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For: Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021;19:3735-46. [PMID: 34285775 DOI: 10.1016/j.csbj.2021.06.030] [Cited by in F6Publishing: 23] [Reference Citation Analysis]
Number Citing Articles
1 Hiort P, Hugo J, Zeinert J, Müller N, Kashyap S, Rajapakse JC, Azuaje F, Renard BY, Baum K. DrDimont: explainable drug response prediction from differential analysis of multi-omics networks. Bioinformatics 2022;38:ii113-9. [PMID: 36124784 DOI: 10.1093/bioinformatics/btac477] [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 Ranek JS, Stanley N, Purvis JE. Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction. Genome Biol 2022;23:186. [PMID: 36064614 DOI: 10.1186/s13059-022-02749-0] [Reference Citation Analysis]
4 Guo X, Han J, Song Y, Yin Z, Liu S, Shang X. Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions. Front Genet 2022;13:921775. [DOI: 10.3389/fgene.2022.921775] [Reference Citation Analysis]
5 Chen YA, Allendes Osorio RS, Mizuguchi K. TargetMine 2022: A new vision into drug target analysis. Bioinformatics 2022:btac507. [PMID: 35894632 DOI: 10.1093/bioinformatics/btac507] [Reference Citation Analysis]
6 Abdelhalim H, Berber A, Lodi M, Jain R, Nair A, Pappu A, Patel K, Venkat V, Venkatesan C, Wable R, Dinatale M, Fu A, Iyer V, Kalove I, Kleyman M, Koutsoutis J, Menna D, Paliwal M, Patel N, Patel T, Rafique Z, Samadi R, Varadhan R, Bolla S, Vadapalli S, Ahmed Z. Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine. Front Genet 2022;13:929736. [PMID: 35873469 DOI: 10.3389/fgene.2022.929736] [Reference Citation Analysis]
7 Chaturvedi P, Khan R, Sahu P, Ludhiadch A, Singh G, Munshi A. Role of Omics in Migraine Research and Management: A Narrative Review. Mol Neurobiol 2022. [PMID: 35796901 DOI: 10.1007/s12035-022-02930-3] [Reference Citation Analysis]
8 Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications. Cell Reports Physical Science 2022;3:100978. [DOI: 10.1016/j.xcrp.2022.100978] [Reference Citation Analysis]
9 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]
10 Noviani M, Chellamuthu VR, Albani S, Low AHL. Toward Molecular Stratification and Precision Medicine in Systemic Sclerosis. Front Med 2022;9:911977. [DOI: 10.3389/fmed.2022.911977] [Reference Citation Analysis]
11 Lombardo SD, Wangsaputra IF, Menche J, Stevens A. Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease. Genes 2022;13:764. [DOI: 10.3390/genes13050764] [Reference Citation Analysis]
12 Hall RD, D’auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? Trends in Plant Science 2022. [DOI: 10.1016/j.tplants.2022.02.001] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
13 Cai Z, Poulos RC, Liu J, Zhong Q. Machine learning for multi-omics data integration in cancer. iScience 2022;25:103798. [PMID: 35169688 DOI: 10.1016/j.isci.2022.103798] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
14 Villa Nova M, Lin TP, Shanehsazzadeh S, Jain K, Ng SCY, Wacker R, Chichakly K, Wacker MG. Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence. Front Digit Health 2022;4:799341. [DOI: 10.3389/fdgth.2022.799341] [Reference Citation Analysis]
15 John Cremin C, Dash S, Huang X. Big Data: Historic Advances and Emerging Trends in Biomedical Research. Current Research in Biotechnology 2022. [DOI: 10.1016/j.crbiot.2022.02.004] [Reference Citation Analysis]
16 Arjmand B, Hamidpour SK, Tayanloo-beik A, Goodarzi P, Aghayan HR, Adibi H, Larijani B. Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer. Front Genet 2022;13:824451. [DOI: 10.3389/fgene.2022.824451] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
17 Boroń D, Zmarzły N, Wierzbik-Strońska M, Rosińczuk J, Mieszczański P, Grabarek BO. Recent Multiomics Approaches in Endometrial Cancer. Int J Mol Sci 2022;23:1237. [PMID: 35163161 DOI: 10.3390/ijms23031237] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022;8:768106. [DOI: 10.3389/fmolb.2021.768106] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
19 Espejo C, Patchett AL, Wilson R, Lyons AB, Woods GM. Challenges of an Emerging Disease: The Evolving Approach to Diagnosing Devil Facial Tumour Disease. Pathogens 2021;11:27. [PMID: 35055975 DOI: 10.3390/pathogens11010027] [Reference Citation Analysis]
20 Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods. Front Plant Sci 2021;12:777028. [PMID: 34880894 DOI: 10.3389/fpls.2021.777028] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
21 Glaab E, Rauschenberger A, Banzi R, Gerardi C, Garcia P, Demotes J. Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review. BMJ Open 2021;11:e053674. [PMID: 34873011 DOI: 10.1136/bmjopen-2021-053674] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
22 Demirel HC, Arici MK, Tuncbag N. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications. Mol Omics 2021. [PMID: 34734935 DOI: 10.1039/d1mo00158b] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
23 Ni D, Chai Z, Wang Y, Li M, Yu Z, Liu Y, Lu S, Zhang J. Along the allostery stream: Recent advances in computational methods for allosteric drug discovery. WIREs Comput Mol Sci. [DOI: 10.1002/wcms.1585] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
24 Subbannayya Y, Di Fiore R, Urru SAM, Calleja-Agius J. The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives. Biomedicines 2021;9:1481. [PMID: 34680597 DOI: 10.3390/biomedicines9101481] [Cited by in F6Publishing: 3] [Reference Citation Analysis]