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TECHNOLOGY AND CODE article

Front. Neurosci.
Sec. Neurodevelopment
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1400412
This article is part of the Research Topic Multiomics Approaches for Understanding Autism Spectrum Disorder View all 3 articles

Deriving comprehensive literature trends on multi-omics analysis studies in Autism Spectrum Disorder using literature mining pipeline

Provisionally accepted
Dattatray Mongad Dattatray Mongad Indhypriya Subramanian Indhypriya Subramanian Anamika Krishanpal, PhD Anamika Krishanpal, PhD *
  • Persistent Systems (India), Pune, Maharashtra, India

The final, formatted version of the article will be published soon.

    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 towards 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 like 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.

    Keywords: Classification, Summarization, Topic Modeling, Generative AI, NER Font: (Default) +Body (Calibri), 11 pt, Not Bold Normal

    Received: 22 Mar 2024; Accepted: 26 Jul 2024.

    Copyright: © 2024 Mongad, Subramanian and Krishanpal, PhD. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Anamika Krishanpal, PhD, Persistent Systems (India), Pune, Maharashtra, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.