Literature DB >> 24272434

Introduction to machine learning.

Yalin Baştanlar1, Mustafa Ozuysal.   

Abstract

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

Mesh:

Year:  2014        PMID: 24272434     DOI: 10.1007/978-1-62703-748-8_7

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  52 in total

1.  Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods.

Authors:  Viviane Ribas Pereira; Danillo Roberto Pereira; Kátia Cristina de Melo Tavares Vieira; Vitor Pereira Ribas; Carlos José Leopoldo Constantino; Patrícia Alexandra Antunes; Ana Paula Alves Favareto
Journal:  Environ Sci Pollut Res Int       Date:  2019-11-07       Impact factor: 4.223

Review 2.  Recent advances on the machine learning methods in predicting ncRNA-protein interactions.

Authors:  Lin Zhong; Meiqin Zhen; Jianqiang Sun; Qi Zhao
Journal:  Mol Genet Genomics       Date:  2020-10-02       Impact factor: 3.291

Review 3.  Computational algorithms for in silico profiling of activating mutations in cancer.

Authors:  E Joseph Jordan; Keshav Patil; Krishna Suresh; Jin H Park; Yael P Mosse; Mark A Lemmon; Ravi Radhakrishnan
Journal:  Cell Mol Life Sci       Date:  2019-04-13       Impact factor: 9.261

Review 4.  Machine learning in neuro-oncology: toward novel development fields.

Authors:  Vincenzo Di Nunno; Mario Fordellone; Giuseppe Minniti; Sofia Asioli; Alfredo Conti; Diego Mazzatenta; Damiano Balestrini; Paolo Chiodini; Raffaele Agati; Caterina Tonon; Alicia Tosoni; Lidia Gatto; Stefania Bartolini; Raffaele Lodi; Enrico Franceschi
Journal:  J Neurooncol       Date:  2022-06-28       Impact factor: 4.506

Review 5.  Radiomics: a primer on high-throughput image phenotyping.

Authors:  Kyle J Lafata; Yuqi Wang; Brandon Konkel; Fang-Fang Yin; Mustafa R Bashir
Journal:  Abdom Radiol (NY)       Date:  2021-08-25

Review 6.  Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Authors:  Miroslava Cuperlovic-Culf; Thao Nguyen-Tran; Steffany A L Bennett
Journal:  Methods Mol Biol       Date:  2023

7.  The symptom phenotype of oncology outpatients remains relatively stable from prior to through 1 week following chemotherapy.

Authors:  C Miaskowski; B A Cooper; B Aouizerat; M Melisko; L-M Chen; L Dunn; X Hu; K M Kober; J Mastick; J D Levine; M Hammer; F Wright; J Harris; J Armes; E Furlong; P Fox; E Ream; R Maguire; N Kearney
Journal:  Eur J Cancer Care (Engl)       Date:  2016-01-18       Impact factor: 2.520

Review 8.  An introduction to machine learning and analysis of its use in rheumatic diseases.

Authors:  Kathryn M Kingsmore; Christopher E Puglisi; Amrie C Grammer; Peter E Lipsky
Journal:  Nat Rev Rheumatol       Date:  2021-11-02       Impact factor: 20.543

Review 9.  Application of machine learning in predicting hospital readmissions: a scoping review of the literature.

Authors:  Yinan Huang; Ashna Talwar; Satabdi Chatterjee; Rajender R Aparasu
Journal:  BMC Med Res Methodol       Date:  2021-05-06       Impact factor: 4.615

10.  Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department.

Authors:  I-Min Chiu; Chi-Yung Cheng; Wun-Huei Zeng; Ying-Hsien Huang; Chun-Hung Richard Lin
Journal:  J Clin Med       Date:  2021-04-26       Impact factor: 4.241

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