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Network medicine: a network-based approach to human disease
Open accessAbstract
Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
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“Few genes act in isolation but rather affect the function of other genes to influence a particular phenotype via in cellular networks or pathways (Barabasi et al., 2011; Geschwind and Konopka, 2009).”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Barabasi et al., 2011
In the citing paper
“Communities of co-expressed proteins can be linked to disease processes, and the most strongly correlated proteins, or “hubs,” within these co-expression modules are enriched in key drivers of disease pathogenesis5–10.”
In the citing paper
“Machine learning has been used to analyze high-throughput molecular interactomes to unravel genetic architecture perturbed in disease12,13 and help design therapies to target them14.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“This evaluation method aligns with the approach predominantly used in the literature12,14–16,18–20.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“TxGNN leverages an additional metric learning component for zero-shot prediction, capitalizing on the insight that diseases can share disease-associated genetic and genomic networks10,14 and, thus, medical knowledge of disease-associated networks can be transferred by the model from well-annotated diseases to other diseases to enhance predictions on diseases with limited treatment options (Fig. 2a”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“In protein interaction networks, for instance, mutations in interacting proteins often lead to similar diseases [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Further, essential genes are often found in hubs of a molecular network whereas non-essential genes (e.g., those associated with disease) are located on the periphery [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Given by the shared components and disease module hypotheses [13], cellular components associated with the same phenotype tend to cluster in the same network neighborhood [16].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Thus, the network parsimony principle dictates that shortest molecular paths between known disease-associated components tend to correlate with causal molecular pathways [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“First, interacting entities are typically more similar than non-interacting entities, as implicated by the local hypothesis [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“The organizing principles governing networks link network structure to molecular phenotypes, biological roles, disease, and health, thus providing the conceptual grounding that, we argue, can explain the successes (and limitations) of graph representation learning and inform future development of the field.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Because such processes are costly and time-consuming, in silico approaches have been adopted into the drug discovery pipeline.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Modern drug discovery requires elucidating a candidate drug’s chemical structure, identifying its drug targets, quantifying its efficacy and toxicity, and detecting its potential side effects [13, 14, 32, 164].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
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Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Self-citationStatistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Self-citationCommunity structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Self-citationNetwork motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Uncovering the overlapping community structure of complex networks in nature and society
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005) · Nature · doi:10.1038/nature03607 · Cited by 5,373
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
Self-citationThe Large-Scale Organization of Metabolic Networks
