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.
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
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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
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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.
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.
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
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Published in Molecular Systems Biology, 2022
CellNeighborEX identifies diverse gene sets associated with partnering cell types
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.
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.