Semantically Rich Local Dataset Generation for Explainable AI in Genomics-Reference-Cited by-同舟云学术

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Semantically Rich Local Dataset Generation for Explainable AI in Genomics

Published:2024-07-14 Issue: Volume:24 Page:267-276
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
language:
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Author:

Barbosa Pedro¹²^ORCID,Savisaar Rosina³^ORCID,Fonseca Alcides⁴^ORCID

Affiliation:

1. LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal

2. Instituto de Medicina Molecular João Lobo Antunes, Lisbon, Portugal

3. Mondego Science, Paris, France

4. LASIGE, Department of Informatics; Faculty of Sciences, University of Lisbon, Lisbon, Portugal

Funder

Fundação para a Ciência e Tecnologia

Publisher

ACM

Reference62 articles.

1. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

2. Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, and D. Sculley. 2020. Population-Based Black-Box Optimization for Biological Sequence Design. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 324--334.

3. Effective gene expression prediction from sequence by integrating long-range interactions

4. Base-resolution models of transcription-factor binding reveal soft motif syntax

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