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972
Self-citation
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Items 901–1,000 of 1,013
Mellors, T. R., Withers, J. B., Ameli, A., Jones, A., Wang, M., Zhang, L., Sanchez, H. N., Santolini, M., do Valle, Í. F., Sebek, M., et al. (2020) · Network and Systems Medicine · doi:10.1089/nsm.2020.0007 · Cited by 42
Lee, D., Park, Y., & Kim, S. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa188 · Cited by 41
Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
Cohen, A. S., Cox, C. R., Le, T. P., Cowan, T., Masucci, M. D., Strauss, G. P., & Kirkpatrick, B. W. (2020) · Schizophrenia · doi:10.1038/s41537-020-00115-2 · Cited by 39
It’s in Our Blood: A Glimpse of Personalized Medicine
Lamb, J. R., Jennings, L. L., Guðmundsdóttir, V., Gudnason, V. G., & Emilsson, V. (2020) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2020.09.003 · Cited by 37
Network medicine in Cardiovascular Research
Lee, L. Y.-H., Pandey, A. K., Maron, B. A., & Loscalzo, J. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvaa321 · Cited by 42
Self-citationChen, Y.-W., Diamante, G., Ding, J., Nghiem, T. X., Yang, J., Ha, S. M., Cohn, P., Arneson, D. V., Blencowe, M., García, J., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104052 · Cited by 43
Lee, Y., Li, H., Li, J., Rebman, E., Achour, I., Regan, K., Gamazon, E. R., Chen, J. L., Yang, X. H., Cox, N. J., et al. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001519 · Cited by 42
Pooled screening for antiproliferative inhibitors of protein-protein interactions
Nim, S., Jeon, J., Corbi‐Verge, C., Seo, M., Ivarsson, Y., Moffat, J., Tarasova, N. I., & Kim, P. M. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2026 · Cited by 43
Proteomics insights into DNA damage response and translating this knowledge to clinical strategies
von Stechow, L., & Olsen, J. V. (2016) · PROTEOMICS · doi:10.1002/pmic.201600018 · Cited by 43
Chee, F. L. M., Beernaert, B., Griffith, B. G. C., Loftus, A. E. P., Kumar, Y., Wills, J. C., Lee, M. W., Valli, J., Wheeler, A. P., Armstrong, J. D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37021-x · Cited by 41
Review of Predicting Synergistic Drug Combinations
Pan, Y., Ren, H., Lan, L., Li, Y., & Huang, T. (2023) · Life · doi:10.3390/life13091878 · Cited by 40
Vafaii, H., Mandino, F., Desrosiers-Grégoire, G., O’Connor, D. A., Markicevic, M., Shen, X., Ge, X., Hermán, P., Hyder, F., Papademetris, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44363-z · Cited by 45
Network physics of attractive colloidal gels: Resilience, rigidity, and phase diagram
Nabizadeh, M., Nasirian, F., Li, X., Saraswat, Y. C., Waheibi, R. A., Hsiao, L. C., Bi, D., Ravandi, B., & Jamali, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2316394121 · Cited by 40
Making the right connections: Network biology and plant immune system dynamics
McCormack, M. E., Lopez, J. A., Crocker, T. H., & Mukhtar, M. S. (2015) · Current Plant Biology · doi:10.1016/j.cpb.2015.10.002 · Cited by 41
Network approaches to drug discovery
Chandra, N. R., & Padiadpu, J. (2012) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.741119 · Cited by 42
Connecting the Dots: Applications of Network Medicine in Pharmacology and Disease
Jacunski, A., & Tatonetti, N. P. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.168 · Cited by 43
MicroRNA target prediction using thermodynamic and sequence curves
Ghoshal, A., Shankar, R., Bagchi, S., Grama, A. Y., & Chaterji, S. (2015) · BMC Genomics · doi:10.1186/s12864-015-1933-2 · Cited by 41
Detecting similar binding pockets to enable systems polypharmacology
Duran‐Frigola, M., Siragusa, L., Ruppin, E., Barril, X., Cruciani, G., & Aloy, P. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005522 · Cited by 41
Palluzzi, F., Ferrari, R., Graziano, F., Novelli, V., Rossi, G., Galimberti, D., Rainero, I., Benussi, L., Nacmias, B., Bruni, A. C., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185797 · Cited by 38
Repositioning drugs by targeting network modules: a Parkinson’s disease case study
