom15101414 · Cited by 3 · see citing works

PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation

Yousefi, B., Firoozbakht, F., Melograna, F., Schwikowski, B., & Steen, K. V. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1274637 · Cited by 3 · see citing works

Disentangling multiple sclerosis phenotypes through Mendelian disorders: A network approach

Bellucci, G., Buscarinu, M. C., Reniè, R., Rinaldi, V., Bigi, R., Mechelli, R., Romano, S., Salvetti, M., & Ristori, G. (2024) · Multiple Sclerosis Journal · doi:10.1177/13524585241227119 · Cited by 3 · see citing works

In-silico identification of therapeutic targets in pancreatic ductal adenocarcinoma using WGCNA and Trader

Yavari, P., Roointan, A., Naghdibadi, M., & Masoudi‐Sobhanzadeh, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-74252-4 · Cited by 3 · see citing works

Network-based framework for studying etiology and phenotype diversity in primary ciliopathiesPreprint

Aarts, E., Trip, D. S. L., Neatu, R., Martin, C. G., Riley, B., Kraus, A., Green, A., Al‐Hamed, M. H., Armstrong, R. E., Sayer, J. A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631887 · Cited by 3 · see citing works

Association of Preoperative Parameters on Intraoperative Indicators in Myocardial Revascularization Surgery: Insights from a Targeted Complex Network Model

Bertolucci, V., Ninomiya, A. F., Souza, J. P., Barbosa, F. F. P., Nonose, N., de Carvalho, L. M., Scariot, P. P. M., dos Reis, I. G. M., & Messias, L. H. D. (2024) · Surgeries · doi:10.3390/surgeries6010001 · Cited by 2 · see citing works

Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records

Shehhi, A. A., Alblooshi, H., Fadul, R., Tumzghi, N., Tenaiji, A. A., Harbi, M. A., & Al-Jasmi, F. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-13879-3 · Cited by 3 · see citing works

AI-Based Facial Phenotyping Supports a Shared Molecular Axis in PACS1-, PACS2-, and WDR37-Related Syndromes

Rincón, J., Gil‐Salvador, M., Lucia‐Campos, C., Acero, L., Trujillano, L., Arnedo, M., Pamplona, P., Casas, A. A., Puisac, B., Ramos, F. J., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26167964 · Cited by 2 · see citing works

Identifying MicroRNA-Disease Associations by Multiple Kernel Learning with Higher-Order Interactions

Ju, X., Qu, Y., Guo, Z., Meng, X., Zheng, Y., & Wu, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356946 · Cited by 2 · see citing works

Using Multiplex Networks to Understand Physical–Digital Structural Consistency for Social Fintech Sustainable Development

Zhang, G., Wu, J., Jeon, G., Wang, P., & Tan, M. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3546982 · Cited by 2 · see citing works

Ontology-guided clustering enables proteomic analysis of rare pediatric disorders

Itang, E. C. M., Albrecht, V., Schebesta, A.-S., Thielert, M., Lanz, A.-L., Danhauser, K., Jin, J., Prell, T., Strobel, S., Klein, C., et al. (2025) · EMBO Molecular Medicine · doi:10.1038/s44321-025-00253-z · Cited by 2 · see citing works

A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction

Natarajan, S., Kundapuram, N., Bhaskar, N., Policharla, S. S., & Das, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-04428-z · Cited by 2 · see citing works

Towards key scientific questions in the diagnosis and treatment of rare diseases: Summary from the 297th Meeting of the Shuangqing Forum

Zhu, C., Hong, W., Wang, L., Ding, L., & Zhang, X. (2022) · 动物学研究 · doi:10.24272/j.issn.2095-8137.2022.068 · Cited by 2 · see citing works

Network-based drug repurposing for psychiatric disorders using single-cell genomicsPreprint

Gupta, C., Kalafut, N. C., Clarke, D., Choi, J. J., Arachchilage, K. H., Khullar, S., Xia, Y., Zhou, X., Gerstein, M., & Wang, D. (2024) · medRxiv · doi:10.1101/2024.12.01.24318008 · Cited by 2 · see citing works

Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights from Predictive Modeling and Analytics (Preprint)Preprint

Netsey, E. K., Naandam, S. M., Asante, J., Kuukua, A. E., Yadem, A. C., Owusu, G., Shaffer, J. G., Srivastav, S. K., Doumbia, S., Owusu‐Dabo, E., et al. (2025) · doi:10.2196/preprints.73637 · Cited by 2 · see citing works

SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases

Namba, S., Li, C., Otani, N. Y., & Yamanishi, Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf039 · Cited by 1 · see citing works

A statistical physics approach to integrating multi-omics data for disease-module detection

Wang, X., Ryu, M. H., Cho, M. H., Castaldi, P. J., Hersh, C. P., Silverman, E. K., & Liu, Y. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101183 · Cited by 1 · see citing works

MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics

Wang, L., Xiang, J., Guo, Z., Zeng, K.-X., & Li, M. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00755-x · Cited by 2 · see citing works

Network-based framework for studying etiology and phenotypic diversity in primary ciliopathies

Aarts, E., Trip, D. S. L., Neatu, R., Martin, C. G., Riley, B., Kraus, A., Green, A., Al-Hamed, M. H., Armstrong, R. E., Sayer, J. A., et al. (2026) · Genome biology · doi:10.1186/s13059-025-03899-7 · Cited by 1 · see citing works

Virtual Reality Based Access to Knowledge Graphs for History Research

Becker, J., Botsch, M., Cimiano, P., Derksen, M., Elahi, M. F., Maier, A., Maile, M., Pätzold, I., Penningroth, J., Reglin, B., et al. (2023) · Studies on the semantic web · doi:10.3233/ssw230011 · Cited by 1 · see citing works

A many‐objective optimization‐based local tensor factorization model for skin cancer detection

Zhao, H., Wen, J., Yang, J., Cai, X., & Liu, C. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7947 · Cited by 1 · see citing works

Enhancing the Accuracy of Network Medicine Through Understanding the Impact of Sample Size in Gene Co-expression Networks

Aguirre‐Plans, J., Chen, B., Ghiassian, S. D., Jones, A., Akmaev, V. R., Saleh, A., Gysi, D. M., & Barabási, A.-Ĺ. (2025) · Network and Systems Medicine · doi:10.14293/nsm.25.1.0002 · Cited by 1 · see citing works

Therapeutic Target Identification in Pancreatic Ductal Adenocarcinoma: A Comprehensive In-Silico Study employing WGCNA and TraderPreprint

Yavari, P., Masoudi‐Sobhanzadeh, Y., & Roointan, A. (2023) · Research Square · doi:10.21203/rs.3.rs-2627227/v1 · Cited by 1 · see citing works

Red-wine gene networks associated with exceptional longevity in humansPreprint

Lacayo, P., Martignoni, A., Park, K. S., Castro, C., & Murakami, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.21.660865 · Cited by 1 · see citing works

Swarm-GestaltMatcher: distributed Gestalt learning through Swarm Learning to enhance facial phenotyping for rare genetic syndromesPreprint

Bandyopadhyay, A., Hennocq, Q., Lienhard, O., Hsieh, T., Zaiter, A., Breton, L., Warnat‐Herresthal, S., Schultze, H., Aschenbrenner, A. C., Cormier‐Daire, V., et al. (2026) · medRxiv · doi:10.64898/2026.01.02.26343337 · Cited by 1 · see citing works

Unifying the genetic landscape of common and rare diseases via latent neighborhoodsPreprint

Trip, D. S. L., Aarts, E., Magnusson, G., Gankin, D., Barrio‐Hernandez, I., & Beltrão, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.27.701945 · Cited by 1 · see citing works

Rare diseases: a pivotal moment towards equity, inclusion, discovery and sustainable solutions

Sayer, J. A., Mallett, A. J., & Soliman, N. A. (2026) · Journal of Rare Diseases · doi:10.1007/s44162-026-00165-7 · Cited by 1 · see citing works

Cartographs enable interpretation of complex network visualizations

(2022) · Nature Computational Science · doi:10.1038/s43588-022-00203-6

The power of network visualizations

Menche, J. (2022) · doi:10.14293/s2199-rexpo22014.v1

Computational-based strategies to deal with liver injuries induced by the repurposed drugs against SARS-CoV-2

Bakhtiyari, N., Parvizpour, S., Masoudi‐Sobhanzadeh, Y., & Masoudi‐Nejad, A. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101336

Estimation of multiple networks with common structures in heterogeneous subgroups

Qin, X., Hu, J., Ma, S., & Wu, M. (2024) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2024.105298

Enhancing the accuracy of Network Medicine through understanding the impact of sample size in gene co-expression networks

Aguirre‐Plans, J., Chen, B., Ghiassian, S. D., Jones, A., Akmaev, V. R., Saleh, A., Gysi, D. M., & Barabási, A.-Ĺ. (2024) · doi:10.58647/rexpo.24000042.v1

A multilayer network approach elucidates time- and tissue-specific developmental and aging processesPreprint

Lombardo, S. D., Rendeiro, A. F., & Menche, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.02.651926

A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs

Lee, K., Kim, D., Kim, I., Lee, J.-H., Ha, D., Lim, S., Kim, E., Im, S., Shin, K., & Kim, S. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf070 · Cited by 1 · see citing works

Identification of Ageing-related Hub Genes in Humans, Mouse and Rat Based on Bioinformatics Analysis

Chaurasiya, M., & Prasad, G. (2025) · Annals of Neurosciences · doi:10.1177/09727531251381065

