Nicholas Allen Baclig Abad

Evaluation and optimization of sequence-based gene regulatory deep learning models

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

Identifying, Prioritizing, and Visualizing Functional Promoter SNVs with the Recurrence-agnostic REMIND-Cancer Pipeline and pSNV Hunter

The paper introduces REMIND-Cancer Pipeline and pSNV Hunter to identify and prioritize functional promoter SNVs, including lowly recurrent and singleton mutations, using multi-omics data from the PCAWG dataset.

The promoter mutation paucity as part of the dark matter of the cancer genome

The paper identifies previously overlooked activating promoter mutations in cancer using a computational workflow applied to the PCAWG dataset, validating several candidates through luciferase assays.