Yue, Z., Arora, I., Zhang, E. Y., Laufer, V. A., Bridges, S. L., & Chen, J. Y. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1889-0 · Cited by 40
Network Modules Uncover Mechanisms of Skeletal Muscle Dysfunction in COPD Patients
Tényi, Á., Cano, I., Marabita, F., Kiani, N., Kalko, S. G., Barreiro, E., de Atauri, P., Cascante, M., Gomez-Cabrero, D., & Roca, J. (2018) · Journal of Translational Medicine · doi:10.1186/s12967-018-1405-y · Cited by 40
A dynamic weighted TOPSIS method for identifying influential nodes in complex networks
Yang, P., Liu, X., & Xu, G. (2018) · Modern Physics Letters B · doi:10.1142/s0217984918502160 · Cited by 40
Pathway analysis using XGBoost classification in Biomedical Data
Dimitrakopoulos, G. Ν., Vrahatis, A. G., Plagianakos, V. P., & Sgarbas, K. (2018) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3200947.3201029 · Cited by 40
Propranolol selectively inhibits cervical cancer cell growth by suppressing the cGMP/PKG pathway
Gong, L., Lei, Y., Tan, X., Dong, Y., Luo, Z., Zhang, D., & Han, S. (2019) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2019.01.027 · Cited by 40
Novel protein pathways in development and progression of pulmonary sarcoidosis
Bhargava, M., Viken, K. J., Barkes, B. Q., Griffin, T. J., Gillespie, M. M., Jagtap, P. D., Sajulga, R., Peterson, E. J., Dincer, E. H., Li, L., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69281-8 · Cited by 42
Zhan, Z., Dai, Y.-C., Li, Q., & Lv, Y. (2020) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2020.116129 · Cited by 40
A straightforward edge centrality concept derived from generalizing degree and strength
Bröhl, T., & Lehnertz, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-08254-5 · Cited by 40
Graupera, I., Isus, L., Coll, M., Pose, E., Díaz, A., Vallverdú, J., Rubio‐Tomás, T., Martínez–Sánchez, C., Huelin, P., Llopis, M., et al. (2022) · JHEP Reports · doi:10.1016/j.jhepr.2022.100482 · Cited by 40
The common ground of genomics and systems biology
Conesa, A., & Mortazavi, A. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-s2-s1 · Cited by 40
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Balagué, N., Hristovski, R., Almarcha, M., García‐Retortillo, S., & Ivanov, P. C. (2022) · Sports Medicine - Open · doi:10.1186/s40798-022-00512-0 · Cited by 40
Lee, W.-Y., Park, K., Bak, S. B., Lee, S., Bae, S.-J., Kim, M., Park, S., Kim, C. O., Kim, J.-H., Kim, Y. W., et al. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.12.040 · Cited by 39
Chemical Complexity of Food and Implications for Therapeutics
Menichetti, G., Barabási, A.-Ĺ., & Loscalzo, J. (2025) · New England Journal of Medicine · doi:10.1056/nejmra2413243 · Cited by 42
Self-citationKuperstein, I., Grieco, L., Cohen, D., Thieffry, D., Zinovyev, A. Y., & Barillot, E. (2015) · Mutagenesis · doi:10.1093/mutage/geu078 · Cited by 38
Integration, visualization and analysis of human interactome
Pastrello, C., Pasini, E., Kotlyar, M., Otasek, D., Wong, S., Sangrar, W., Rahmati, S., & Jurišica, I. (2014) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2014.01.151 · Cited by 41
Future Translational Applications From the Contemporary Genomics Era
Fox, C. S., Hall, J. L., Arnett, D. K., Ashley, E. A., Delles, C., Engler, M. B., Freeman, M. W., Johnson, J. A., Lanfear, D. E., Liggett, S. B., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000211 · Cited by 39
Self-citationComputation as the Mechanistic Bridge Between Precision Medicine and Systems Therapeutics
Hansen, J., & Iyengar, R. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.199 · Cited by 40
Logical transformation of genome-scale metabolic models for gene level applications and analysis
Zhang, C., Ji, B., Mardinoğlu, A., Nielsen, J., & Hua, Q. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv134 · Cited by 38
Current advances in systems and integrative biology
Robinson, S. W., Fernandes, M., & Husi, H. (2014) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.08.007 · Cited by 40
Computational Disease Gene Prioritization: An Appraisal
