Role of Machine Learning in Bioinformatics

Machine learning (ML) is a branch of artificial intelligence (AI) that allows computers to learn and improve from experience without being explicitly programmed. In the field of bioinformatics, ML has emerged as a powerful tool for analysing and interpreting vast amounts of biological data. The application of machine learning algorithms can be incorporated to bioinformatics applications such as genomics, proteomics, system biology, synthetic biology, microarrays and in text mining. This article summarizes the role of machine learning in bioinformatics and bioinformatics research with suitable examples.

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Machine Learning in Bioinformatics

Ø  Before the advent of machine learning, bioinformatics algorithms, such as protein structure prediction, had to be programmed by hand.

Ø  Size and the number of available biological datasets have increased dramatically in recent years.

Ø  Deep learning and other machine learning techniques can learn features of these data sets rather than requiring the programmer to define them individually.

Ø  The algorithm can also learn how to combine low-level features to create more abstract features.

Ø  When properly trained, this multi-layered approach enables such systems to make sophisticated predictions.

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Applications of Machine Learning in Bioinformatics

Machine Learning in Bioinformatics

Classification and Prediction

Ø  Machine learning algorithms can be used to classify the biological data.

Ø  Classifications of data can be based on various criteria, such as disease diagnosis, protein function prediction, and gene expression analysis.

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Ø  Example: ML can help to label new genomic data (such as genomes of unculturable bacteria) based on a model of already labelled data.

Ø  ML algorithms can be used to predict the outcome of experiments.

Ø  Machine learning can also use to design and experiment or to predict the outcome of drug trial experiments.

Feature Selection

Ø  Feature Selection is the method of reducing the input variable to your model by using only relevant data and avoiding the noise in the data.

Ø  It is the process of automatically choosing relevant features for your machine learning model based on the type of problem you are trying to solve.

Ø  ML algorithms can identify the most relevant features of biological data that are responsible for a particular phenotype or trait.

Ø  This can be used to identify important biomarkers or genetic variants associated with disease.

Ø  It can also identify the changes in the expression of a particular gene during different growth phases if an organism.

Clustering and Association

Ø  This is based on two analysis of data (1). Clustering and (2). Association.

Ø  Cluster analysis finds the commonalities between the data and categorizes them as per the presence and absence of those commonalities.

Ø  Association is a learning method in ML, which is used for finding the relationships between variables in a large database.

Ø  ML algorithms can cluster similar biological data based on shared features.

Ø  ML can also identify associations between different types of biological data, such as genes, gene and protein sequences and diseases.

Deep learning

Ø  Deep learning is a type of ML that uses artificial neural networks to identify complex patterns in biological data.

Ø  Deep learning can be used to predict protein-protein interactions and drug-target interactions in a cell.

Ø  It can also help to find new drug candidate molecules and its possible efficacies and side-effects.

Data Integration

Ø  Machine learning algorithms can integrate and analyse data from multiple sources, such as genetic, proteomic, and clinical data.

Ø  It can also identify new biomarkers or disease targets.

Image analysis

Ø  ML algorithms can analyze and interpret large amounts of data generated by imaging techniques, such as microscopy and MRI.

Ø  This can help you to identify structures, cells or tissues with the help of images.

Ø  It can analyse the images of a biopsy or an MRI scan to diagnose the disease. It can also help to stage a pathological condition such as cancer.

Ø  More interestingly, image analysis can help you to identify plants and animals with photographs taken with our mobile phones.

Overall, Machine Learning has revolutionized the field of bioinformatics by providing powerful tools for analysing and interpreting large and complex biological datasets. It has the potential to accelerate the discovery of new treatments and cures for diseases and transform the practice of medicine. Moreover, Machine learning techniques together Artificial Intelligence can help the researchers to solve complex biological problems.

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  1. The article is good upto 1st year college students but its little thin on concept side.One of the most basic algorithm where all machine learning starts is feature selection such as PCA which you have not mentioned which i have used and use extensively for building basic models which downstream application is building survival or regression building. supervised vs unsupervised methods, for example in RNA seq there are plenty of supervised methods which you could have written bit more

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