Text Mining to Understand Disease-Causing Gene Variants
Abstract
:1. Introduction
1.1. Background
1.2. Motivation
1.3. Objectives
2. Literature Review
2.1. Online Resources
2.1.1. Databases
2.1.2. Variant Classification Tools
2.1.3. Pathway Analysis Tools
2.2. Text Mining
2.3. Large Language Models
3. Examples and Case Studies
3.1. Autism Spectrum Disorder as an Example of the Implications of Genetic Variants
3.2. The Automated Curation
3.3. UniProt Database Curation
3.4. iPTMnet Database Curation
3.5. Arranging the Information
3.6. Knowledge Retrieval
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix A.1. HTATSF1
Appendix A.2. OR6C65
Appendix A.3. ITIH6
Appendix A.4. DDX26B
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Nezamuldeen, L.; Jafri, M.S. Text Mining to Understand Disease-Causing Gene Variants. Knowledge 2024, 4, 422-443. https://doi.org/10.3390/knowledge4030023
Nezamuldeen L, Jafri MS. Text Mining to Understand Disease-Causing Gene Variants. Knowledge. 2024; 4(3):422-443. https://doi.org/10.3390/knowledge4030023
Chicago/Turabian StyleNezamuldeen, Leena, and Mohsin Saleet Jafri. 2024. "Text Mining to Understand Disease-Causing Gene Variants" Knowledge 4, no. 3: 422-443. https://doi.org/10.3390/knowledge4030023