BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Gligorijević V, Pržulj N. Methods for biological data integration: perspectives and challenges. J R Soc Interface 2015;12:20150571. [PMID: 26490630 DOI: 10.1098/rsif.2015.0571] [Cited by in Crossref: 114] [Cited by in F6Publishing: 97] [Article Influence: 19.0] [Reference Citation Analysis]
Number Citing Articles
1 Alfatemi A, Peng H, Rong W, Zhang B, Cai H. Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold. BMC Med Inform Decis Mak 2022;22. [DOI: 10.1186/s12911-022-01938-y] [Reference Citation Analysis]
2 Robin V, Bodein A, Scott-boyer M, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022;9:962799. [DOI: 10.3389/fmolb.2022.962799] [Reference Citation Analysis]
3 Dinarvand M, Koch FC, Al Mouiee D, Vuong K, Vijayan A, Tanzim AF, Azad AKM, Penesyan A, Castaño-Rodríguez N, Vafaee F. dRNASb: a systems biology approach to decipher dynamics of host-pathogen interactions using temporal dual RNA-seq data. Microb Genom 2022;8. [PMID: 36136078 DOI: 10.1099/mgen.0.000862] [Reference Citation Analysis]
4 Hassan Zada MS, Yuan B, Khan WA, Anjum A, Reiff-Marganiec S, Saleem R. A unified graph model based on molecular data binning for disease subtyping. J Biomed Inform 2022;:104187. [PMID: 36055637 DOI: 10.1016/j.jbi.2022.104187] [Reference Citation Analysis]
5 Lobato-delgado B, Priego-torres B, Sanchez-morillo D. Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis. Cancers 2022;14:3215. [DOI: 10.3390/cancers14133215] [Reference Citation Analysis]
6 Crawford J, Christensen BC, Chikina M, Greene CS. Widespread redundancy in -omics profiles of cancer mutation states. Genome Biol 2022;23:137. [PMID: 35761387 DOI: 10.1186/s13059-022-02705-y] [Reference Citation Analysis]
7 Gliozzo J, Mesiti M, Notaro M, Petrini A, Patak A, Puertas-Gallardo A, Paccanaro A, Valentini G, Casiraghi E. Heterogeneous data integration methods for patient similarity networks. Brief Bioinform 2022:bbac207. [PMID: 35679533 DOI: 10.1093/bib/bbac207] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Ma L, Shao Z, Li L, Huang J, Wang S, Lin Q, Li J, Gong M, Nandi AK. Heuristics and metaheuristics for biological network alignment: A review. Neurocomputing 2022;491:426-41. [DOI: 10.1016/j.neucom.2021.08.156] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Hesami M, Alizadeh M, Jones AMP, Torkamaneh D. Machine learning: its challenges and opportunities in plant system biology. Appl Microbiol Biotechnol 2022. [PMID: 35575915 DOI: 10.1007/s00253-022-11963-6] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
10 Keyvanpour MR, Haddadi F, Mehrmolaei S. DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Computational Biology and Chemistry 2022. [DOI: 10.1016/j.compbiolchem.2022.107707] [Reference Citation Analysis]
11 Madrid-márquez L, Rubio-escudero C, Pontes B, González-pérez A, Riquelme JC, Sáez ME. MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation. Applied Sciences 2022;12:3987. [DOI: 10.3390/app12083987] [Reference Citation Analysis]
12 Iqbal MA, Reyer H, Oster M, Hadlich F, Trakooljul N, Perdomo-sabogal A, Schmucker S, Stefanski V, Roth C, Camarinha Silva A, Huber K, Sommerfeld V, Rodehutscord M, Wimmers K, Ponsuksili S. Multi-Omics Reveals Different Strategies in the Immune and Metabolic Systems of High-Yielding Strains of Laying Hens. Front Genet 2022;13:858232. [DOI: 10.3389/fgene.2022.858232] [Reference Citation Analysis]
13 Pavlopoulou A, Asfa S, Gioukakis E, Mavragani IV, Nikitaki Z, Takan I, Pouget JP, Harrison L, Georgakilas AG. In Silico Investigation of the Biological Implications of Complex DNA Damage with Emphasis in Cancer Radiotherapy through a Systems Biology Approach. Molecules 2021;26:7602. [PMID: 34946681 DOI: 10.3390/molecules26247602] [Reference Citation Analysis]
