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

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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Bibliographic Details
Main Authors: 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
Other Authors: School of Biological Sciences
Format: Journal Article
Language:English
Published: 2024
Subjects:
Medicine, Health and Life Sciences
Malaria
Parasites
Online Access:https://hdl.handle.net/10356/173875

Internet

https://hdl.handle.net/10356/173875

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