Chuan-Sheng Foo

This paper introduces a multimodal framework that links biosynthetic gene clusters and natural products by integrating genomic and chemical data through shared embeddings, enabling bidirectional retrieval and strain selection.

The paper uses a deep learning model to analyze ChIP-seq data and identifies binding motifs for CTCF zinc fingers 1-2, revealing GAG and ATT motifs.

This study presents whole-genome sequencing data from 4,810 Singaporeans across three ethnic groups, revealing genetic structure, admixture, and improved imputation for Asian and Oceanian populations.

The paper develops a method to predict RNA secondary structure from structure probing data without using sequence information.