14 Fischer N, Efferth T, Niki E, Capanoglu E, Sieniawska E. The impact of “omics” technologies for grapevine (Vitis vinifera) research. JBR 2021;11:567-81. [DOI: 10.3233/jbr-200633] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Arani AA, Sehhati M, Tabatabaiefar MA. Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization. Sci Rep 2021;11:23747. [PMID: 34887492 DOI: 10.1038/s41598-021-03230-x] [Reference Citation Analysis]
16 Sadeghi S, Lu J, Ngom A. A Network-Based Drug Repurposing Method Via Non-Negative Matrix Factorization. Bioinformatics 2021:btab826. [PMID: 34875000 DOI: 10.1093/bioinformatics/btab826] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
17 Qiu Y, Ching WK, Zou Q. Matrix factorization-based data fusion for the prediction of RNA-binding proteins and alternative splicing event associations during epithelial-mesenchymal transition. Brief Bioinform 2021;22:bbab332. [PMID: 34410342 DOI: 10.1093/bib/bbab332] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
18 Naseri A, Sharghi M, Hasheminejad SMH. Enhancing gene regulatory networks inference through hub-based data integration. Comput Biol Chem 2021;95:107589. [PMID: 34673384 DOI: 10.1016/j.compbiolchem.2021.107589] [Reference Citation Analysis]
19 Pourrezaei S, Shadabi S, Gheidishahran M, Rahimiforoushani A, Akhbari M, Tavakoli M, Safavi M, Madihi M, Norouzi M. Molecular epidemiology and phylogenetic analysis of human T-lymphotropic virus type 1 in the tax gene and it association with adult t-cell leukemia/lymphoma disorders. Iran J Microbiol 2021;13:509-17. [PMID: 34557280 DOI: 10.18502/ijm.v13i4.6976] [Reference Citation Analysis]
20 Hulot A, Laloë D, Jaffrézic F. A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data. BMC Bioinformatics 2021;22:392. [PMID: 34348641 DOI: 10.1186/s12859-021-04303-4] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
21 Sadeghi SS, Keyvanpour MR. Computational Drug Repurposing: Classification of the Research Opportunities and Challenges. Curr Comput Aided Drug Des 2020;16:354-64. [PMID: 31198115 DOI: 10.2174/1573409915666190613113822] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
22 Cavigelli S, Leips J, Jenny Xiang QY, Lemke D, Konow N. Next Steps in Integrative Biology: Mapping Interactive Processes Across Levels of Biological Organization. Integr Comp Biol 2021:icab161. [PMID: 34259855 DOI: 10.1093/icb/icab161] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
23 Arani AA, Sehhati M, Tabatabaiefar MA. Genetic variant effect prediction by supervised nonnegative matrix tri-factorization. Mol Omics 2021. [PMID: 34164638 DOI: 10.1039/d1mo00038a] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Dornhaus A, Smith B, Hristova K, Buckley LB. How can we fully realize the potential of mathematical and biological models to reintegrate biology? Integr Comp Biol 2021:icab142. [PMID: 34160617 DOI: 10.1093/icb/icab142] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 García Del Valle EP, Lagunes García G, Prieto Santamaría L, Zanin M, Menasalvas Ruiz E, Rodríguez-González A. DisMaNET: A network-based tool to cross map disease vocabularies. Comput Methods Programs Biomed 2021;207:106233. [PMID: 34157517 DOI: 10.1016/j.cmpb.2021.106233] [Reference Citation Analysis]
26 Pavel A, Del Giudice G, Federico A, Di Lieto A, Kinaret PAS, Serra A, Greco D. Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment. Brief Bioinform 2021;22:1430-41. [PMID: 33569598 DOI: 10.1093/bib/bbaa417] [Cited by in Crossref: 2] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
27 Yang Y, Li G, Xie Y, Wang L, Lagler TM, Yang Y, Liu J, Qian L, Li Y. iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement. Brief Bioinform 2021:bbab122. [PMID: 33839756 DOI: 10.1093/bib/bbab122] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
28 Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021;49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
