How Machine Learning is Transforming the Field of Biology

Machine Learning

Machine LearningMachine learning in biology has been used broadly for prediction and discovery.

Machine learning in the field of biology has multiple applications, ranging from natural language processing to healthcare. Bioinformatics and biology-related disciplines have not been left behind in the revolution. Before machine learning emerged, these disciplines faced the problem of extracting valuable insights from large biological datasets. But as of today, ML techniques in the field of Biology such as deep learning can learn the features of complex datasets and present them in a manner that is easy to understand.

Applications of AI in biology have stormed the world and have changed the way we work and live. Advances in these areas have led to many either praising it or decrying it. AI and ML, as they popularly have several applications and benefits across a wide range of industries. Most notably, they are revolutionizing the way biological research is performed, leading to new innovations across healthcare and biotechnology.

 

Applications of Machine Learning in Biology

Identifying gene coding regions

In the field of genomics, next-generation sequencing is making rapid progress by sequencing genomes in a short period of time. Therefore, Machine Learning in Biology is applied to identify genetic coding regions within the genome. Such gene prediction tools that incorporate machine learning are more sensitive than common homology-based sequence searches.

 

Structure prediction

Proteomics is already working on PPI. However, using Machine Learning in Biology improved accuracy from 70% to over 80%. Using machine learning in text mining holds great promise because it uses training sets to identify new drug discovery targets from multiple journal articles and search secondary databases.

 

Neural networks

Deep learning is a new branch of machine learning that is an extension of neural networks. In deep learning, “deep” refers to the number of layers to which data is transformed. Deep learning is similar to a multi-layer neural network. These multi-layered nodes try to mimic what the human brain thinks to solve a problem. Neural networks are already used in machine learning. Machine learning in Biology algorithms based on neural networks requires sophisticated or important data from raw datasets to perform the analysis. However, the increasing amount of data on genome sequencing has made it difficult to process meaningful information and perform analyzes. Multiple layers of the neural network filter the information and communicate with each layer to improve the output.

Deep learning algorithms extract features from large data sets like a group of images or genomes and develop a model on the basis of extracted features. Once the model is developed, then algorithms can use the developed model to perform analysis of other data set. Today, scientists use deep learning algorithms in biology to perform classification of cellular images, genome analysis, drug discovery and also find out how image data and genome data are linked with electronic medical records. Nowadays deep learning in biology is an active field. Deep learning is applied to high-throughput biological data that help to make better understating about high dimension data set. Computational biology uses deep learning in regulatory genomics to identify regulatory variants, the effects of mutations using DNA sequences, and analysis of whole-cell and tissue populations.

 

AI in healthcare

Machine learning and AI in biology are widely used by hospitals and healthcare providers to improve patient satisfaction, provide personalized treatment, make accurate predictions, and improve quality of life. It is also used to make clinical trials more efficient and speed up drug discovery and delivery processes.

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