The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.

"all data can be accessed in additional file 8"

"Declarations Ethics approval and consent to participateMice were housed in the Animal Facility of Research Center Principe Felipe (CIPF). This study was carried out following the European Union Guidelines for the Care (European Union Directive (2010/63/EU) and the guidelines for the Use of Laboratory Animals. The procedure was approved by the Committee of Ethics in Research of CIPF. Consent for publicationNot applicable. Competing interestsAuthors declare that no competing interests exist. Competing interests Authors declare that no competing interests exist."

"This work is supported by grants PID2020-117979RB-I00 from the Spanish Ministry of Science and Innovation, ACCI2018/29 from CIBER-ISCIII, PI2020/01305, PI22/00082 and project IMPaCT-Data IMP/00019 from the ISCIII, co-funded with European Regional Development Funds (ERDF), the grant “Large-scale drug repurposing in rare diseases by genomic Big Data analysis with machine learning methods” from the Fundación BBVA (G999088Q), the H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533), grant P18-RT-3471 from Consejeria de Salud y Consumo, Junta de Andalucia, and grant PIP-0087-2021 from Junta de Andalucía, co-funded with European Regional Development Funds (ERDF). The authors also acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020-DOC_00350) co-funded by the European Social Fund (FSE) 2014–2020. SV was funded by a Conselleria de Innovación, Universidades, Ciencia y Sociedad, GVA predoctoral contract (ACIF/2021/430).The authors thank the Research Center Principe Felipe and its staff for providing animal facilities, and Marta Llansola and Mª Carmen Castro for their technical help."