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Identify potential targets for SARS-CoV-2 using multimodal data analysis

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Researchers identify new strategies and software for analyzing multiple types of data together to find effective targets for SARS-CoV-2.

Sonica Tiagi

Dr. Sonica Tiagi, Agenct Senior
Department lecturer
Infection

SARS-CoV-2, more commonly referred to as the coronavirus, has been the topic of thousands of research studies in the last 15 months. Therefore, it is expected that we will know how this virus works and how to treat COVID-19, the disease. Cause. But that’s not the case. Despite the large amount of experimentation and data available, the complex biology of the coronavirus SARS-CoV-2 is not yet fully understood.

The biology of this virus is so complex that researchers are still working on identifying target molecules for drug discovery. Dr. Sonica Tiagi, Infectious disease department, paper Released on May 24th. She said, “We have a long way to go from developing a drug that can cure COVID-19 disease. What we know is that the new drug needs to stop the function of the virus. I’m sick. “

Dr. Tyagi worked with colleagues at the University of Melbourne to develop new strategies and software to analyze multiple types of data together to identify effective targets.

Dr. Tyagi said that these data are of multiple types (or “multimodal”) and are very complex, with advanced machine learning to extract relevant information from other meaningless or “noise”. He said he needed a way.

“Existing research focuses on the analysis of a single data type that captures only small subset changes in molecular distress in the body caused by the virus. We have a systematic understanding of the disease. We will provide and propose a new strategy that can capture the overall view. Elucidation of the molecular mechanism of SARS-CoV-2.

“We worked with a colleague at the University of Melbourne to integrate multimodal data and find interconnects between data features. We used strongly correlated features to identify reliable drug discovery targets. . “

Their approach to analyzing multimodal data in parallel to highlight the interrelationships of disease-causing biomolecules can be used as an open access tool for the research community to reuse and apply to future datasets. ..

You can contact Dr. Sonica Tiagi [email protected]

reference

Tyrone Chen, Mercy Philip, Kim Ann Le Khao, Sonica Tiagi. COVID-19 Multimodal data harmonization approach for drug target discovery. Bioinformatics briefing. https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbab185/6279836

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