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

• 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
• 其他作者: School of Biological Sciences
• 格式: Article
• 語言: English
• 出版: 2024
• 主題: Medicine, Health and Life Sciences; Malaria; Parasites
• 在線閱讀: https://hdl.handle.net/10356/173875

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...