NetSig: network-based discovery from cancer genomes

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works

Cancer Genome Landscapes

Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863 · see citing works

Comprehensive molecular profiling of lung adenocarcinoma

The Cancer Genome Atlas Research Network (2014) · Nature · doi:10.1038/nature13385 · Cited by 5,732 · see citing works

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

Mermel, C. H., Schumacher, S. E., Hill, B., Meyerson, M. M., Beroukhim, R., & Getz, G. A. (2011) · Genome biology · doi:10.1186/gb-2011-12-4-r41 · Cited by 3,469 · see citing works

Discovery and saturation analysis of cancer genes across 21 tumour types

Lawrence, M. S., Stojanov, P., Mermel, C. H., Robinson, J. T., Garraway, L. A., Golub, T. R., Meyerson, M., Gabriel, S. B., Lander, E. S., & Getz, G. (2014) · Nature · doi:10.1038/nature12912 · Cited by 3,096 · see citing works

Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing

Frampton, G. M., Fichtenholtz, A., Otto, G. A., Wang, K., Downing, S. R., He, J., Schnall-Levin, M., White, J., Sanford, E. M., An, P., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2696 · Cited by 2,157 · see citing works

Absolute quantification of somatic DNA alterations in human cancer

Carter, S. L., Cibulskis, K., Helman, E., McKenna, A., Shen, H., Zack, T. I., Laird, P. W., Onofrio, R. C., Winckler, W., Weir, B. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2203 · Cited by 2,003 · see citing works

Mapping the Hallmarks of Lung Adenocarcinoma with Massively Parallel Sequencing

Imielinski, M., Berger, A. H., Hammerman, P. S., Hernandez, B., Pugh, T. J., Hodis, E., Cho, J., Suh, J., Capelletti, M., Sivachenko, A., et al. (2012) · Cell · doi:10.1016/j.cell.2012.08.029 · Cited by 1,789 · see citing works

Lessons from the Cancer Genome

Garraway, L. A., & Lander, E. S. (2013) · Cell · doi:10.1016/j.cell.2013.03.002 · Cited by 1,322 · see citing works

Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas

Cancer Genome Atlas Research Network, Campbell, J. D., Alexandrov, A., Kim, J., Wala, J., Berger, A. H., Pedamallu, C. S., Shukla, S. A., Guo, G., Brooks, A. N., et al. (2016) · Nature Genetics · doi:10.1038/ng.3564 · Cited by 1,200 · see citing works

Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing

Lohr, J. G., Stojanov, P., Lawrence, M. S., Auclair, D., Chapuy, B., Sougnez, C. L., Cruz‐Gordillo, P., Knoechel, B., Asmann, Y. W., Slager, S. L., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121343109 · Cited by 977 · see citing works

A human phenome-interactome network of protein complexes implicated in genetic disorders

Lage, K., Karlberg, E., Størling, Z. M., Olason, P., Pedersen, A., Rigina, O., Hinsby, A., Tümer, Z., Pociot, F., Tommerup, N., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1295 · Cited by 973 · see citing works

Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Leiserson, M. D. M., Vandin, F., Wu, H.-T., Dobson, J. R., Eldridge, J. V., Thomas, J. L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M. D., et al. (2014) · Nature Genetics · doi:10.1038/ng.3168 · Cited by 908 · see citing works

Network-based stratification of tumor mutations

Hofree, M., Shen, J. P., Carter, H. K., Gross, A. M., & Ideker, T. E. (2013) · Nature Methods · doi:10.1038/nmeth.2651 · Cited by 726 · see citing works

Mutual exclusivity analysis identifies oncogenic network modulesPreprint

Ciriello, G., Cerami, E. G., Sander, C., & Schultz, N. D. (2011) · Genome Research · doi:10.1101/gr.125567.111 · Cited by 676 · see citing works

A scored human protein–protein interaction network to catalyze genomic interpretation

Li, T., Wernersson, R., Hansen, R. B., Horn, H., Mercer, J., Slodkowicz, G., Workman, C. T., Rigina, O., Rapacki, K., Stærfeldt, H., et al. (2016) · Nature Methods · doi:10.1038/nmeth.4083 · Cited by 630 · see citing works

Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study

Roychowdhury, S., Iyer, M. K., Robinson, D. R., Lonigro, R. J., Wu, Y.-M., Cao, X., Sundaram, S. K., Sam, L. T., Balbin, O. A., Quist, M. J., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3003161 · Cited by 629 · see citing works