Additional file 2 of Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

Faviez, C., Chen, X., Garcelon, N., Zaidan, M., Billot, K., Petzold, F., Faour, H., Douillet, M., Rozet, J., Cormier‐Daire, V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25902422

Additional file 1 of Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

Faviez, C., Chen, X., Garcelon, N., Zaidan, M., Billot, K., Petzold, F., Faour, H., Douillet, M., Rozet, J., Cormier‐Daire, V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25902330

Additional file 1 of ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

Schlüter, A., Vélez-Santamaría, V., Verdura, E., Rodríguez‐Palmero, A., Ruíz, M., Fourcade, S., Planas‐Serra, L., Launay, N., Guilera, C., Martínez, J. J., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24104450

Construction of Multiple Dynamic Differential Protein Interaction Networks for Identifying Disease Genes

Mao, Q., Huang, Z., Wen, X., Hu, J., Ju, X., & Meng, X. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356092

Mini-Galaxy: Rethinking Complex Human Diseases Through the Lens of Systems Biology and Multilayered AI Network Perspectives

Correia, C., Ung, C. Y., Zhang, Z., Blissenbach, E., Zhu, S., Billadeau, D. D., Loh, Y., & Li, H. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27073161

Modeling the causal mechanism between genotypes and phenotypes using large-scale biobank data and context-specific regulatory networks

Li, W., Zeng, W.-W., & Wong, W.-H. (2026) · Cybernetics and Intelligence · doi:10.26599/cai.2024.9390003

Network-based machine learning methods to model variability across individuals

Yousefi, B. (2023) · theses.fr (ABES) · doi:10.70675/b7843ad7z3ae0z43caz9e47zf95945cd3b83

The Emerging Role of Artificial Intelligence in Advancing Molecular Precision Medicine for Cancer

Cerchia, C., & Lavecchia, A. (2026) · Cancer drug discovery and develogment/Cancer drug discovery and development · doi:10.1007/978-3-032-24071-2_13

Nine quick tips for graph theory in systems biology

Pagliarini, R., & Chicco, D. (2026) · Discover Networks · doi:10.1007/s44354-026-00032-3

Nadir Hastalıklarda Entegre Multi-Omik ve Yapay Zekâ: Moleküler Mekanizmalardan Hassas Tanı ve Dijital Fenotiplemeye Geçiş

Bayramcı, N. S. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1358.c5471

Nadir Hastalıklarda Bitki Metabolitlerinin Translasyonel Potansiyeli: Moleküler Mekanizmalardan Ağ Tabanlı Hassas Tedavi Stratejilerine

Bayramcı, N. S., & Selvi, B. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1358.c5473

Cancer Signaling Pathways and Cancer Biology

Nair, A., Prasad, A. P., Anand, A. V., Shyl, D., & Yesupatham, S. K. (2026) · Studies in Rhythm Engineering · doi:10.1007/978-981-95-8938-8_7

Mechanism-First Phytochemical Therapeutics: Integrating Target Prediction, Pathway Enrichment, Phenotypic Effects, and Disease Network Biology

Mwangi, J., Wanjiru, G., Kiprop, S., & Atieno, C. (2025) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/toaoxanf3i

Interactome modules underlie shared spatiotemporal patterns in specific psychiatric and neurodegenerative disordersPreprint

Karunakaran, K. B., Ganesh, S., Mahadevan, J., Purushottam, M., Balakrishnan, N., Amemori, K., Jain, S., & Viswanath, B. (2026) · Research Square · doi:10.21203/rs.3.rs-10583889/v1

Predicting gene compensation in disease with graph embedding techniques

García-Criado, F., Pérez-García, J., Rojano, E., Seoane-Zonjic, P., & Ranea, J. A. (2026) · Artif. Intell. Medicine · doi:10.1016/j.artmed.2026.103505

Dynamic perturbation analysis: a modular rewiring framework for decoding cancer stage progression and identifying therapeutic vulnerabilities

Yang, B., Li, M., Liu, X., Jin, X., & Hu, G. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag572

A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing

Muhiuddin, G., Samanta, S., Aljohani, A. F., & Alkhaibari, A. M. (2023) · Mathematics · doi:10.3390/math11143166 · Cited by 8 · see citing works

Biological Network Approaches and Applications in Rare Disease Studies

Zhang, P., & Itan, Y. (2019) · Genes · doi:10.3390/genes10100797 · Cited by 45 · see citing works

Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

Rao, A., Saipradeep, V. G., Joseph, T., Kotte, S., Sivadasan, N., & Srinivasan, R. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0372-8 · Cited by 57 · see citing works

Virtual BSC RS/BSC Life Session : Multi-omics data integration methods to study rare genetic diseases

Baudot, A. (2021) · UPCommons institutional repository (Universitat Politècnica de Catalunya)

Towards Identification of Human Disease Phenotype-Genotype Association via a Network-Module Based Method

Jiang, J., Dress, A., & Chen, M. (2009) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2009.88