Gill, N., Singh, S. N., & Aseri, T. C. (2014) · Journal of Computational Biology · doi:10.1089/cmb.2013.0158 · Cited by 41
Dynamical network model for age-related health deficits and mortality
Taneja, S., Mitnitski, A., Rockwood, K. J., & Rutenberg, A. D. (2016) · Physical review. E · doi:10.1103/physreve.93.022309 · Cited by 40
Jumping across biomedical contexts using compressive data fusion
Žitnik, M., & Zupan, B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw247 · Cited by 39
Bersanelli, M., Mosca, E., Remondini, D., Castellani, G., & Milanesi, L. (2016) · Scientific Reports · doi:10.1038/srep34841 · Cited by 40
Biomolecular Network-Based Synergistic Drug Combination Discovery
Li, X., Qin, G., Yang, Q., Chen, L., & Xie, L. (2016) · BioMed Research International · doi:10.1155/2016/8518945 · Cited by 39
Ni, J., Koyutürk, M., Tong, H., Haines, J. L., Xu, R., & Zhang, X. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1317-x · Cited by 39
Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers
Choi, J., Park, S., Yoon, Y., & Ahn, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx487 · Cited by 40
Inflammatory Bowel Disease: Complexity and Variability Need Integration
Fiocchi, C. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00075 · Cited by 39
Ferrari, R., Kia, D. A., Tomkins, J. E., Hardy, J., Wood, N. W., Lovering, R. C., Lewis, P. A., & Manzoni, C. (2018) · BMC Genomics · doi:10.1186/s12864-018-4804-9 · Cited by 39
A tetrameric protein scaffold as a nano-carrier of antitumor peptides for cancer therapy
Ma, B., Niu, F., Qu, X., He, W., Feng, C., Wang, S., Ouyang, Z., Yan, J., Wen, Y., Xu, D., et al. (2019) · Biomaterials · doi:10.1016/j.biomaterials.2019.03.004 · Cited by 40
Maiorino, E., Baek, S. H., Guo, F., Zhou, X., Kothari, P. H., Silverman, E. K., Barabási, A.-Ĺ., Weiss, S. T., Raby, B. A., & Sharma, A. (2020) · Nature Communications · doi:10.1038/s41467-020-14600-w · Cited by 41
Self-citationFrom genotype to phenotype: augmenting deep learning with networks and systems biology
Gazestani, V., & Lewis, N. E. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.04.001 · Cited by 38
Disease characterization using a partial correlation-based sample-specific network
Huang, Y., Chang, X., Zhang, Y., Chen, L., & Liu, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa062 · Cited by 38
The road towards data integration in human genomics: players, steps and interactions
Bernasconi, A., Canakoglu, A., Masseroli, M., & Ceri, S. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa080 · Cited by 39
OATargets: a knowledge base of genes associated with osteoarthritis joint damage in animals
Soul, J., Barter, M. J., Little, C. B., & Young, D. A. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-218344 · Cited by 39
Dai, Q., Chu, Y., Li, Z., Zhao, Y., Mao, X., Wang, Y., Xiong, Y., & Wei, D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104706 · Cited by 40
Stem Cell Differentiation into Cardiomyocytes: Current Methods and Emerging Approaches
Afjeh‐Dana, E., Naserzadeh, P., Moradi, E., Hosseini, N., Seifalian, A. M., & Ashtari, B. (2022) · Stem Cell Reviews and Reports · doi:10.1007/s12015-021-10280-1 · Cited by 39
Complexity and network dynamics in physiological adaptation: An integrated view
Baffy, G., & Loscalzo, J. (2014) · Physiology & Behavior · doi:10.1016/j.physbeh.2014.04.018 · Cited by 40
Self-citationGenetic and functional characterization of disease associations explains comorbidity
Rubio-Pérez, C., Güney, E., Aguilar, D., Piñero, J., Garcı́a-Garcı́a, J., Iadarola, B., Sanz, F., Fernández‐Fuentes, N., Furlong, L. I., & Oliva, B. (2017) · Scientific Reports · doi:10.1038/s41598-017-04939-4 · Cited by 41
Drug Repurposing Using Biological Networks
Somolinos-Simón, F. J., León, C., & Guerrero‐Aspizua, S. (2021) · Processes · doi:10.3390/pr9061057 · Cited by 37
Troubleshooting and deconvoluting label-free cell phenotypic assays in drug discovery
Ye, F. (2013) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/j.vascn.2013.01.004 · Cited by 38
Comparative analysis of a novel disease phenotype network based on clinical manifestations
