Progress and Application of Machine Learning in Bioinformatics

IEEE Computer Society Team
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machine learning for bioinformatics

2.5 million terabytes of data are created daily, and 90% of existing data was created in the last two years. Data centers and storage represent 1.8% of the world’s global electricity, and this number will only continue to increase as consumers and businesses alike generate more and more data every day.

One of the fields that take up the most data is life sciences, health, and bioinformatics. Considering individual patient data, hospital records, imaging, wearables, apps, and more, big data use large sets of data to unearth useful patterns of information. Specifically for bioinformatics, developing this new computationally focused life sciences field requires deeper investment in artificial intelligence and machine learning to perform massive amounts of research and testing that human scientists couldn’t do in their entire lifetime.

As the exploration and development of life sciences dominate the 21st century, bioinformatics, the science of collecting and analyzing complex biological data like DNA codes, offers scientists and researchers significant opportunities and challenges. Bioinformatics data is enormous; it involves using computer technology to disseminate biological data into actionable insights for future research and scientific learning. This large amount of highly complex data makes it extremely difficult for humans to interpret test results, so advanced computing technology is necessary.



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Enter the rising popularity of artificial intelligence and machine learning. Artificial intelligence has permeated the computer science field, and its usage is growing rapidly at a rate of 38.1% from 2022 to 2030. Artificial intelligence is a critical branch of computer science, and machine learning makes up a large portion of the artificial intelligence field.

Machine learning empowers computers to stimulate human learning behavior, allowing extensive data analysis to scale without human researcher constraints. Machine learning also builds on its own knowledge and is constantly learning and improving. Like AI, machine learning projects a considerable growth rate of 38.76%, and adoption has been promising, too; 50% of survey respondents to McKinsey Research said they implemented artificial intelligence in their business. While machine learning has been quickly adopted in business analytics and sales and marketing, the unique characteristics and requirements of data in bioinformatics — specifically the large amounts and complexity — make machine learning the perfect instrument to further the field of bioinformatics.

In “Application and Research Progress of Machine Learning in Bioinformatics,” the authors present the concepts of supervised learning, unsupervised learning, and semi-supervised learning in bioinformatics. These learning techniques use various types of input data; for example, a supervised learning technique of a decision tree categorizes information or makes predictions based on how a previous set of questions was answered. It is “supervised” in that machine learning resides in the background to help with learning.


Download “Application and Research Progress of Machine Learning in Bioinformatics”

Read the full article, “Application and research progress of machine learning in Bioinformatics,” in IEEE’s 2020 International Conference on Computer Vision, Image and Deep Learning. Learn more about the future of machine learning’s role in bioinformatics and how advanced technologies will drive the industry forward.