29 Tayefi M, Ngo P, Chomutare T, Dalianis H, Salvi E, Budrionis A, Godtliebsen F. Challenges and opportunities beyond structured data in analysis of electronic health records. WIREs Comp Stat 2021;13. [DOI: 10.1002/wics.1549] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
30 Reyna MA, Chitra U, Elyanow R, Raphael BJ. NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks. J Comput Biol 2021;28:469-84. [PMID: 33400606 DOI: 10.1089/cmb.2020.0435] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
31 Cantini L, Zakeri P, Hernandez C, Naldi A, Thieffry D, Remy E, Baudot A. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat Commun 2021;12:124. [PMID: 33402734 DOI: 10.1038/s41467-020-20430-7] [Cited by in Crossref: 9] [Cited by in F6Publishing: 21] [Article Influence: 9.0] [Reference Citation Analysis]
32 Irshad O, Ghani Khan MU. Formalization and Semantic Integration of Heterogeneous Omics Annotations for Exploratory Searches. CBIO 2021;16:162-78. [DOI: 10.2174/1574893615666200127122818] [Reference Citation Analysis]
33 Rodosthenous T, Shahrezaei V, Evangelou M. Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study. Bioinformatics 2020;36:4616-25. [PMID: 32437529 DOI: 10.1093/bioinformatics/btaa530] [Cited by in Crossref: 2] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
34 [DOI: 10.1109/icdm50108.2020.00179] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
35 Yu G, Wang Y, Wang J, Domeniconi C, Guo M, Zhang X. Attributed heterogeneous network fusion via collaborative matrix tri-factorization. Information Fusion 2020;63:153-65. [DOI: 10.1016/j.inffus.2020.06.012] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
36 Mohammadi E, Benfeitas R, Turkez H, Boren J, Nielsen J, Uhlen M, Mardinoglu A. Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning. Cancers (Basel) 2020;12:E2694. [PMID: 32967266 DOI: 10.3390/cancers12092694] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
37 Bigan E, Sasidharan Nair S, Lejeune FX, Fragnaud H, Parmentier F, Mégret L, Verny M, Aaronson J, Rosinski J, Neri C. Genetic cooperativity in multi-layer networks implicates cell survival and senescence in the striatum of Huntington's disease mice synchronous to symptoms. Bioinformatics 2020;36:186-96. [PMID: 31228193 DOI: 10.1093/bioinformatics/btz514] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
38 Forcato M, Romano O, Bicciato S. Computational methods for the integrative analysis of single-cell data. Brief Bioinform 2021;22:20-9. [PMID: 32363378 DOI: 10.1093/bib/bbaa042] [Cited by in Crossref: 11] [Cited by in F6Publishing: 18] [Article Influence: 5.5] [Reference Citation Analysis]
39 Seyed Tabib NS, Madgwick M, Sudhakar P, Verstockt B, Korcsmaros T, Vermeire S. Big data in IBD: big progress for clinical practice. Gut 2020;69:1520-32. [PMID: 32111636 DOI: 10.1136/gutjnl-2019-320065] [Cited by in Crossref: 58] [Cited by in F6Publishing: 53] [Article Influence: 29.0] [Reference Citation Analysis]
40 Randhawa V, Pathania S. Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge. Brief Funct Genomics 2020;19:364-76. [PMID: 32678894 DOI: 10.1093/bfgp/elaa015] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
41 Hammoud Z, Kramer F. Multilayer networks: aspects, implementations, and application in biomedicine. Big Data Anal 2020;5. [DOI: 10.1186/s41044-020-00046-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
42 Leal LG, David A, Jarvelin MR, Sebert S, Männikkö M, Karhunen V, Seaby E, Hoggart C, Sternberg MJE. Identification of disease-associated loci using machine learning for genotype and network data integration. Bioinformatics 2019;35:5182-90. [PMID: 31070705 DOI: 10.1093/bioinformatics/btz310] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
43 Canakoglu A, Bernasconi A, Colombo A, Masseroli M, Ceri S. GenoSurf: metadata driven semantic search system for integrated genomic datasets. Database (Oxford) 2019;2019:baz132. [PMID: 31820804 DOI: 10.1093/database/baz132] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
