The role of machine learning in bioinformatics and biology

Machine learning (ML), a subset of artificial intelligence (AI) is advancing at a rapid pace in the digital era giving systems the ability to learn from data and perform tasks, and accurate decision-making without being explicitly programmed. The most sought-after ML apps in real-life are Google Maps' Traffic Prediction, Google Translate, Netflix movie recommendation, Amazon Alexa, Amazon's Recommendation Engine, Spam Detection in Gmail, and Tesla's Self-driving Cars, and the list of examples is far longer.

With the help of technologies and techniques like algorithms, deep learning, reinforcement learning, supervised learning, and unsupervised learning to imitate how the human brain works, ML is capable of making accurate decisions based on the trial and error method. The magic is when a toddler spends weeks or months to identify and be familiar with something, it takes only a few minutes for ML to master it.

So, ML has a wide range of applications, and the adaptation for bioinformatics and biology is indeed a revolutionary act. Bioinformatics is the application of computation and analysis techniques to capture and interpret biological data. This is an interdisciplinary field between computer science, mathematics, statistics, biology, and genetics. The intervention of ML’S data-driven methods helps give valuable insights from large biological datasets through mathematical calculations to big data which provide solutions for complex problems and large-scale experiments. As a result, ML makes features of complex datasets simplified and presents them in a manner that is easy to understand.

The Bioinformatics field is mainly used to identify genes and nucleotides to understand genetic diseases better and ML is used to develop codes, algorithms, and models that record and store biological data. On the other hand, computational biology is concerned with finding solutions to issues that arise from bioinformatics studies. These two subjects are used interchangeably and they are closely associated with information science, computer science, physics, and mathematics.

The following article will highlight the recent applications of ML in the fields of molecular medicine, personalized medicine, microbial genome applications, preventive medicine, drug development, and climate change studies with the help of some advanced deep-learning tools like Deepvariant, Atomwise algorithms, and Cell profile.

Healthcare

To improve the quality of well-being and healthcare, ML and AI are currently being used in hospitals. This approach will soon help analyze real-time data from multiple healthcare systems in different countries for making accurate clinical decisions. Even online clinics will help remote patients and also remote doctors to connect with each other and medical staff building a good rapport. For instance, an emotional health app called eQuoo is there to support your mental well-being and any patient can use it easily. These advancements ensure safer, time-saving, and more efficient services in the healthcare sector and the clinical workflow process.

Drug discovery and manufacturing

ML is widely used in the early stages of drug discovery processes. Apart from that, research and experiments are being conducted to invent new drugs for diseases. The expansion of this field is on the go.

Medical imaging and diagnosis

Using computer vision technology and other innovative tools, ML is used to generate 3D medical images for the detection of injuries and illnesses. The accuracy of these computer-based images allows doctors to come to conclusions.

Personalized medicine

Rather than making decisions based on a specific set of diagnoses or health history and limited genetic information of a patient, ML now allows doctors to detect patient data through predictive analytics and recommend customized treatment options. Not only that, but this will also pave the way for generating a wide range of other treatment options.

Stroke diagnosis

Recognition of patterns through algorithms to diagnose and recommend treatment for stroke patients is done and this field has experienced significant development thanks to ML during the last few years.

Accessing patient data, which lies within electronic records, paper charts, and other sources manually with human intervention was a hassle, and time-consuming, and the requirement of a large medical team was a must. But with the development of ML-enabled technologies such as Intel’s Analytics Toolkit, healthcare facilities can now make the most out of patient data. In conclusion, the execution of ML in the Bioinformatics field is commendable for the betterment of human beings and we are yet to experience more advancements and trends.