Chen, Y., Zhang, X., Zhang, G., & Xu, R. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.09.007 · Cited by 38
Yates, C. M., & Sternberg, M. J. E. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.01.026 · Cited by 38
Doig, T. N., Hume, D. A., Theocharidis, T., Goodlad, J. R., Gregory, C. D., & Freeman, T. C. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-469 · Cited by 37
Chen, Y., & Xu, R. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s9 · Cited by 38
Making sense out of massive data by going beyond differential expression
Schmid, P. R., Palmer, N., Kohane, I. S., & Berger, B. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1118792109 · Cited by 39
Sharma, A., Gulbahce, N., Pevzner, S. J., Menche, J., Ladenvall, C., Folkersen, L., Eriksson, P. O., Orho‐Melander, M., & Barabási, A.-Ĺ. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.024851 · Cited by 36
Self-citationDivekar, R. D., Patel, N. N., Jin, J., Hagan, J., Rank, M. A., Lal, D., Kita, H., & O’Brien, E. K. (2015) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2015.06.018 · Cited by 38
Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD
Chu, J., Hersh, C. P., Castaldi, P. J., Cho, M. H., Raby, B. A., Laird, N. M., Bowler, R. P., Rennard, S. I., Loscalzo, J., Quackenbush, J. F., et al. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-78 · Cited by 38
Self-citationLal, D., Rounds, A. D., Rank, M. A., & Divekar, R. D. (2015) · International Forum of Allergy & Rhinology · doi:10.1002/alr.21502 · Cited by 38
Rouwette, T., Sondermann, J. R., Avenali, L., Gómez‐Varela, D., & Schmidt, M. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.058966 · Cited by 40
Jin, N., Wu, H., Miao, Z., Huang, Y., Hu, Y., Bi, X., Wu, D., Qian, K., Wang, L., Wang, C., et al. (2015) · Scientific Reports · doi:10.1038/srep11566 · Cited by 38
Klimek, P., Aichberger, S., & Stefan Thurner, A. (2016) · Scientific Reports · doi:10.1038/srep39658 · Cited by 39
Bae, J. M., Choo, J. Y., Kim, K., & Park, K. (2016) · Modern Rheumatology · doi:10.1080/14397595.2016.1211229 · Cited by 39
Wang, X., Yu, H., Xu, J., Zhang, S.-Z., Yu, D., Liu, M., & Wang, L. (2017) · Scientific Reports · doi:10.1038/srep45690 · Cited by 38
Na, K. J., & Choi, H. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.194217 · Cited by 38
Clark, A., Alomair, B., Bushnell, L. G., & Poovendran, R. (2017) · IEEE Control Systems · doi:10.1109/mcs.2017.2743518 · Cited by 37
Zachariou, M., Minadakis, G., Oulas, A., Afxenti, S., & Spyrou, G. M. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.03.009 · Cited by 38
Proteome-based systems biology in chronic pain
Gómez‐Varela, D., Barry, A. M., & Schmidt, M. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.04.004 · Cited by 39
Chang, X., Lima, L. D. A., Liu, Y., Li, J., Li, Q. S., Abou‐Sleiman, P. M., & Hakonarson, H. H. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00434 · Cited by 36
Ata, S. K., Ou-Yang, L., Fang, Y., Kwoh, C.-K., Wu, M., & Li, X. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0662-y · Cited by 38
The latent geometry of the human protein interaction network
Alanis‐Lobato, G., Mier, P., & Andrade-Navarro, M. A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty206 · Cited by 41
Interactions of antithrombotic herbal medicines with Western cardiovascular drugs
Zuo, H., Linghu, K., Wang, Y., Liu, K., Gao, Y., Yu, H., Yang, F. Q., & Yuanjia, H. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.104963 · Cited by 38
Parallel Murine and Human Plaque Proteomics Reveals Pathways of Plaque Rupture
Vaisar, T., Hu, J. H., Airhart, N., Fox, K., Heinecke, J. W., Nicosia, R. F., Kohler, T. R., Potter, Z. E., Simon, G. M., Dix, M. M., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.317295 · Cited by 39
The identification of influential nodes based on structure similarity
Zhao, J., Song, Y., Liu, F., & Deng, Y. (2020) · Connection Science · doi:10.1080/09540091.2020.1806203 · Cited by 38
Wang, X., Wu, M. H., Lai, X., Zheng, J., Hu, M., Li, Y., & Li, S. (2020) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2020/2974268 · Cited by 38
