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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| Format: | Journal Article |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Online Zugang: | https://hdl.handle.net/10356/173875 |