Previously, I completed my B.S. in CS from Georgia Tech specializing in the machine learning and theory threads. There, I worked on graph representation learning for spatial transcriptomics and was incredibly fortunate to have been advised by Xiuwei Zhang (GT), Jian Ma (CMU), and Ruochi Zhang (Broad).
Higher-order genetic interactions, which have profound implications for understanding the molecular mechanisms of phenotypic variation, remain poorly characterized. Most studies to date have focused on pairwise interactions, as designing high-throughput experimental screenings for the vast combinatorial search space of higher-order molecular interactions is dauntingly challenging. Here, we develop DANGO, a computational method based on a self-attention hypergraph neural network, designed to effectively predict higher-order genetic interaction among groups of genes. As a proof-of-concept, we provide comprehensive predictions for over 400 million trigenic interactions in the yeast S. cerevisiae, significantly expanding the quantitative characterization of such interactions. Our results demonstrate that DANGO accurately predicts trigenic interactions, uncovering both known and novel biological functions related to cell growth. We further incorporate protein embeddings and model uncertainty scoring to enhance the biological relevance and interpretability of the predicted interactions. Moreover, the predicted interactions can serve as powerful genetic markers for growth response under diverse conditions. Together, DANGO enables a more complete map of complex genetic interactions that impinge upon phenotypic diversity.Competing Interest StatementThe authors have declared no competing interest.
MLSB
DiffRNAFold: Generating RNA Tertiary Structures with Latent Space Diffusion
Abstract RNA molecules provide an exciting frontier for novel therapeutics. Accurate determination of RNA structure could accelerate development of therapeutics through an improved understanding of function. However, the extremely large conformation space has kept the RNA 3D structure space largely unresolved. Using recent advances in generative modeling, we propose DiffRNAFold, a latent space diffusion model for RNA tertiary structure design. Our preliminary results suggest that DiffRNAFold generated molecules are similar in 3D space to true RNA molecules, providing an important first step towards accurate structure and function prediction in vivo.
ISMB
CLARIFY: Cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics
Motivation: Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. Results: We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputs refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.
@article{Clarify,author={Bafna, Mihir and Li, Hechen and Zhang, Xiuwei},title={CLARIFY: Cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics},booktitle={31st Conference on Intelligent Systems for Molecular Biology},short={ISMB},year={2023},journal={Bioinformatics},volume={39},number={Supplement_1},pages={i484-i493},month=jun,press={https://www.cc.gatech.edu/news/award-winning-computer-models-propel-research-cellular-differentiation},publisher={Oxford University Press},appear={true},}