Chapter 2 Machine Learning : An Indispensable Tool in Bioinformatics

The increase in the number and complexity of biological databases has raised the need for modern and powerful data analysis tools and techniques. In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories. The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications. It is broadly used to investigate the underlying mechanisms and interactions between biological molecules in many diseases, and it is an essential tool in any biomarker discovery process. In this chapter, we provide a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown. Feature selection, classifier evaluation, and two supervised classification topics that have a deep impact on current bioinformatics are presented. We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine

[1]  Concha Bielza,et al.  Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.

[2]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[3]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[4]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[5]  Edward R. Dougherty,et al.  Superior feature-set ranking for small samples using bolstered error estimation , 2005, Bioinform..

[6]  Richard Baumgartner,et al.  Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions , 2003, Bioinform..

[7]  M. Hilario,et al.  Processing and classification of protein mass spectra. , 2006, Mass spectrometry reviews.

[8]  Hiroshi Motoda,et al.  Computational Methods of Feature Selection , 2022 .

[9]  Ron Kohavi,et al.  Data Mining Using MLC a Machine Learning Library in C++ , 1996, Int. J. Artif. Intell. Tools.

[10]  Ingo Mierswa,et al.  YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.

[11]  Kathleen Marchal,et al.  Advances in Cluster Analysis of Microarray Data , 2005, Data Analysis and Visualization in Genomics and Proteomics.

[12]  M. Kubát.  An Introduction to Machine Learning , 2017, Springer International Publishing.

[13]  Blaz Zupan,et al.  Orange: From Experimental Machine Learning to Interactive Data Mining , 2004, PKDD.

[14]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[15]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[16]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[17]  J. Davis.  Bioinformatics and Computational Biology Solutions Using R and Bioconductor , 2007 .

[18]  Mia K. Markey,et al.  A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples , 2006, J. Biomed. Informatics.