44 Koo DCE, Bonneau R. Towards region-specific propagation of protein functions. Bioinformatics 2019;35:1737-44. [PMID: 30304483 DOI: 10.1093/bioinformatics/bty834] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
45 Jenkins K, Mateeva T, Szabó I, Melnik A, Picotti P, Csikász-Nagy A, Rosta E. Combining data integration and molecular dynamics for target identification in α-Synuclein-aggregating neurodegenerative diseases: Structural insights on Synaptojanin-1 (Synj1). Comput Struct Biotechnol J 2020;18:1032-42. [PMID: 32419904 DOI: 10.1016/j.csbj.2020.04.010] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
46 Wekesa JS, Luan Y, Meng J. Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis. J Comput Biol 2021;28:1-18. [PMID: 32302512 DOI: 10.1089/cmb.2019.0120] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
47 Nguyen ND, Wang D. Multiview learning for understanding functional multiomics. PLoS Comput Biol 2020;16:e1007677. [PMID: 32240163 DOI: 10.1371/journal.pcbi.1007677] [Cited by in Crossref: 27] [Cited by in F6Publishing: 29] [Article Influence: 13.5] [Reference Citation Analysis]
48 Zhu Y, Li X. Privacy-preserving k-means clustering with local synchronization in peer-to-peer networks. Peer-to-Peer Netw Appl 2020;13:2272-84. [DOI: 10.1007/s12083-020-00881-x] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
49 Oerton E, Roberts I, Lewis PSH, Guilliams T, Bender A. Understanding and predicting disease relationships through similarity fusion. Bioinformatics 2019;35:1213-20. [PMID: 30169824 DOI: 10.1093/bioinformatics/bty754] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
50 Saqi M, Lysenko A, Guo YK, Tsunoda T, Auffray C. Navigating the disease landscape: knowledge representations for contextualizing molecular signatures. Brief Bioinform 2019;20:609-23. [PMID: 29684165 DOI: 10.1093/bib/bby025] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
51 Irshad O, Khan MUG. Integration and Querying of Heterogeneous Omics Semantic Annotations for Biomedical and Biomolecular Knowledge Discovery. CBIO 2020;15:41-58. [DOI: 10.2174/1574893614666190409112025] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
52 Wang Y, Yu G, Wang J, Fu G, Guo M, Domeniconi C. Weighted matrix factorization on multi-relational data for LncRNA-disease association prediction. Methods 2020;173:32-43. [DOI: 10.1016/j.ymeth.2019.06.015] [Cited by in Crossref: 11] [Cited by in F6Publishing: 16] [Article Influence: 5.5] [Reference Citation Analysis]
53 Konopka T, Smedley D. Incremental data integration for tracking genotype-disease associations. PLoS Comput Biol 2020;16:e1007586. [PMID: 31986132 DOI: 10.1371/journal.pcbi.1007586] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
54 Kiyanpour F, Abedi M, Gheisari Y. A systematic integrative approach reveals novel microRNAs in diabetic nephropathy. J Res Med Sci 2020;25:1. [PMID: 32055241 DOI: 10.4103/jrms.JRMS_289_19] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
55 Golriz Khatami S, Robinson C, Birkenbihl C, Domingo-Fernández D, Hoyt CT, Hofmann-Apitius M. Challenges of Integrative Disease Modeling in Alzheimer's Disease. Front Mol Biosci 2019;6:158. [PMID: 31993440 DOI: 10.3389/fmolb.2019.00158] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
56 Min F, Zhang Z, Zhai W, Shen R. Frequent pattern discovery with tri-partition alphabets. Information Sciences 2020;507:715-32. [DOI: 10.1016/j.ins.2018.04.013] [Cited by in Crossref: 44] [Article Influence: 22.0] [Reference Citation Analysis]
57 Cingiz MÖ, Diri B. Two-tier combinatorial structure to integrate various gene co-expression networks of prostate cancer. Gene 2019;721:144102. [PMID: 31499125 DOI: 10.1016/j.gene.2019.144102] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
