Deep learning is used in all spheres of life. It has its uses in all fields. It has a great impact on biomedical research. It’s like an intelligent computer that can improve your tasks with little help. It has changed the way scientists study medicine and diseases.
It has a major impact on genomics, a field of biology that investigates the organization of DNA into genes and the processes by which these genes are turned on or off within individual cells.
Researchers at the University of California, San Diego have formulated a new deep learning platform that can be quickly and easily adapted to fit various genomics projects. Hannah Carter, Ph.D., associate professor in the Department of Medicine at UC San Diego School of Medicine, said every cell has the same DNA, but the way the DNA is expressed changes the way the cell looks and behaves. the cells.
EUGENe uses modules and subpackages to facilitate essential functions within a genomics deep learning workflow. These functions include (1) extracting, transforming, and loading sequence data from various file formats; (2) instantiate, initialize, and train various model architectures; and (3) evaluate and interpret the behavior of the model.
While deep learning has the potential to provide valuable insights into the diverse biological processes that govern genetic variation, its implementation poses challenges for researchers who need broader computer science expertise. The researchers said the goal was to develop a platform that would allow genomics researchers to optimize their analysis of deep learning data, making it easier and more efficient to extract predictions from raw data.
Although only about 2% of the total genome is made up of genes that encode specific proteins, the remaining 98%, often called junk DNA due to its presumed lack of known function, plays a critical role in determining the timing, location and manner of production. that certain genes are activated. Understanding the functions of these noncoding sections of the genome has been a top priority for genomics researchers. Deep learning has proven to be a powerful tool to achieve this goal, although using it effectively can be difficult.
Adam Klie, Ph.D. A Carter Lab student and first author of the study, he said many existing platforms require many hours of coding and wrangling data. He noted that numerous projects require researchers to start their work from scratch, requiring expertise that may not be available to all laboratories interested in the field.
To evaluate its effectiveness, the researchers tested EUGENe by attempting to replicate the findings of three previous genomic studies that used a variety of sequencing data types. In the past, analyzing such diverse data sets would require integrating several different technology platforms.
EUGENe demonstrated remarkable flexibility, effectively replicating the results of each investigation. This flexibility highlights the platform’s ability to handle a wide range of sequencing data and its potential as an adaptable instrument for genomic research.
EUGENe shows adaptability to different types of DNA sequencing data and support for various deep learning models. The researchers aim to expand their reach to encompass a broader range of data types, including single-cell sequencing data, and plan to make Eugene accessible to research groups around the world.
Carter expressed excitement about the collaborative potential of the project. He said one of the interesting things about this project is that the more people use the platform, the better they will be able to do it over time, which will be essential as deep learning continues to evolve rapidly.
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Rachit Ranjan is a consulting intern at MarktechPost. He is currently pursuing his B.tech from the Indian Institute of technology (IIT), Patna. He is actively shaping his career in the field of artificial intelligence and data science and is passionate and dedicated to exploring these fields.
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