Author

Fatemeh Amrollahi

Emory UniversityORCID 0009-0009-6617-0241Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Assay:whole-exome sequencing· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Chengliang Dong· 1 shared Congzheng SongiD· 1 shared Daniel Jay BratiD· 1 shared David A. GutmaniD· 1 shared Jose Enrique Velazquez Vega· 1 shared Joshua E. LewisiD· 1 shared Lee CooperiD· 1 shared Mohamed T. AmgadiD· 1 shared

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2017-2017 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Chengliang Dong

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1 paper

Recent·Most cited

BioinformaticsScientific ReportsPeer-reviewedSep 11, 2017

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models

Safoora Yousefi, Fatemeh Amrollahi, Mohamed T. Amgad…·Lee Cooper

This paper applies deep learning and Bayesian optimization to predict cancer clinical outcomes from genomic data, showing improved prognostic accuracy through transfer learning and providing an open-source framework.

234 citationstop 1%

whole-genome sequencingwhole-exome sequencing

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