58 Johnson EO, Hung DT. A Point of Inflection and Reflection on Systems Chemical Biology. ACS Chem Biol 2019;14:2497-511. [PMID: 31613592 DOI: 10.1021/acschembio.9b00714] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
59 Güvenç Paltun B, Mamitsuka H, Kaski S. Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Brief Bioinform 2021;22:346-59. [PMID: 31838491 DOI: 10.1093/bib/bbz153] [Cited by in Crossref: 9] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
60 Liu D, Davila-Velderrain J, Zhang Z, Kellis M. Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization. Nucleic Acids Res 2019;47:7235-46. [PMID: 31265076 DOI: 10.1093/nar/gkz538] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
61 Geng Y, Zhao Z, Liu J. [Reconstruction of tumor clonal haplotypes based on an improved spanning algorithm]. Nan Fang Yi Ke Da Xue Xue Bao 2019;39:1287-92. [PMID: 31852653 DOI: 10.12122/j.issn.1673-4254.2019.11.04] [Reference Citation Analysis]
62 Jan M, Gobet N, Diessler S, Franken P, Xenarios I. A multi-omics digital research object for the genetics of sleep regulation. Sci Data 2019;6:258. [PMID: 31672980 DOI: 10.1038/s41597-019-0171-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
63 Franzese N, Groce A, Murali TM, Ritz A. Hypergraph-based connectivity measures for signaling pathway topologies. PLoS Comput Biol 2019;15:e1007384. [PMID: 31652258 DOI: 10.1371/journal.pcbi.1007384] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 1.7] [Reference Citation Analysis]
64 Weighill D, Tschaplinski TJ, Tuskan GA, Jacobson D. Data Integration in Poplar: 'Omics Layers and Integration Strategies. Front Genet 2019;10:874. [PMID: 31608114 DOI: 10.3389/fgene.2019.00874] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]
65 Wani N, Raza K. Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Comput Biol Chem 2019;83:107120. [PMID: 31499298 DOI: 10.1016/j.compbiolchem.2019.107120] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 4.7] [Reference Citation Analysis]
66 Singh A, Goel N, Yogita. Integrative Analysis of Multi-Genomic Data for Kidney Renal Cell Carcinoma. Interdiscip Sci 2020;12:12-23. [PMID: 31392539 DOI: 10.1007/s12539-019-00345-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 0.7] [Reference Citation Analysis]
67 Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019;15:e1007084. [PMID: 31295267 DOI: 10.1371/journal.pcbi.1007084] [Cited by in Crossref: 70] [Cited by in F6Publishing: 82] [Article Influence: 23.3] [Reference Citation Analysis]
68 Guala D, Ogris C, Müller N, Sonnhammer ELL. Genome-wide functional association networks: background, data & state-of-the-art resources. Brief Bioinform 2020;21:1224-37. [PMID: 31281921 DOI: 10.1093/bib/bbz064] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
69 Fu G, Wang J, Domeniconi C, Yu G. Matrix factorization-based data fusion for the prediction of lncRNA-disease associations. Bioinformatics 2018;34:1529-37. [PMID: 29228285 DOI: 10.1093/bioinformatics/btx794] [Cited by in Crossref: 67] [Cited by in F6Publishing: 74] [Article Influence: 22.3] [Reference Citation Analysis]
70 Hampel H, Toschi N, Babiloni C, Baldacci F, Black KL, Bokde ALW, Bun RS, Cacciola F, Cavedo E, Chiesa PA, Colliot O, Coman CM, Dubois B, Duggento A, Durrleman S, Ferretti MT, George N, Genthon R, Habert MO, Herholz K, Koronyo Y, Koronyo-Hamaoui M, Lamari F, Langevin T, Lehéricy S, Lorenceau J, Neri C, Nisticò R, Nyasse-Messene F, Ritchie C, Rossi S, Santarnecchi E, Sporns O, Verdooner SR, Vergallo A, Villain N, Younesi E, Garaci F, Lista S; Alzheimer Precision Medicine Initiative (APMI). Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. J Alzheimers Dis 2018;64:S47-S105. [PMID: 29562524 DOI: 10.3233/JAD-179932] [Cited by in Crossref: 69] [Cited by in F6Publishing: 73] [Article Influence: 23.0] [Reference Citation Analysis]
