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
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2024
• Subjects: Medicine, Health and Life Sciences; Malaria; Parasites
• Online Access: https://hdl.handle.net/10356/173875
• Institution: Nanyang Technological University

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...