Somatic ERCC2 Mutations Correlate with Cisplatin Sensitivity in Muscle-Invasive Urothelial Carcinoma

Allen, E. M. V., Mouw, K. W., Kim, P., Iyer, G., Wagle, N., Al‐Ahmadie, H. A., Zhu, C., Ostrovnaya, I., Kryukov, G. V., O’Connor, K. W., et al. (2014) · Cancer Discovery · doi:10.1158/2159-8290.cd-14-0623 · Cited by 621 · see citing works

Integrative Genomic Approaches Identify IKBKE as a Breast Cancer Oncogene

Boehm, J. S., Zhao, J. J., Yao, J., Kim, S. Y., Firestein, R., Dunn, I. F., Sjostrom, S. K., Garraway, L. A., Weremowicz, S., Richardson, A. L., et al. (2007) · Cell · doi:10.1016/j.cell.2007.03.052 · Cited by 608 · see citing works

High-Throughput Detection of Actionable Genomic Alterations in Clinical Tumor Samples by Targeted, Massively Parallel Sequencing

Wagle, N., Berger, M. F., Davis, M. J., Blumenstiel, B. S., DeFelice, M. T., Pochanard, P., Ducar, M. D., Hummelen, P. V., MacConaill, L. E., Hahn, W. C., et al. (2011) · Cancer Discovery · doi:10.1158/2159-8290.cd-11-0184 · Cited by 526 · see citing works

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Rossin, E. J., Lage, K., Raychaudhuri, S., Xavier, R. J., Tatar, D., Benita, Y., Constortium, I. I. B. D. G., Cotsapas, C., & Daly, M. J. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001273 · Cited by 438 · see citing works

Algorithms for Detecting Significantly Mutated Pathways in Cancer

Vandin, F., Upfal, E., & Raphael, B. J. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0265 · Cited by 427 · see citing works

Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics

Khurana, E., Fu, Y., Colonna, V., Mu, X. J., Kang, H. M., Lappalainen, T., Sboner, A., Lochovsky, L., Chen, J., Harmanci, A. O., et al. (2013) · Science · doi:10.1126/science.1235587 · Cited by 359 · see citing works

Functional impact bias reveals cancer drivers

González-Pérez, A., & López-Bigas, N. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks743 · Cited by 328 · see citing works

Pathway and network analysis of cancer genomes

Creixell, P., Linding, R., Reimand, J., Bader, G. D., Haider, S., Wu, G., Ouellette, B. F. F., Boutros, P. C., Stein, L. D., Shibata, T., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3440 · Cited by 326 · see citing works

Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

Marbach, D., Lamparter, D., Quon, G., Kellis, M., Kutalik, Z., & Bergmann, S. (2016) · Nature Methods · doi:10.1038/nmeth.3799 · Cited by 326 · see citing works

A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes

Lage, K., Hansen, N. T., Karlberg, E. O., Eklund, A. C., Roque, F. S., Donahoe, P. K., Szallasi, Z., Jensen, T. S., & Brunak, S. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810772105 · Cited by 292 · see citing works

Combinatorial patterns of somatic gene mutations in cancer

Yeang, C.-H., McCormick, F., & Levine, A. (2008) · The FASEB Journal · doi:10.1096/fj.08-108985 · Cited by 251 · see citing works

High-throughput Phenotyping of Lung Cancer Somatic Mutations

Berger, A. H., Brooks, A. N., Wu, X., Shrestha, Y., Chouinard, C. R., Piccioni, F., Bagul, M., Kamburov, A., Imielinski, M. B., Hogstrom, L. J., et al. (2016) · Cancer Cell · doi:10.1016/j.ccell.2016.06.022 · Cited by 207 · see citing works

Functional Copy-Number Alterations in Cancer

Taylor, B. S., Barretina, J., Socci, N. D., DeCarolis, P., Ladanyi, M., Meyerson, M., Singer, S., & Sander, C. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003179 · Cited by 170 · see citing works

Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles

Kim, E., Ilić, N., Shrestha, Y., Zou, L., Kamburov, A., Zhu, C., Yang, X., Lubonja, R., Tran, N. H., Nguyen, C., et al. (2016) · Cancer Discovery · doi:10.1158/2159-8290.cd-16-0160 · Cited by 168 · see citing works

Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations

Babur, Ö., Gönen, M., Aksoy, B. A., Schultz, N. D., Ciriello, G., Sander, C., & Demir, E. (2015) · Genome biology · doi:10.1186/s13059-015-0612-6 · Cited by 161 · see citing works

Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors

Miller, C. A., Settle, S. H., Sulman, E. P., Aldape, K., & Milosavljevic, A. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-34 · Cited by 126 · see citing works

An ecosystem of cancer cell line factories to support a cancer dependency map

Boehm, J. S., & Golub, T. R. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3967 · Cited by 75 · see citing works

In vivo multiplexed interrogation of amplified genes identifies GAB2 as an ovarian cancer oncogene

Dunn, G. P., Cheung, H. W., Agarwalla, P. K., Thomas, S. R., Zektser, Y. A., Karst, A. M., Boehm, J. S., Weir, B. A., Berlin, A. M., Zou, L., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311909111 · Cited by 43 · see citing works

Inherited and de novo genetic risk for autism impacts shared networks

Ruzzo, E. K., Pérez-Cano, L., Jung, J.-Y., Wang, L.-K., Kashef-Haghighi, D., Hartl, C., Singh, C., Xu, J., Hoekstra, J. N., Leventhal, O., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.015 · Cited by 457 · see citing works

Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders

Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452 · see citing works

HumanNet v2: human gene networks for disease research

Hwang, S., Kim, C. Y., Yang, S., Kim, E., Hart, T., Marcotte, E. M., & Lee, I. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1126 · Cited by 210 · see citing works

Cancer gene mutation frequencies for the U.S. population

Mendiratta, G., Ke, E., Aziz, M., Liarakos, D., Tong, M. M., & Stites, E. C. (2021) · Nature Communications · doi:10.1038/s41467-021-26213-y · Cited by 208 · see citing works

An integrative ENCODE resource for cancer genomics

Zhang, J., Lee, D., Dhiman, V., Jiang, P., Xu, J., McGillivray, P., Yang, H., Liu, J., Meyerson, W., Clarke, D., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14743-w · Cited by 172 · 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

Pathway and network analysis of more than 2500 whole cancer genomes

Reyna, M. A., Haan, D., Paczkowska, M., Verbeke, L. P. C., Vázquez, M., Kahraman, A., Pulido-Tamayo, S., Barenboim, J., Wadi, L., Dhingra, P., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14367-0 · Cited by 110 · see citing works

Tau Pathology Drives Dementia Risk-Associated Gene Networks toward Chronic Inflammatory States and Immunosuppression

Rexach, J. E., Polioudakis, D., Yin, A., Swarup, V., Chang, T. S., Nguyen, T., Sarkar, A., Chen, L., Huang, J., Lin, L.-C., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108398 · Cited by 94 · see citing works

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

Öztürk, K., Dow, M. T., Carlin, D. E., Bejar, R. L., & Carter, H. K. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.06.016 · Cited by 90 · see citing works

Decoding disease: from genomes to networks to phenotypes

Wong, A. K., Sealfon, R. S. G., Theesfeld, C. L., & Troyanskaya, O. G. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00389-x · Cited by 87 · see citing works

Coexpression network architecture reveals the brain-wide and multiregional basis of disease susceptibility

Hartl, C., Ramaswami, G., Pembroke, W. G., Muller, S., Pintacuda, G., Saha, A., Parsana, P., Battle, A. J., Lage, K., & Geschwind, D. H. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00887-5 · Cited by 82 · see citing works

Interpretation of cancer mutations using a multiscale map of protein systems

Zheng, F., Kelly, M. R., Ramms, D. J., Heintschel, M. L., Tao, K., Tutuncuoglu, B., Lee, J. J., Ono, K., Foussard, H., Chen, M., et al. (2021) · Science · doi:10.1126/science.abf3067 · Cited by 81 · see citing works

Systems biology based drug repositioning for development of cancer therapy

Turanlı, B., Altay, Ö., Borén, J., Türkez, H., Nielsen, J., Uhlén, M., Arğa, K. Y., & Mardinoğlu, A. (2019) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2019.09.020 · Cited by 80 · see citing works

Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s disease

Morabito, S., Miyoshi, E., Michael, N., & Swarup, V. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa182 · Cited by 74 · see citing works

A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity

Swaney, D. L., Ramms, D. J., Wang, Z., Park, J., Goto, Y., Soucheray, M., Bhola, N. E., Kim, K., Zheng, F., Zeng, Y., et al. (2021) · Science · doi:10.1126/science.abf2911 · Cited by 71 · see citing works

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

Lyu, J., Li, J. J., Su, J., Peng, F., Chen, Y. E., Ge, X., & Li, W. (2020) · Science Advances · doi:10.1126/sciadv.aba6784 · Cited by 60 · see citing works

Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders

Hernandez, L. M., Kim, M., Hoftman, G. D., Haney, J. R., de la Torre-Ubieta, L., Paşaniuc, B., & Gandal, M. J. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.005 · Cited by 57 · see citing works

Amplification-free SERS analysis of DNA mutation in cancer cells with single-base sensitivity

Wu, L., Garrido‐Maestu, A., Guerreiro, J. R. L., Carvalho, S., Abalde‐Cela, S., Prado, M., & Diéguez, L. (2019) · Nanoscale · doi:10.1039/c9nr00501c · Cited by 50 · see citing works

Explainable Multilayer Graph Neural Network for cancer gene prediction

Chatzianastasis, M., Vazirgiannis, M., & Zhang, Z. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad643 · Cited by 49 · see citing works

NetCore: a network propagation approach using node coreness

Barel, G., & Herwig, R. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa639 · Cited by 46 · see citing works

Classifying tumors by supervised network propagation

Zhang, W., Ma, J., & Ideker, T. E. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty247 · Cited by 43 · see citing works

Network-based cancer precision medicine: A new emerging paradigm

Tan, A., Huang, H., Zhang, P., & Li, S. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.05.015 · Cited by 41 · see citing works

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

Zhang, Z., & Wei, X. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.005 · Cited by 41 · see citing works

Prediction of cancer driver genes through network-based moment propagation of mutation scores

Gumpinger, A., Lage, K., Horn, H., & Borgwardt, K. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa452 · Cited by 38 · see citing works

Comprehensive evaluation of computational methods for predicting cancer driver genes

Shi, X., Teng, H., Shi, L., Bi, W., Wei, W., Mao, F., & Sun, Z. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab548 · Cited by 38 · see citing works

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Zhang, L., Fan, S., Vera, J., & Lai, X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.037 · Cited by 35 · see citing works

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

Wang, H., Wang, T., Zhao, X., Wu, H., You, M., Sun, Z. S., & Mao, F. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa084 · Cited by 35 · see citing works

CBNA: A control theory based method for identifying coding and non-coding cancer drivers

Pham, V., Liu, L., Bracken, C. P., Goodall, G. J., Long, Q., Li, J., & Le, T. D. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007538 · Cited by 31 · see citing works

Pathway perturbations in signaling networks: Linking genotype to phenotype

Li, Y., McGrail, D. J., Latysheva, N. S., Yi, S. S., Babu, M. M., & Sahni, N. (2018) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2018.05.001 · Cited by 30 · see citing works

Gene Targeting in Disease Networks

Weighill, D. A., Guebila, M. B., Glass, K., Platig, J., Yeh, J. J., & Quackenbush, J. F. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.649942 · Cited by 30 · see citing works

Machine learning methods to model multicellular complexity and tissue specificity

Sealfon, R. S. G., Wong, A. K., & Troyanskaya, O. G. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00339-3 · Cited by 30 · see citing works

Protein-Protein interactions uncover candidate ‘core genes’ within omnigenic disease networks

Ratnakumar, A., Weinhold, N., Mar, J. C., & Riaz, N. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008903 · Cited by 29 · see citing works

A network-based deep learning methodology for stratification of tumor mutations

Liu, C., Han, Z. Y., Zhang, Z., Nussinov, R., & Cheng, F. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1099 · Cited by 23 · see citing works

Typing tumors using pathways selected by somatic evolution

Wang, S., Ma, J., Zhang, W., Shen, J. P., Huang, J. K.-L., Peng, J., & Ideker, T. E. (2018) · Nature Communications · doi:10.1038/s41467-018-06464-y · Cited by 23 · see citing works

Analysis of physicochemical properties of protein–protein interaction modulators suggests stronger alignment with the “rule of five”

Truong, J. Q., George, A., & Holien, J. K. (2021) · RSC Medicinal Chemistry · doi:10.1039/d1md00213a · Cited by 23 · see citing works

Pathway and network analysis of more than 2,500 whole cancer genomesPreprint

Reyna, M. A., Haan, D., Paczkowska, M., Verbeke, L. P. C., Vázquez, M., Kahraman, A., Pulido-Tamayo, S., Barenboim, J., Wadi, L., Dhingra, P., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/385294 · Cited by 19 · see citing works

Genomic and transcriptional alterations in first-line chemotherapy exert a potentially unfavorable influence on subsequent immunotherapy in NSCLC

He, Y., Chen, L., Zhao, L., Dang, S., Liu, G., Sasada, S., Ma, P. C., van Zandwijk, N., Rosell, R., Popper, H., et al. (2021) · Theranostics · doi:10.7150/thno.58039 · Cited by 18 · see citing works

