ML is transforming biological research, resulting in new discoveries in healthcare and biotechnology.
Machine Learning and Artificial Intelligence have taken the world by storm, changing the way people live and work. Advances in these fields have elicited both praise and criticism. AI and ML, as they’re colloquially known, offer several applications and advantages across a wide range of sectors. Most importantly, they are transforming biological research, resulting in new discoveries in healthcare and biotechnology.
What Are The Applications of Machine Learning in Biotechnology?
Here are some use cases of ML in biotech:
Identifying Gene Coding Regions
Next-generation sequencing has greatly improved the study of genomics by sequencing a gene in a short period of time. As a result, the machine learning approach is being used to discover gene coding areas in a genome. Such machine learning-based gene prediction techniques would be more sensitive than traditional homology-based sequence analyses.
Structure Prediction
PPI was mentioned before in the context of proteomics. However, the application of ML in structure prediction has increased accuracy from 70% to more than 80%. The application of ML in text mining is extremely promising, with training sets used to find new or unique pharmacological targets from many journal articles and secondary databases searched.
Neural Networks
Deep learning is an extension of neural networks and is a relatively new topic in ML. The term “deep” in deep learning represents the number of layers through which data is changed. As a result, deep learning is analogous to a multi-layer neural structure. These multi-layer nodes attempt to simulate how the human brain works in order to solve issues. ML already uses neural networks. To undertake analysis, neural network-based ML algorithms require refined or meaningful data from raw data sets. However, the rising amount of data generated by genome sequencing makes it harder to analyse significant information. Multiple layers of a neural network filter information and interact with each other, allowing the output to be refined.
Mental Illness
Anxiety, stress, substance use disorder, eating disorder, and other symptoms of mental disease are examples. The bad news is that most people go undiagnosed since they are not sure if they have a problem. That is a stunning but harsh reality. Until today, doctors and scientists have not been as effective in predicting mental diseases. Yes, technology innovation has enabled healthcare professionals to create smart solutions that not only detect mental disease but also recommend the appropriate diagnostic and treatment techniques.
AI in Healthcare
Machine learning and artificial intelligence (AI) are widely employed by hospitals and healthcare providers to increase patient happiness, administer individualized treatments, make accurate forecasts, and improve quality of life. It is also being utilized to improve the efficiency of clinical trials and to accelerate the process of medication development and distribution.
Final Thoughts
The development of digitization has rendered the twenty-first-century data-centric, affecting every business and sector. The healthcare, biology, and biotech industries are not immune to the effects. Enterprises are seeking to locate a solution that can combine their operations with a powerful resolution and give the capacity to record, exchange, and transmit data in a systematic, quicker, and smoother manner. Bioinformatics, biomedicine, network biology, and other biological subfields have long struggled with biological data processing challenges.
Source: analyticsinsight.net
