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For: Bean DM, Al-Chalabi A, Dobson RJB, Iacoangeli A. A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis. Genes (Basel) 2020;11:E668. [PMID: 32575372 DOI: 10.3390/genes11060668] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
Number Citing Articles
1 Founta K, Dafou D, Kanata E, Sklaviadis T, Zanos TP, Gounaris A, Xanthopoulos K. Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning. Mol Med 2023;29:12. [PMID: 36694130 DOI: 10.1186/s10020-023-00603-y] [Reference Citation Analysis]
2 Joshi I, Bhrdwaj A, Khandelwal R, Pande A, Agarwal A, Srija CD, Suresh RA, Mohan M, Hazarika L, Thakur G, Hussain T, Albogami S, Nayarisseri A, Singh SK. Artificial intelligence, big data and machine learning approaches in genome-wide SNP-based prediction for precision medicine and drug discovery. Big Data Analytics in Chemoinformatics and Bioinformatics 2023. [DOI: 10.1016/b978-0-323-85713-0.00021-9] [Reference Citation Analysis]
3 Ba H, Wang X, Wang D, Ren J, Wang Z, Sun HX, Hu P, Zhang G, Wang S, Ma C, Wang Y, Wang E, Chen L, Liu T, Gu Y, Li C. Single-cell transcriptome reveals core cell populations and androgen-RXFP2 axis involved in deer antler full regeneration. Cell Regen 2022;11:43. [PMID: 36542206 DOI: 10.1186/s13619-022-00153-4] [Reference Citation Analysis]
4 Das T, Kaur H, Gour P, Prasad K, Lynn AM, Prakash A, Kumar V. Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review. Brief Bioinform 2022;23:bbac442. [PMID: 36411673 DOI: 10.1093/bib/bbac442] [Reference Citation Analysis]
5 Goutman SA, Hardiman O, Al-Chalabi A, Chió A, Savelieff MG, Kiernan MC, Feldman EL. Emerging insights into the complex genetics and pathophysiology of amyotrophic lateral sclerosis. Lancet Neurol 2022;21:465-79. [PMID: 35334234 DOI: 10.1016/S1474-4422(21)00414-2] [Cited by in Crossref: 44] [Cited by in F6Publishing: 24] [Article Influence: 44.0] [Reference Citation Analysis]
6 Dhasmana S, Dhasmana A, Narula AS, Jaggi M, Yallapu MM, Chauhan SC. The panoramic view of amyotrophic lateral sclerosis: A fatal intricate neurological disorder. Life Sci 2022;288:120156. [PMID: 34801512 DOI: 10.1016/j.lfs.2021.120156] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
7 Hu J, Lepore R, Dobson RJB, Al-Chalabi A, M Bean D, Iacoangeli A. DGLinker: flexible knowledge-graph prediction of disease-gene associations. Nucleic Acids Res 2021;49:W153-61. [PMID: 34125897 DOI: 10.1093/nar/gkab449] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
8 Zhu J, Wang S, Bai H, Wang K, Hao J, Zhang J, Li J. Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer. Front Oncol 2021;11:588811. [PMID: 33747908 DOI: 10.3389/fonc.2021.588811] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
9 Broce IJ, Castruita PA, Yokoyama JS. Moving Toward Patient-Tailored Treatment in ALS and FTD: The Potential of Genomic Assessment as a Tool for Biological Discovery and Trial Recruitment. Front Neurosci 2021;15:639078. [PMID: 33732107 DOI: 10.3389/fnins.2021.639078] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
10 Ashenden SK, Kurbatova N, Bartosik A. Target identification and validation. The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry 2021. [DOI: 10.1016/b978-0-12-820045-2.00005-2] [Reference Citation Analysis]
11 Vasilopoulou C, Morris AP, Giannakopoulos G, Duguez S, Duddy W. What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis? J Pers Med 2020;10:E247. [PMID: 33256133 DOI: 10.3390/jpm10040247] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
12 Morello G, Salomone S, D'Agata V, Conforti FL, Cavallaro S. From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis. Front Neurosci 2020;14:577755. [PMID: 33192262 DOI: 10.3389/fnins.2020.577755] [Cited by in Crossref: 21] [Cited by in F6Publishing: 23] [Article Influence: 7.0] [Reference Citation Analysis]
13 Iacoangeli A, Lin T, Al Khleifat A, Jones AR, Opie-Martin S, Coleman JRI, Shatunov A, Sproviero W, Williams KL, Garton F, Restuadi R, Henders AK, Mather KA, Needham M, Mathers S, Nicholson GA, Rowe DB, Henderson R, McCombe PA, Pamphlett R, Blair IP, Schultz D, Sachdev PS, Newhouse SJ, Proitsi P, Fogh I, Ngo ST, Dobson RJB, Wray NR, Steyn FJ, Al-Chalabi A. Genome-wide Meta-analysis Finds the ACSL5-ZDHHC6 Locus Is Associated with ALS and Links Weight Loss to the Disease Genetics. Cell Rep 2020;33:108323. [PMID: 33113361 DOI: 10.1016/j.celrep.2020.108323] [Cited by in Crossref: 26] [Cited by in F6Publishing: 22] [Article Influence: 8.7] [Reference Citation Analysis]