MIT scientists build a system that can generate AI models for biology research🧬

As I discussed before, now you know tweaking DNA is the biggest thing we have to achieve to make Mars' colonization dream reliable. So, we have to deal with huge biological data and Artificial Intelligence is already becoming a vital component of space travel and its exploration and also from now, in biological data modeling. Here there is a great discovery done by the MLT scientists. It is BioAutoMATED.

BioAutoMATED, an open-source, automated machine-learning platform, aims to help democratize artificial intelligence for research labs.

Biology research is increasingly reliant on machine learning (ML) to analyze large datasets and make predictions. However, building and deploying ML models can be a time-consuming and challenging process, even for experienced researchers.

To address this challenge, MIT scientists developed BioAutoMATED, an automated ML platform for biology research. BioAutoMATED can automatically select and build an appropriate ML model for a given dataset, and even take care of the laborious task of data preprocessing, whittling down a months-long process to just a few hours.

They evaluated BioAutoMATED on a variety of biological datasets, and found that it was able to achieve state-of-the-art performance on several tasks, including protein function prediction, drug discovery, and image classification.

BioAutoMATED is an open-source platform that is freely available to researchers worldwide. They believe that BioAutoMATED will make it easier for biologists to adopt ML and accelerate the pace of discovery in biology.

The fundamental language of biology is based on sequences. Biological sequences such as DNA, RNA, proteins, and glycans have the amazing informational property of being intrinsically standardized, like an alphabet. A lot of AutoML tools are developed for text, so it made sense to extend it to biological sequences.

BioAutoMATED’s repertoire of supervised ML models includes three types:

🟣Binary classification models (dividing data into two classes)

🟣Multi-class classification models (dividing data into multiple classes), and

🟣Regression models (fitting continuous numerical values or measuring the strength of key relationships between variables)

BioAutoMATED is even able to help determine how much data is required to appropriately train the chosen model.

Conducting novel and successful experiments at the intersection of biology and machine learning can cost a lot of money. Currently, biology-centric labs need to invest in significant digital infrastructure and AI-ML trained human resources before they can even see if their ideas are poised to pan out. The invention team wants to lower these barriers for domain experts in biology.

The open-source code is publicly available and, researchers emphasize, it is easy to run.

https://github.com/jackievaleri/BioAutoMATED

They want to prime the biological research community and generate awareness related to AutoML techniques, as a seriously useful pathway that could merge rigorous biological practice with fast-paced AI-ML practice better than it is achieved today.

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