Publications

Journal Articles

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single-Cell Transcriptomes

Published in Advanced Intelligent Systems, 2026

Here, we present FTGRN (Foundation Transformer for Gene Regulatory Networks), a universal framework for GRN inference based on a pretrain–finetune paradigm. FTGRN integrates gene embeddings derived from Generative Pre-trained Transformer-4 (GPT-4) with publicly available chromatin immunoprecipitation sequencing (ChIP-seq) data to construct a regulatory knowledge base for pretraining a Transformer-based graph neural network.

Recommended citation: GuangzhengWeng, HyobinKim, PatrickMartin, JunilKim, Tae-HyungKim, Gi-HoonNam, DonghaKim, Kyoung JaeWon. Adv. Intell. Syst.. 2025; 000, e202500781. https://doi.org/10.1002/aisy.202500781
Download Paper

Multi-scale and multi-context interpretable mapping of cell states across heterogenous spatial samples.

Published in Nature Communications, 2025

There is a growing demand for methods that can effectively align and compare spatial data in the absence of obvious visual correspondence. To address this challenge, we developed an interpretable cell mapping strategy by considering spatial context at various scales.

Download Paper

Ovarian tumor cells gain competitive advantage by actively reducing the cellular fitness of microenvironment cells

Published in Nature Biotechnology, 2024

Here we demonstrate that the expression of Flower Lose and reduced microenvironment fitness is not a pre-existing condition but, rather, a cancer-induced phenomenon

Recommended citation: Madan, E., Palma, A.M., Vudatha, V. et al. Ovarian tumor cells gain competitive advantage by actively reducing the cellular fitness of microenvironment cells. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02453-3
Download Paper

Integrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference

Published in Advanced Science, 2024

By combining LLMs ability to distillate biological knowledge from text and deep learning methodologies capturing complex patterns in gene expression data, GRNPT overcomes the limitations of traditional GRN inference methods and enables more accurate and comprehensive understanding of gene regulatory dynamics.

Recommended citation: G. Weng, P. Martin, H. Kim, K. J. Won, Integrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference. Adv. Sci. 2024, 2409990. https://doi.org/10.1002/advs.202409990
Download Paper

Data enhancement in the age of spatial biology

Published in Advances in Cancer Research, 2024

This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.

Download Paper

Identification of mammalian transcription factors that bind to inaccessible chromatin

Published in Nucleic Acid Research, 2023

Here, we systematically investigated the binding mechanism of hundreds of TFs by analysing ChIP-seq data with our explainable statistical model, ChIPanalyser. This tool uses as inputs the DNA sequence binding motif; the capacity to distinguish between strong and weak binding sites; the concentration of TF; and chromatin accessibility.

Download Paper

ChatGPT and large language models in academia: opportunities and challenges

Published in BioData Mining, 2023

In this editorial, we discuss this technology from the academic’s perspective with regard to its limitations and utility for academic writing, education, and programming

Recommended citation: Meyer, J.G., Urbanowicz, R.J., Martin, P.C.N. et al. ChatGPT and large language models in academia: opportunities and challenges. BioData Mining 16, 20 (2023). https://doi.org/10.1186/s13040-023-00339-9
Download Paper

CellNeighborEX: deciphering neighbor‐dependent gene expression from spatial transcriptomics data

Published in Molecular Systems Biology, 2022

CellNeighborEX identifies diverse gene sets associated with partnering cell types

Download Paper

Vesalius: high‐resolution in silico anatomization of spatial transcriptomic data using image analysis

Published in Molecular Systems Biology, 2022

Vesalius uniquely detected territories composed of multiple cell types and successfully recovered tissue structures in high‐resolution ST data including in mouse brain, embryo, liver, and colon.

Download Paper

Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework

Published in Computational and Structural Biotechnology Journal, 2022

We present ChIPanalyser, a user-friendly, versatile and powerful R/Bioconductor package predicting and modelling the binding of TFs to DNA. ChIPanalyser performs similarly to state-of-the-art tools, but is an explainable model and provides biological insights into binding mechanisms of TFs.

Download Paper

Patrick CN Martin, PhD

Publications

Journal Articles