About: Machine learning in bioinformatics

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Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein structure prediction, this proved difficult. Machine learning techniques, such as deep learning can learn features of data sets, instead of requiring the programmer to define them individually. The algorithm can further learn how to combine low-level features into more abstract features, and so on. This multi-layered approach allows such systems to make sophisticated predictions when appropriately trained. These methods contrast with other computational biology approaches

Attributes	Values
rdfs:label	تعلم الآلة في المعلوماتية الحية (ar) Aprendizaje automático en bioinformática (es) Machine learning in bioinformatics (en)
rdfs:comment	تعلم الآلة (بالإنجليزية: Machine learning)‏، هو مجال فرعي من علم الحاسب الذي يشمل على تطوير خوارزميات تعلم كيفية إصدار التوقعات استنادا إلى البيانات، يحتوي على عدد من التطبيقات الناشئة في مجال المعلوماتية الحيوية. المعلوماتية الحيوية تتعامل مع طرق حسابية ورياضية النهج من أجل فهم ومعالجة البيانات البيولوجية. (ar) El aprendizaje automático en bioinformática consiste en la aplicación de algoritmos de aprendizaje automático, en entornos de bioinformática, como, por ejemplo, la genómica, la proteómica, los microarrays, la biología de sistemas, la biología evolutiva y la minería de textos. Esto permite automatizar la búsqueda de patrones complejos en series de datos, facilitando la comprensión de procesos biológicos tan complejos como la estructura de las proteínas, lo que diferencia a esta disciplina de los enfoques tradicionales de bioinformática, que requieren supervisión y que dificultan la aparición de patrones inesperados u ocultos. (es) Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein structure prediction, this proved difficult. Machine learning techniques, such as deep learning can learn features of data sets, instead of requiring the programmer to define them individually. The algorithm can further learn how to combine low-level features into more abstract features, and so on. This multi-layered approach allows such systems to make sophisticated predictions when appropriately trained. These methods contrast with other computational biology approaches (en)
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dcterms:subject	Bioinformatics Machine learning
Wikipage page ID	53970843 (xsd:integer)
Wikipage revision ID	1123482893 (xsd:integer)
Link from a Wikipage to another Wikipage	Protein secondary structure PubMed Naive Bayes classifier Natural language processing Metabolic gene cluster Metagenomics Cortical neuron Decision trees Deep neural network Beta sheet Cytochrome b DNA binding site Vectorization (mathematics) Decision tree Deep learning Polymerase chain reaction Protein primary structure Protein contact map Protein structure prediction Proteomics 16S ribosomal RNA Bioinformatics Colorectal cancer Computational genomics Cosine similarity Anaerobic organism Mathematical biology Gene expression Gene prediction Natural product Operational taxonomic unit Protein tertiary structure Radial basis function network 16S rRNA 18S Clostridioides difficile GenBank Gene regulation Genetic algorithm Genomics Convolutional neural network Lab-on-a-chip Machine learning Stroke Cluster analysis Computational biology Feature (machine learning) Feature engineering Feature extraction Feature learning Kruskal–Wallis one-way analysis of variance Precision medicine Protein structure Protein targeting Microarray Machine learning Centroid Time complexity Docking (molecular) Euclidean spaces K-medoids K-mer Protein quaternary structure Alpha helix Amino acid Euclidean space Evolution Nonribosomal peptide Biological Graphical model Data mining Principal component analysis Visual cortex Gaussian distribution Protein folding Visual field Protein Random forest Receptive field Recurrent neural network Regression analysis

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