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MClarke1991/README.md

I am a Research Fellow with PIBBSS (Principles of Intelligent Behaviour in Biological and Social Systems). My current research focuses on the mechanistic interpretability of machine learning, specifically using sparse autoencoders. This work combines my interests in computational modeling and complex systems, now applied to understanding the inner workings of AI.

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.

Throughout my career, I have been keen to share my knowledge and expertise with others. As a mentor to postdocs, PhD students, Masters students, and undergraduates, I take pride in helping to guide and inspire the next generation of scientists. I look forward to continuing this mentorship in my new role, now focusing on the exciting field of AI interpretability.

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



⚡ Published Project 📰 Paper 💾 Code/Data
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
Executable Modelling Review Executable cancer models: successes and challenges NA
Views on AML Prognostic hallmarks in AML NA

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  1. gemma-2-2b-geography gemma-2-2b-geography Public

    Jupyter Notebook 1

  2. 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

  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/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