MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites

Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSED, for predicting chromatin profiles in malaria pa...

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Detalles Bibliográficos
Autores principales: Wang, Chengqi, Dong, Yibo, Li, Chang, Oberstaller, Jenna, Zhang, Min, Gibbons, Justin, Pires, Camilla Valente, Xiao, Mianli, Zhu, Lei, Jiang, Rays H. Y., Kim, Kami, Miao, Jun, Otto, Thomas D., Cui, Liwang, Adams, John H., Liu, Xiaoming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577899/
https://www.ncbi.nlm.nih.gov/pubmed/37845769
http://dx.doi.org/10.1186/s13059-023-03063-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577899/
https://www.ncbi.nlm.nih.gov/pubmed/37845769
http://dx.doi.org/10.1186/s13059-023-03063-z

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