Machine learning for bioinformatics and neuroimaging

Angela Serra, Paola Galdi, Roberto Tagliaferri*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Machine Learning (ML) is a well-known paradigm that refers to the ability of systems to learn a specific task from the data and aims to develop computer algorithms that improve with experience. It involves computational methodologies to address complex real-world problems and promises to enable computers to assist humans in the analysis of large, complex data sets. ML approaches have been widely applied to biomedical fields and a great body of research is devoted to this topic. The purpose of this article is to present the state-of-the art in ML applications to bioinformatics and neuroimaging and motivate research in new trend-setting directions. We show how ML techniques such as clustering, classification, embedding techniques and network-based approaches can be successfully employed to tackle various problems such as gene expression clustering, patient classification, brain networks analysis, and identification of biomarkers. We also present a short description of deep learning and multiview learning methodologies applied in these contexts. We discuss some representative methods to provide inspiring examples to illustrate how ML can be used to address these problems and how biomedical data can be characterized through ML. Challenges to be addressed and directions for future research are presented and an extensive bibliography is included. This article is categorized under: Application Areas > Health Care Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

Original languageEnglish
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Early online date22 Feb 2018
DOIs
Publication statusPublished - Aug 2018

Keywords

  • Bioinformatics
  • Classification
  • Clustering
  • Deep learning
  • Dimensionality reduction
  • Feature selection
  • Machine learning
  • Multi-view learning
  • Networks
  • Neuroimaging

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