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

"the source code for running and training malariased models is available at https://github com/charleywang/malariased [ 47 ] and zenodo ( https://doi org/10 5281/zenodo 8336741 [ 48 ]). availability of data and materials malariased was trained in atac-seq profiles (geo: gse104075"

"source code for enformer is available at github https://github com/google-deepmind/deepmind-research/tree/master/enformer [ 46 ].; its source code is available under the mit open-source license.; the source code for running and training malariased models is available at https://github com/charleywang/malariased [ 47 ] and zenodo ( https://doi org/10 5281/zenodo 8336741 [ 48 ])."

"Declarations Ethics approval and consent to participateNot applicable. Consent for publicationNot applicable. Competing interestsThe authors declare that they have no competing interests. Competing interests The authors declare that they have no competing interests."

"Funding This work was supported by the National Institutes of Health grant R01 AI117017 (J.H.A.) and U19 AI089672 (C.L.). This work was supported by the internal award of the College of Public Health at the University of South Florida."