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.
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 Molecular Systems Biology, 2022
CellNeighborEX identifies diverse gene sets associated with partnering cell types
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 biorxiv, 2024
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.
Recommended citation: Multi-scale and multi-context interpretable mapping of cell states across heterogenous spatial samples Patrick C.N. Martin, Wenqi Wang, Hyobin Kim, Henrietta Holze, Kyoung Jae Won bioRxiv 2024.08.31.610638; doi: https://doi.org/10.1101/2024.08.31.61063
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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 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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Analysis of plant photosynthetic efficiency data.
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ChIPanalyser: Predicting Transcription Factor Binding Sites
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Cancer Competition R package and python module.
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Vesalius: Dissecting Tissue Anatomy from Spatial Transcriptomic Data
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Bombadillo
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This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
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Open source publication
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From top down hierarchies to distrubuted social systems.
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A discussion on complex systems and critial tipping points
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A discussion on the concept of chrono-urbanism and 15 minute cities
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The link between geology and singing.