Thank you for Subscribing to Life Science Review Weekly Brief
Machine learning in life sciences and biotechnology is helping health professionals and patients to track health and provide treatment in time.
FREMONT CA: Health is the most valuable possession to humans, and they will go to any length to maintain it. As a result, the life sciences and biotechnology industry is massive and multifaceted, with numerous subsectors. Drug development and production, therapies, diagnostics, therapeutics, genomics and proteomics, veterinary life sciences, as well as cosmetics, medical technology, and distribution are the most well-known sectors.
For data scientists, the life sciences and biotechnology industry is a dream come true. The players have a lot of data, and a data scientist works in the big data field on a daily basis. The primary priority is causality and precision, which necessitates a solid mathematical and (bio)statistical foundation. If an algorithm leads to a wrong decision in medication research, it might result in a massive loss of money or even loss of life.
Here are three use cases of machine learning in life science and biotechnology:
● Development of microbiome therapeutics
In the human body, there are many microorganisms, or microbiota, such as bacteria, fungi, viruses, and other single-celled organisms. The microbiome is made up of all of the microbiota's genes. The microbiota has a considerable impact on human health, and abnormalities can lead to diseases including Parkinson's and inflammatory bowel disease.
Such interdependencies must first be understood to design microbiome-targeted treatments and forecast microbiome-drug interactions. This is where artificial intelligence (AI) comes in. More than 1000 people's individual parameters are considered in the search for patterns. The key algorithms in that stage are supervised machine learning and reinforcement learning.
● Prediction, diagnosis and treatment of mental disorders
It is vital to diagnose mental illnesses and intervene as soon as feasible. There are two basic approaches: consumer-facing apps that detect disorders and diagnostic tools for psychiatrists. Consumer apps are usually conversational chatbots that have been augmented using machine learning algorithms. The software analyses the consumer's spoken language and provides assistance recommendations.
● Predicting heart failures
Pre-existing illnesses are common in people who suffer from heart failure. Telemedicine systems are frequently used to monitor and consult patients, and mobile health data such as blood pressure, body weight, and heart rate are recorded and transferred. The majority of prediction and prevention systems are based on set rules, which might lead to a higher frequency of false alarms. Too many false alarms result in increased health costs and decreased patient faith in the prediction because most alerts result in hospital admission. To eliminate false alarms, a classifier based on Naïve Bayes has been finally developed.