Machine learning for Bioinformatics usually refers to the changes in systems that perform several tasks associated with artificial intelligence. Such tasks involve prediction recognition, planning, diagnosis etc. The changes might be either enhancement to already performing systems synthesis of new systems.
Machine learning for bioinformatics is a interesting and fast growing field that applies machine learning algorithms to solve various problems in bioinformatics, such as analyse biological data, predicting gene sequences, protein structures, drugs interactions.
Some tasks cannot be defined well except by example that is we might be able to specify input/output pairs but not a compact relationship between inputs and output. We might be like machines to be able to adjust their internal structure to produce actual outputs for a large number of samples inputs and thus appropriately check their input/output function to approximate the relationship unexpressed in the examples.
Machine learning for Bioinformatics methods can be used for on the job improvement of existing machine designs. Machine might be easy as compare to humans for capturing certain biological tasks. Environments changes over time.
Machines that can modify to a changing environment would reduce the need for constant redesign. Novel idea and task is continuously being discovered by humans. There is a many stream of new events in the world. Continuing redesign of artificial intelligence systems to confirm to new knowledge is inappropriate, but machine learning methods might be able to track much of it.
To pursue a career in machine learning in bioinformatics, you need to have a strong background in both computer science and biology. You should also have good mathematical and statistical skills, as well as programming skills in languages such as Python, R, Java, C++, or MATLAB. You should also be familiar with various machine learning tools and frameworks, such as TensorFlow, PyTorch, scikit-learn, Bioconductor, or BioPython.
Some of the Career Options in Machine Learning for Bioinformatics are:
- Phylogeneticist: A person who studies the evolutionary relationships among organisms using molecular data.
- Scientist Curator: A person who collects, organizes, annotates, and maintains biological data sets and databases.
- Protein Analyst: A person who analyzes the structure, function, and interactions of proteins using computational methods.
- Gene Analyst: A person who analyzes the structure, function, and regulation of genes using computational methods.
- Research Scientist/ Associate: A person who conducts research on various aspects of machine learning in bioinformatics using experimental or theoretical approaches.
- Computational Biologist: A person who applies computational methods to study biological systems at different levels of organization.
- Bioinformatics Software Developer: A person who develops software tools and applications for machine learning in bioinformatics.
- Database Programmer: A person who designs, develops, and maintains databases for storing and retrieving biological data.
- Network Administrator/ Analyst: A person who manages and monitors the network infrastructure and security for bioinformatics systems.
- Pharmacogenomics: A person who studies how genetic variations affect drug response and efficacy.
- Chemoinformatician: A person who applies computational methods to study chemical structures and properties.
- Structural Analyst: A person who studies the three-dimensional structure of biomolecules using computational methods.
- Molecular Modeler: A person who creates computer models of biomolecules and their interactions using computational methods.
- Pharmacogenetician: A person who studies how genetic variations affect drug metabolism and toxicity.
The average salary of a machine learning for bioinformatics professional depends on various factors, such as the level of education, experience, skills, location, and industry. According to Glassdoor, the average salary of a machine learning engineer in India is ₹1,014,000 per year, while the average salary of a bioinformatics scientist in India is ₹600,000 per year.
Applications Of Machine Learning for Bioinformatics
Machine learning for bioinformatics is a interesting and fast growing field that applies machine learning algorithms to solve various problems in bioinformatics such as predicting gene sequencing, drug interaction, structures of protein analysis of biological data and many more biological things
Some examples of applications of machine learning for bioinformatics are:
- Gene expression analysis – Machine learning can help identify patterns and clusters in gene expression data, such as microarrays, and reveal the underlying biological processes and pathways.
- Protein structure prediction – Machine learning can help predict the three-dimensional structure of proteins from their amino acid sequences, which is essential for understanding their functions and interactions.
- Drug discovery – Machine learning can help design new drugs by screening large databases of chemical compounds, predicting their binding affinity and toxicity, and optimizing their molecular structures.
- Text mining – Machine learning can help extract relevant information from large collections of biomedical literature, such as identifying genes, diseases, drugs, and their relationships.
Machine Learning in OMICS Field
As the bioinformatics field extend, it must keep not only with new data but with new algorithms too. The bioinformatics field is growingly on machine learning algorithms to conduct predictive analytics and obtain greater understanding of the complex biological processes of the human body.
The career scope of machine learning in bioinformatics is very promising, as there is a high demand for skilled professionals who can handle large and complex biological data sets and extract useful insights from them. Some of the areas where machine learning in bioinformatics can be applied are:
Machine Learning used in Biological Domain
- Genomics
- Proteomics
- Microarrays
- System biology
- Evolution
- Text mining
Genomics
There is an increasing required for the development of machine learning systems that can automatically determine the location of protein-encoding genes within given DNA sequence. This is the problem in computational biology that are known as gene prediction.
Machine learning has also been used for the problem of multiple sequence alignment which involves aligning DNA or amino acid sequences in order to determine the similarities that could indicate a shared development. It can also be used to detect and visualize genomes rearrangement.
Proteomics
Machine learning used to arrange the amino acids of a protein sequence into one of three structural classes (helix, coil, sheet).The current state of the art in secondary structure prediction uses a system which is Deep CNF (deep convolutional neural fields) which relies on the machine learning model of artificial neural networks to reach an accuracy of approximately 84%.
The theoretical limit for three-state protein secondary structure is 88–90%.Machine learning has also been applied to proteomics problems such as protein contact map prediction , protein side-chain prediction, , and protein contact map prediction, protein loop modelling.
Microarrays
One of the major problems in this field is to identifying which genes are expressed based on collected data. In addition, due to the large number of genes on which data is collected by the microarray, there is a large amount of irrelevant data for identification of expressed gene and further complicating this problem.
With the help of Machine learning a potential solution to this problem as various classification methods can be used to perform this identification. The most commonly used methods are radial basis function networks decision trees, Bayesian classification, deep learning and random forest.
Systems Biology
Machine learning has been used to help in the modelling of these complex interactions in biological systems in domains such as metabolic pathways, genetic networks and signal transduction networks. Graphical models, a machine learning technique for determining the structure between different type of variables, are one of the most commonly used methods for modelling in genetic networks.
In computations, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using a technique known as Markov chain optimization. Genetic algorithm, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures.
Other systems biology applications of machine learning include the task of high throughput microarray data analysis, enzyme function prediction, analysis of genome-wide association studies to better understand markers of disease, protein function prediction.
Text Mining
Machine learning can be used for this accomplish extraction process using techniques such as natural language processing to extract the useful information data from human generated reports in database.
This process has been applied to the search for target of new drugs, as this task requires the examination of information stored in biological databases and journals. Interpretations of protein in protein databases frequently do not reflect the complete known set of knowledge of each protein, so additional information must be extracted from biomedical reports.
Machine learning has been applied to automated annotation of the functions of gene and proteins, molecule interaction analysis, analysis of DNA-expression arrays, determination of the subcellular localization of a protein and large-scale protein interaction analysis.
Another application of text mining is the detection and visualization of typical DNA regions of reference data.
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