Erfaneh Gharavi

The paper introduces scEmbed, a framework for fast clustering and cell-type annotation of scATAC-seq data using pre-trained embeddings, showing improved performance and transferability.

The paper introduces four metrics to evaluate unsupervised embeddings of genomic regions and provides guidelines for reliable embedding learning.

The paper develops a system for fast and flexible genomic interval retrieval using joint representation learning of region sets and metadata.

The paper introduces a method to represent genomic region sets as embeddings using a modified word2vec approach, showing it effectively captures biological associations in lower dimensions.