Version 1
: Received: 11 April 2024 / Approved: 12 April 2024 / Online: 12 April 2024 (09:59:40 CEST)
How to cite:
Mamidi, T.K.K.; Wilk, B.M.; Gajapathy, M.; Worthey, E.A. DITTO: An Explainable Machine-Learning Model for Transcript-Specific Variant Pathogenicity Prediction. Preprints2024, 2024040837. https://doi.org/10.20944/preprints202404.0837.v1
Mamidi, T.K.K.; Wilk, B.M.; Gajapathy, M.; Worthey, E.A. DITTO: An Explainable Machine-Learning Model for Transcript-Specific Variant Pathogenicity Prediction. Preprints 2024, 2024040837. https://doi.org/10.20944/preprints202404.0837.v1
Mamidi, T.K.K.; Wilk, B.M.; Gajapathy, M.; Worthey, E.A. DITTO: An Explainable Machine-Learning Model for Transcript-Specific Variant Pathogenicity Prediction. Preprints2024, 2024040837. https://doi.org/10.20944/preprints202404.0837.v1
APA Style
Mamidi, T.K.K., Wilk, B.M., Gajapathy, M., & Worthey, E.A. (2024). DITTO: An Explainable Machine-Learning Model for Transcript-Specific Variant Pathogenicity Prediction. Preprints. https://doi.org/10.20944/preprints202404.0837.v1
Chicago/Turabian Style
Mamidi, T.K.K., Manavalan Gajapathy and Elizabeth A. Worthey. 2024 "DITTO: An Explainable Machine-Learning Model for Transcript-Specific Variant Pathogenicity Prediction" Preprints. https://doi.org/10.20944/preprints202404.0837.v1
Abstract
Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