Mutational Profile Evaluates Response and Survival to First‐Line Chemotherapy in Lung Cancer

He, Y., Song, L., Wang, H., Chen, P., Liu, Y., Sun, H., Li, X., Dang, S., Liu, G., Liu, X., et al. (2020) · Advanced Science · doi:10.1002/advs.202003263 · Cited by 18 · see citing works

Gene relevance based on multiple evidences in complex networks

Nanni, N. D., Gnocchi, M., Moscatelli, M., Milanesi, L., & Mosca, E. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz652 · Cited by 17 · see citing works

Framework for quality assessment of whole genome cancer sequences

Whalley, J. P., Buchhalter, I., Rheinbay, E., Raine, K. M., Kleinheinz, K., Stobbe, M. D., Werner, J., Beltrán, S., Gut, M., Hübschmann, D., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18688-y · Cited by 17 · see citing works

pDriver : a novel method for unravelling personalized coding and miRNA cancer drivers

Pham, V., Liu, L., Bracken, C. P., Nguyen, T., Goodall, G. J., Li, J., & Le, T. D. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab262 · Cited by 16 · see citing works

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Li, Y., Burgman, B., Khatri, I. S., Pentaparthi, S. R., Su, Z., McGrail, D. J., Li, Y., Wu, E., Eckhardt, S. G., Sahni, N., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1015 · Cited by 16 · see citing works

NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks

Reyna, M. A., Chitra, U., Elyanow, R., & Raphael, B. J. (2021) · Journal of Computational Biology · doi:10.1089/cmb.2020.0435 · Cited by 16 · see citing works

Shiny GATOM: omics-based identification of regulated metabolic modules in atom transition networks

Emelianova, M., Gainullina, A., Poperechnyi, N., Loboda, A. A., Artyomov, M. N., & Sergushichev, A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac427 · Cited by 15 · see citing works

A new machine learning method for cancer mutation analysis

Habibi, M., & Taheri, G. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010332 · Cited by 15 · see citing works

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

Li, R., Jiang, S., Li, W., Hong, H., Zhao, C., Huang, X., Zhang, Z., Li, H., Chen, H., & Bo, X. (2019) · RNA Biology · doi:10.1080/15476286.2019.1607714 · Cited by 13 · see citing works

NIBNA: a network-based node importance approach for identifying breast cancer drivers

Chaudhary, M. S., Pham, V., & Le, T. D. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab145 · Cited by 13 · see citing works

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

Cutigi, J. F., Evangelista, A. F., & Simao, A. D. S. (2020) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s021972002050016x · Cited by 13 · see citing works

On the complexity of quantum link prediction in complex networks

Moutinho, J. P., Magano, D., & Coutinho, B. C. (2024) · Scientific Reports · doi:10.1038/s41598-023-49906-4 · Cited by 12 · see citing works

Identifying cancer driver genes using a two-stage random walk with restart on a gene interaction network

Meng, P., Wang, G., Guo, H., & Jiang, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106810 · Cited by 11 · see citing works

System and network biology-based computational approaches for drug repositioning

Pandita, V., Parihar, A., Parihar, D. S., Panda, S., Shanmugarajan, D., Kumari, L., & Badwaik, H. R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91172-6.00003-0 · Cited by 11 · see citing works

Combining Mutation and Gene Network Data in a Machine Learning Approach for False-Positive Cancer Driver Gene Discovery

Cutigi, J. F., Evangelista, R. F., Ramos, R. H., Ferreira, C. D. O. L., Evangelista, A. F., de Carvalho, A. C. P. L. F., & Simao, A. D. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65775-8_8 · Cited by 11 · see citing works

A graph convolutional neural network for gene expression data analysis with multiple gene networks

Hu, Y., Zhuang, Z., & Pan, W. (2021) · Statistics in Medicine · doi:10.1002/sim.9140 · Cited by 10 · see citing works

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Additional file 5 of Tailored graphical lasso for data integration in gene network reconstruction

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Additional file 2 of Tailored graphical lasso for data integration in gene network reconstruction

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Additional file 3 of Tailored graphical lasso for data integration in gene network reconstruction

Lingjærde, C., Lien, T. G., Borgan, Ø., Bergholtz, H., & Glad, I. K. (2021) · Figshare · doi:10.6084/m9.figshare.16821610

Additional file 4 of Tailored graphical lasso for data integration in gene network reconstruction

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