AI Article Synopsis
- Autism spectrum disorder (ASD) involves diverse brain connectivity issues that influence social behavior, with recent advancements in its genetic and molecular understanding thanks to a multi-omics approach.
- Significant amounts of biomedical literature data require manual curation, but machine learning and AI are making strides in automating this process for easier insight extraction.
- The developed literature mining pipeline utilizes topic modeling and generative AI to categorize scientific texts, enabling applications like knowledgebase creation and summarization, particularly focusing on multi-omics research related to ASD.
Article Abstract
Autism spectrum disorder (ASD) is characterized by highly heterogenous abnormalities in functional brain connectivity affecting social behavior. There is a significant progress in understanding the molecular and genetic basis of ASD in the last decade using multi-omics approach. Mining this large volume of biomedical literature for insights requires considerable amount of manual intervention for curation. Machine learning and artificial intelligence fields are advancing toward simplifying data mining from unstructured text data. Here, we demonstrate our literature mining pipeline to accelerate data to insights. Using topic modeling and generative AI techniques, we present a pipeline that can classify scientific literature into thematic clusters and can help in a wide array of applications such as knowledgebase creation, conversational virtual assistant, and summarization. Employing our pipeline, we explored the ASD literature, specifically around multi-omics studies to understand the molecular interplay underlying autism brain.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590066 | PMC |
| http://dx.doi.org/10.3389/fnins.2024.1400412 | DOI Listing |
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