Davide Chicco - Search Results

Year	Number of Results
2015	2
2016	1
2017	3
2018	2
2019	3
2020	5
2021	10
2022	2

1

Siamese Neural Networks: An Overview.

Chicco D. Chicco D. Methods Mol Biol. 2021;2190:73-94. doi: 10.1007/978-1-0716-0826-5_3. Methods Mol Biol. 2021. PMID: 32804361 Review.

2

Brief Survey on Machine Learning in Epistasis.

Chicco D, Faultless T. Chicco D, et al. Methods Mol Biol. 2021;2212:169-179. doi: 10.1007/978-1-0716-0947-7_11. Methods Mol Biol. 2021. PMID: 33733356 Review.

3

Genome Informatics 2016.

Chicco D, Hoffman MM. Chicco D, et al. Genome Biol. 2017 Jan 16;18(1):5. doi: 10.1186/s13059-016-1135-5. Genome Biol. 2017. PMID: 28093077 Free PMC article.

4

Ten quick tips for machine learning in computational biology.

Chicco D. Chicco D. BioData Min. 2017 Dec 8;10:35. doi: 10.1186/s13040-017-0155-3. eCollection 2017. BioData Min. 2017. PMID: 29234465 Free PMC article. Review.

5

Editorial: Artificial Intelligence Bioinformatics: Development and Application of Tools for Omics and Inter-Omics Studies.

Chicco D, Heider D, Facchiano A. Chicco D, et al. Front Genet. 2020 Apr 9;11:309. doi: 10.3389/fgene.2020.00309. eCollection 2020. Front Genet. 2020. PMID: 32328085 Free PMC article. No abstract available.

6

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality.

Shin S, Austin PC, Ross HJ, Abdel-Qadir H, Freitas C, Tomlinson G, Chicco D, Mahendiran M, Lawler PR, Billia F, Gramolini A, Epelman S, Wang B, Lee DS. Shin S, et al. Among authors: chicco d. ESC Heart Fail. 2021 Feb;8(1):106-115. doi: 10.1002/ehf2.13073. Epub 2020 Nov 17. ESC Heart Fail. 2021. PMID: 33205591 Free PMC article.

7

Data analytics and clinical feature ranking of medical records of patients with sepsis.

Chicco D, Oneto L. Chicco D, et al. BioData Min. 2021 Feb 3;14(1):12. doi: 10.1186/s13040-021-00235-0. BioData Min. 2021. PMID: 33536030 Free PMC article.

8

geoCancerPrognosticDatasetsRetriever, a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO).

Alameer A, Chicco D. Alameer A, et al. Among authors: chicco d. Bioinformatics. 2021 Dec 22:btab852. doi: 10.1093/bioinformatics/btab852. Online ahead of print. Bioinformatics. 2021. PMID: 34935889

9

An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data.

Chicco D, Oneto L. Chicco D, et al. IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2759-2765. doi: 10.1109/TCBB.2020.3041527. Epub 2021 Dec 8. IEEE/ACM Trans Comput Biol Bioinform. 2021. PMID: 33259306

10

Machine Learning Compared With Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic Review.

Cho SM, Austin PC, Ross HJ, Abdel-Qadir H, Chicco D, Tomlinson G, Taheri C, Foroutan F, Lawler PR, Billia F, Gramolini A, Epelman S, Wang B, Lee DS. Cho SM, et al. Among authors: chicco d. Can J Cardiol. 2021 Aug;37(8):1207-1214. doi: 10.1016/j.cjca.2021.02.020. Epub 2021 Mar 5. Can J Cardiol. 2021. PMID: 33677098

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