71 García del Valle EP, Lagunes García G, Prieto Santamaría L, Zanin M, Menasalvas Ruiz E, Rodríguez-gonzález A. Disease networks and their contribution to disease understanding: A review of their evolution, techniques and data sources. Journal of Biomedical Informatics 2019;94:103206. [DOI: 10.1016/j.jbi.2019.103206] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
72 Darst BF, Lu Q, Johnson SC, Engelman CD. Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants. Genet Epidemiol 2019;43:657-74. [PMID: 31104335 DOI: 10.1002/gepi.22211] [Cited by in Crossref: 3] [Cited by in F6Publishing: 11] [Article Influence: 1.0] [Reference Citation Analysis]
73 Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet 2019;10:294. [PMID: 31031797 DOI: 10.3389/fgene.2019.00294] [Cited by in Crossref: 52] [Cited by in F6Publishing: 72] [Article Influence: 17.3] [Reference Citation Analysis]
74 Martino D, Ben-othman R, Harbeson D, Bosco A. Multiomics and Systems Biology Are Needed to Unravel the Complex Origins of Chronic Disease. Challenges 2019;10:23. [DOI: 10.3390/challe10010023] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
75 Li Y, Wu FX, Ngom A. A review on machine learning principles for multi-view biological data integration. Brief Bioinform 2018;19:325-40. [PMID: 28011753 DOI: 10.1093/bib/bbw113] [Cited by in Crossref: 72] [Cited by in F6Publishing: 105] [Article Influence: 24.0] [Reference Citation Analysis]
76 Moretto M, Sonego P, Villaseñor-Altamirano AB, Engelen K. First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_. BMC Bioinformatics 2019;20:54. [PMID: 30691411 DOI: 10.1186/s12859-019-2643-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
77 Yu L, Yao S, Gao L, Zha Y. Conserved Disease Modules Extracted From Multilayer Heterogeneous Disease and Gene Networks for Understanding Disease Mechanisms and Predicting Disease Treatments. Front Genet 2018;9:745. [PMID: 30713550 DOI: 10.3389/fgene.2018.00745] [Cited by in Crossref: 38] [Cited by in F6Publishing: 34] [Article Influence: 12.7] [Reference Citation Analysis]
78 Hammoud Z, Kramer F. mully: An R Package to Create, Modify and Visualize Multilayered Graphs. Genes (Basel) 2018;9:E519. [PMID: 30360563 DOI: 10.3390/genes9110519] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.8] [Reference Citation Analysis]
79 Titz B, Gadaleta RM, Lo Sasso G, Elamin A, Ekroos K, Ivanov NV, Peitsch MC, Hoeng J. Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification.Int J Mol Sci. 2018;19. [PMID: 30223557 DOI: 10.3390/ijms19092775] [Cited by in Crossref: 20] [Cited by in F6Publishing: 14] [Article Influence: 5.0] [Reference Citation Analysis]
80 Gu S, Johnson J, Faisal FE, Milenković T. From homogeneous to heterogeneous network alignment via colored graphlets. Sci Rep 2018;8:12524. [PMID: 30131590 DOI: 10.1038/s41598-018-30831-w] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
81 Yu J, Ping P, Wang L, Kuang L, Li X, Wu Z. A Novel Probability Model for LncRNA⁻Disease Association Prediction Based on the Naïve Bayesian Classifier. Genes (Basel) 2018;9:E345. [PMID: 29986541 DOI: 10.3390/genes9070345] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 7.8] [Reference Citation Analysis]
82 Ozsoy MG, Özyer T, Polat F, Alhajj R. Realizing drug repositioning by adapting a recommendation system to handle the process. BMC Bioinformatics 2018;19:136. [PMID: 29649971 DOI: 10.1186/s12859-018-2142-1] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
83 Zhou X, Lei L, Liu J, Halu A, Zhang Y, Li B, Guo Z, Liu G, Sun C, Loscalzo J, Sharma A, Wang Z. A Systems Approach to Refine Disease Taxonomy by Integrating Phenotypic and Molecular Networks. EBioMedicine 2018;31:79-91. [PMID: 29669699 DOI: 10.1016/j.ebiom.2018.04.002] [Cited by in Crossref: 31] [Cited by in F6Publishing: 19] [Article Influence: 7.8] [Reference Citation Analysis]
84 Zachariou M, Minadakis G, Oulas A, Afxenti S, Spyrou GM. Integrating multi-source information on a single network to detect disease-related clusters of molecular mechanisms. J Proteomics 2018;188:15-29. [PMID: 29545169 DOI: 10.1016/j.jprot.2018.03.009] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
