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@jfisher-lab

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MClarke1991/README.md

My current research focuses on understanding and predicting the safety and security of AI systems, applying my prior experience in mechanistic interpretability of both biological and computational networks.

Previously, I was a postdoc at the Cancer Institute at University College London, where I combined biology and computer science to predict cancer evolution and plan treatment programs to avert or overcome resistance. I completed my PhD at the University of Cambridge, exploring how computational network models could be used to find more effective combination treatments for breast cancer. As a postdoc at the Fisher Lab in the UCL Cancer Institute, I built upon this work to predict resistance mechanisms to radiotherapy and find the most effective patient-specific treatments.

💻 My website | 💼 My LinkedIn | ⚡ My Projects | 📰 My Papers | 💬 My Talks | ✉️ Get in touch



⚡ Published Project 📰 Paper 💾 Code/Data
Sparse AutoEncoder Latent Co-occurrence Compositionality and Ambiguity: Latent Co-occurrence and Interpretable Subspaces App, Code
Graph Neural Network Pathway Model Generation MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning Code
Order of mutations in evolution Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution MutationOrder
Combination Treatments for COVID-19 Executable network of SARS-CoV-2-host interaction predicts drug combination treatments COVID19
Combination Treatment for Myc-driven breast cancer Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling HeterogeneousBreastCancer
Melanoma Immunotherapy Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling Melanoma
Blood Cancer Evolution HOXA9 has the hallmarks of a biological switch with implications in blood cancers Analysis & Data
Lung Cancer Radiotherapy Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling Upon peer-reviewed publication
Executable Modelling Review Executable cancer models: successes and challenges NA
Views on AML Prognostic hallmarks in AML NA

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  1. sae_cooccurrence sae_cooccurrence Public

    Code for Compositionality and Ambiguity: Latent Co-occurrence and Interpretable Subspaces (Clarke et al., 2024)

    Jupyter Notebook 4

  2. jfisher-lab/MAGELLAN jfisher-lab/MAGELLAN Public

    Code for MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning (Clarke, Barker, Sun et al., 2025)

    Python 5 1

  3. JFisherLab/MutationOrder JFisherLab/MutationOrder Public

    MutationOrder is a tool to explore how the order in which mutations are acquired in an evolving cancer is constrained by the change in cell phenotypes that these mutations cause.

    R 3

  4. JFisherLab/COVID19 JFisherLab/COVID19 Public

    Network model .json file for the SARS-CoV-2 Host Interaction Model first described in Howell, Clarke, Reuschl et al., npj Digit. Med. 2022.

    2

  5. JFisherLab/Melanoma-LC JFisherLab/Melanoma-LC Public

    Network model .json file for the Langerhans Cell (LC), Melanoma and combined models first described in Howell, Davies et al., Science Advances 2023.

    2