Kim, Y., Park, J.-H., & Cho, Y. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23137411 · Cited by 38
Goldman, A. W., Burmeister, Y., Cesnulevicius, K., Herbert, M. R., Kane, M., Lescheid, D., McCaffrey, T. A., Schultz, M., Seilheimer, B., Smit, A., et al. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2015.00225 · Cited by 41
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Zou, J., Ji, P., Zhao, Y., Li, L., Wei, Y.-Q., Chen, Y., & Yang, S. (2012) · Molecular BioSystems · doi:10.1039/c2mb25267h · Cited by 38
Hori, H., Sasayama, D., Teraishi, T., Yamamoto, N., Nakamura, S., Ota, M., Hattori, K., Kim, Y., Higuchi, T., & Kunugi, H. (2016) · Scientific Reports · doi:10.1038/srep18776 · Cited by 38
Zhang, H., Liang, Y., Han, S., Peng, C., & Li, Y. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20061284 · Cited by 38
Jia, N., Xia, Z., Li, Y., Yu, X. M., Wu, X., Li, Y., Su, R., Wang, M., Chen, R., & Liu, J. (2024) · Communications Earth & Environment · doi:10.1038/s43247-024-01915-5 · Cited by 37
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Soyer, O. S., & O’Malley, M. A. (2013) · BioEssays · doi:10.1002/bies.201300029 · Cited by 42
Theofilatos, K., Pavlopoulou, N., Papasavvas, C., Likothanassis, S. D., Dimitrakopoulos, C. M., Georgopoulos, E. F., Moschopoulos, C. N., & Mavroudi, S. P. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.12.012 · Cited by 37
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Li, C., Shang, D., Wang, Y., Li, J., Han, J., Wang, S., Yao, Q., Wang, Y., Zhang, Y., Zhang, C., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047326 · Cited by 36
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Piovesan, D., Giollo, M., Ferrari, C., & Tosatto, S. C. E. (2015) · Amino Acids · doi:10.1007/s00726-015-2049-3 · Cited by 37
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Chen, L., Li, X., Sheng, Q. Z., Peng, W.-C., Bennett, J. W., Hu, H., & Huang, N. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2561278 · Cited by 37
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Liu, W., Wu, A., Pellegrini, M. A., & Wang, X. (2015) · Scientific Reports · doi:10.1038/srep14344 · Cited by 37
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Zhang, X.-F., Ou-Yang, L., Zhao, X., & Yan, H. (2016) · Scientific Reports · doi:10.1038/srep34112 · Cited by 37
Le, D., & Pham, V.-H. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0437-x · Cited by 37
Uusimaa, J., Kaarteenaho, R. L., Paakkola, T., Tuominen, H. J., Karjalainen, M. K., Nadaf, J., Varilo, T., Uusi-Mäkelä, M. I. E., Suo-Palosaari, M. H., Pietilä, I., et al. (2018) · Acta Neuropathologica · doi:10.1007/s00401-018-1817-z · Cited by 37
Zhao, X., & Liu, Z. (2019) · Genes · doi:10.3390/genes10020143 · Cited by 37
Metadata
- Authors
- Barabási, A.-L., Gulbahce, N., & Loscalzo, J.
- Affiliations
- Northeastern University (US)
- Brigham and Women's Hospital (US)
- Venue
- Nature Reviews Genetics
- Published
- 2010-12-17
- Volume
- 12
- Issue
- 1
- Pages
- 56-68
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3140052
- OpenAlex
- W2062533676
- Cited by
- 5,034
- References
- 140
- Citations by year
- 2026: 421 · 2025: 322 · 2024: 296 · 2023: 299 · 2022: 346 · 2021: 390 · 2020: 384 · 2019: 356 · 2018: 383 · 2017: 326 · 2016: 311 · 2015: 324 · 2014: 309 · 2013: 272 · 2012: 217
- FWCI
- 15.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (HL81587, HL 70819, U54HL070819, P01HL081587, R21HL089734, R01HL061795, HL-48743, P01HL048743, HL-061795, R37HL061795)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- network medicine, molecular networks, disease-associated genes, disease-associated mutations, therapeutic target, biomarkers, disease modules, complex diseases, genome-wide association study, full genome sequencing
- MeSH terms
- Disease, Humans, Gene Expression Profiling, Databases, Genetic, Gene Regulatory Networks, Metabolic Networks and Pathways, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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