85 Pittman ME, Edwards SW, Ives C, Mortensen HM. AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks. Toxicol Appl Pharmacol 2018;343:71-83. [PMID: 29454060 DOI: 10.1016/j.taap.2018.02.006] [Cited by in Crossref: 30] [Cited by in F6Publishing: 34] [Article Influence: 7.5] [Reference Citation Analysis]
86 Botero D, Alvarado C, Bernal A, Danies G, Restrepo S. Network Analyses in Plant Pathogens. Front Microbiol 2018;9:35. [PMID: 29441045 DOI: 10.3389/fmicb.2018.00035] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 1.3] [Reference Citation Analysis]
87 Lu YY, Lv J, Fuhrman JA, Sun F. Towards enhanced and interpretable clustering/classification in integrative genomics. Nucleic Acids Res 2017;45:e169. [PMID: 28977511 DOI: 10.1093/nar/gkx767] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
88 Cilek EE, Ozturk H, Gur Dedeoglu B. Construction of miRNA-miRNA networks revealing the complexity of miRNA-mediated mechanisms in trastuzumab treated breast cancer cell lines. PLoS One 2017;12:e0185558. [PMID: 28981542 DOI: 10.1371/journal.pone.0185558] [Cited by in Crossref: 18] [Cited by in F6Publishing: 22] [Article Influence: 3.6] [Reference Citation Analysis]
89 Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. Elife 2017;6:e26726. [PMID: 28936969 DOI: 10.7554/eLife.26726] [Cited by in Crossref: 114] [Cited by in F6Publishing: 115] [Article Influence: 22.8] [Reference Citation Analysis]
90 Fabres PJ, Collins C, Cavagnaro TR, Rodríguez López CM. A Concise Review on Multi-Omics Data Integration for Terroir Analysis in Vitis vinifera. Front Plant Sci 2017;8:1065. [PMID: 28676813 DOI: 10.3389/fpls.2017.01065] [Cited by in Crossref: 37] [Cited by in F6Publishing: 30] [Article Influence: 7.4] [Reference Citation Analysis]
91 Martínez M, Sorzano COS, Pascual-Montano A, Carazo JM. Gene signature associated with benign neurofibroma transformation to malignant peripheral nerve sheath tumors. PLoS One 2017;12:e0178316. [PMID: 28542306 DOI: 10.1371/journal.pone.0178316] [Cited by in Crossref: 1] [Article Influence: 0.2] [Reference Citation Analysis]
92 Cao Y, Xu W, Niu C, Bo X, Li F. NFP: An R Package for Characterizing and Comparing of Annotated Biological Networks. Biomed Res Int 2017;2017:7457131. [PMID: 28280740 DOI: 10.1155/2017/7457131] [Reference Citation Analysis]
93 Gligorijević V, Malod-Dognin N, Pržulj N. Integrative methods for analyzing big data in precision medicine. Proteomics 2016;16:741-58. [PMID: 26677817 DOI: 10.1002/pmic.201500396] [Cited by in Crossref: 107] [Cited by in F6Publishing: 89] [Article Influence: 17.8] [Reference Citation Analysis]
94 Pouladi N, Achour I, Li H, Berghout J, Kenost C, Gonzalez-Garay ML, Lussier YA. Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records. Yearb Med Inform 2016;:194-206. [PMID: 27830251 DOI: 10.15265/IY-2016-040] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
95 Walsh CJ, Hu P, Batt J, Dos Santos CC. Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods. Cancer Inform 2016;15:25-42. [PMID: 27721651 DOI: 10.4137/CIN.S39369] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.2] [Reference Citation Analysis]
96 Tebani A, Afonso C, Marret S, Bekri S. Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations. Int J Mol Sci 2016;17:E1555. [PMID: 27649151 DOI: 10.3390/ijms17091555] [Cited by in Crossref: 78] [Cited by in F6Publishing: 81] [Article Influence: 13.0] [Reference Citation Analysis]
97 Gligorijevic D, Stojanovic J, Djuric N, Radosavljevic V, Grbovic M, Kulathinal RJ, Obradovic Z. Large-Scale Discovery of Disease-Disease and Disease-Gene Associations. Sci Rep 2016;6:32404. [PMID: 27578529 DOI: 10.1038/srep32404] [Cited by in Crossref: 18] [Cited by in F6Publishing: 14] [Article Influence: 3.0] [Reference Citation Analysis]