Alexandra Maslova

This study evaluates if ensembling improves feature attributions from sequence-to-activity models, finding that it enhances downstream applications like motif identification and variant prediction.

Hi-C-LSTM uses a recurrent neural network to learn low-dimensional representations of Hi-C data, enabling the identification of genomic elements that define chromatin conformation.

This paper introduces AI-TAC, a convolutional neural network that infers cell-type-specific chromatin activity from DNA sequences using an ATAC-seq atlas of immune cells.

This study profiles single-cell transcriptomes of mouse and hESC-derived endocrine progenitors to identify markers and cell-state populations relevant to beta-cell formation.