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Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, [1] including genomics, proteomics, microarrays, systems biology, evolution, and text mining. [ 2 ] [ 3 ]
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization.
Component-based data mining and machine learning software suite written in C++, featuring a visual programming front-end for exploratory data analysis and interactive visualization, and Python bindings and libraries for scripting
Support-vector machine (SVM) based system to find genes: Eukaryotes [34] mGene.ngs SVM based system to find genes using heterogeneous information: RNA-seq, tiling arrays: Eukaryotes [35] MORGAN: Decision tree system to find genes in vertebrate DNA: Eukaryotes [36] BioNIX
The Biopython project is an open-source collection of non-commercial Python tools for computational biology and bioinformatics, created by an international association of developers. [1] [4] [5] It contains classes to represent biological sequences and sequence annotations, and it is able to read and write to a variety of file formats.
The concept of biological computation proposes that living organisms perform computations, and that as such, abstract ideas of information and computation may be key to understanding biology.
Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures.