Obtaining genetics insights from deep learning via explainable artificial intelligence
By Gherman Novakovsky, Nick C. Dexter, Maxwell W. Libbrecht, Wyeth W. Wasserman, S. Mostafavi. Nature reviews genetics, 2022
351 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5c66aef6f4eb4f2d76199a9abdd591801c804079
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Romain Blanc-Mathieu
- Wyeth W. Wasserman
- Aziz Khan
- Nicolas Langren'e
- Shengxin Zhu
- Banghao Chen
- Zhaofeng Zhang
- Anthony Mathelier
In Molecular Biology
- Anthony Mathelier
- Wyeth W. Wasserman
- Albin Sandelin
- François Parcy
- Oriol Fornes
- Jeanne Chèneby
- Aziz Khan
- Romain Blanc-Mathieu
Citations in the corpus
- Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review (2023, background): “Enhanced explainability not only fosters greater trust in AI systems but also promotes their integration across diverse industries [90].”
- JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles (2023, positive): “The high quality of the modelling and improved methods to interpret the deep learning models make them attractive to decipher the cis-regulatory code ( 53 ).”