[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116999-en":3,"doc-seo-116999-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116999,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Cancer Immunotherapy - Lillian McHugh Poster","Machine learning cancer immunotherapy research applies numerical simulations of a dynamical cancer invasion model coupled with an immune system response to assess treatment success. Parameters are identified, then randomly sampled to generate stimulated datasets covering 60 days with balanced cancer/no-cancer outcomes. Predictive models are trained on these datasets and evaluated on new inputs using classifier accuracy. Future work targets real-world predictions by expanding model parameters using referenced TGF-β and exploring alternative algorithms.","Providence College  \nDigitalCommons@Providence  \n\n| Mathematics & Computer Science Student Scholarship | Mathematics & Computer Science |\n| --- | --- |\n| 4-27-2023\u003Cbr>Machine Learning Cancer Immunotherapy\u003Cbr>Lillian McHugh Providence College\u003Cbr>Follow this and additional works at: [https://digitalcommons.providence.edu/computer_science_students](https://digitalcommons.providence.edu/computer_science_students) |  |\n\nMcHugh, Lillian, \"Machine Learning Cancer Immunotherapy\" (2023) . Mathematics & Computer Science Student Scholarship. 11.  \n[https://digitalcommons.providence.edu/computer_science_students/1](https://digitalcommons.providence.edu/computer_science_students/1)1  \nThis Poster is brought to you for free and open access by the Mathematics & Computer Science at DigitalCommons@Providence. It has been accepted for inclusion in Mathematics & Computer Science Student Scholarship by an authorized administrator of DigitalCommons@Providence. For more information, please contact [dps@providence.edu](dps@providence.edu).  \nMachine Learning cancer immunotherapy  \nJoseph L. Shomberg, Ph. D and Lillian McHugh  \n\n| \u003Cbr>Project desrciption | \u003Cbr>overview | Equilibrium and eigenvalues for |\n| --- | --- | --- |\n|  | We worked to create a set of numerical simulations | each model |\n| The a im of our research is to establish the viability of machine learning in relation to the prediction of TGF-B cancer treatment success . Our method begins by analying the dynamical system model of cancer invasion with subsequent immune system response . Important model parameters are identified. Numerical solutions are then run with suitably randomly chosen parameters to create a stimulated dataset that is used to train different predictive machine learning models . When presented with, new data the classifiers accuracy is reported. In the future we look forward to applying real world data to our system . | for a cancer immune model. This was accomplished with the help of a diagnostic classifier which we accurately predicts if cancer is present sixty days after discovery.\u003Cbr>\u003Cbr>\u003Cbr>important terms\u003Cbr> exponential growth & decay\u003Cbr> y'= ry, y (0)=y 0\u003Cbr> logistic equation\u003Cbr> y'= ry(1-(1/B)y) , y (0)=y 0\u003Cbr> predator-prey system\u003Cbr> x'= ax(1-(b/a)y) , y'=-cy(1-(d/c)x) , x (0)=x 0 , y (0)=y 0 |  Exponential growth/decay, y=0 is a fixed point equilibri u . Attracting when r \u003C0 and repelling when r > 0 .\u003Cbr> logistic equation, y=0 and y=B are fixed points . When r > 0 and 0\u003C|y 0|\u003C B, y' is increasing and y=B. In this case y=0 is repelling and y=B is attracting. When r \u003C0, these roles are reversed.\u003Cbr> Predator-prey system, there are two fixed points (x=0, y=0) and (x= c/d, y= a/b) . For (0, 0), the eigenvalues of the Jacobian {-c, a} |\n\nMathematical Model  \nmachine learning results  \n We produced different datasets to match classifier to train . Datasets include 3000 to 8000 numerical solutions . In each simulation the parameter is changed slightly, while the initial conditions remain the same . The intervals produce 50% cancer and 50% no cancer over the course of 60 days  \nMachine learning Results  \nImprovements  \n Introducing real-world data  \n Do we have the ability to make real-world predictions using real data?  \n Expand the models real-world data  \n this could be accomplished by using the equations found in the article \"TFG-B inhibition can overcome cancer primary resistance to PD-1 blockade: a mathematical model.\"  \n Try other classification algorithms ( example: regression tree)  \n How does the accuracy of this algorithm compare to the other algorithms that have been applied  \n1 . References  \na . Ching Shan Chou and Avner Friedman, Introduction to Mathematical Biology: Modeling, Analysis, and Simulations, Springer Undergraduate Texts in Mathematics and Technology, 1st Edition (2016), Springer  \nb. Steven H. Strogatz, Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering, Studies in Nonlinearity, 2 nd Edition","cbCaimGboIBjz5aT","https://ap.wps.com/l/cbCaimGboIBjz5aT","pdf",357867,1,2,"English","en",105,"# Project description\n## Overview and method\n## Important terms and models\n# Mathematical model and results\n## Machine learning results\n## Improvements and future work\n# References and conclusion","[{\"question\":\"What is the main goal of the research?\",\"answer\":\"To establish the viability of machine learning for predicting TGF-β cancer treatment success based on cancer dynamics and immune response modeling.\"},{\"question\":\"How are the datasets generated for training the classifiers?\",\"answer\":\"Numerical simulations run with randomly chosen parameters while initial conditions remain fixed, producing datasets where outcomes are balanced across a 60-day period.\"},{\"question\":\"Which classifiers are used and what accuracies are reported?\",\"answer\":\"A kNN algorithm reports about 81% accuracy, while a Keras classifier reports about 98.65% (shown as ~98.7%) accuracy.\"}]","Machine Learning Cancer Immunotherapy - 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