Summary of Semantically Rich Local Dataset Generation For Explainable Ai in Genomics, by Pedro Barbosa et al.
Semantically Rich Local Dataset Generation for Explainable AI in Genomics
by Pedro Barbosa, Rosina Savisaar, Alcides Fonseca
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Genomics (q-bio.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning study demonstrates that black box deep learning models trained on genomic sequences can accurately predict gene regulatory mechanism outcomes. To achieve this, interpretable surrogate models are developed for local explanations, enabling novel biological insights and supporting biomedical applications. However, generating a dataset in the neighborhood of the input while introducing semantic variability is challenging due to the complex DNA sequence-to-function relationship. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers used deep learning models to predict gene regulatory mechanisms based on genomic sequences. They found that these models are good at making predictions, but it’s hard to understand why they made those predictions. To solve this problem, they created simpler models that can explain how they make predictions for specific instances. This helps scientists better understand the biology of genes and how they work. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
