Machine learning in Bioinformatics

Machine learning in Bioinformatics

The use of machine learning techniques to bioinformatics, such as genomics, proteomics, microarrays, systems biology, evolution, and text mining, is known as machine learning in bioinformatics. High-performance computing allows important breakthroughs in bioinformatics.

Prior to the invention of machine learning algorithms, bioinformatics programmes had to be explicitly designed by hand, which was incredibly difficult for tasks like protein structure prediction. Automatic feature learning is enabled by machine learning techniques like as deep learning, which allow the algorithm to learn how to combine various aspects of the input data into a more abstract collection of features from which to conduct additional learning based on the dataset alone. When trained on huge datasets, this multi-layered method allows such computers to generate extremely complicated predictions. Other computational biology approaches, on the other hand, while still capable of dealing with enormous datasets, do not allow the data to be interpreted and evaluated only by the engine. The quantity and amount of available biological datasets have exploded in recent years, allowing bioinformatics researchers to apply machine learning algorithms.

 

  • The role of micro RNA (mi RNA) and small interfering RNA ( si RNA)
  • Transcription of gene

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