[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128629-en":3,"doc-seo-128629-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128629,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Employing a Machine Learning Approach in Precision Oncology to Predict PIK3CA Functional Status and Identify Phenocopying Variants of Deleterious PIK3CA Mutations - Senior Thesis","The proposal outlines a modular machine learning pipeline to detect malfunctioning cancer genes and pathways using patient transcriptome data, aiming to reveal hidden responders in precision oncology. Focused on the PI3K/AKT/mTOR pathway, it uses integrated RNA-seq, copy number, and mutation features to predict PIK3CA functional status through learned gene-specific weights. Classifier scores are then tested on cell line datasets with Buparlisib and Copanlisib pharmacological profiling to link predicted functionality with PI3K inhibitor sensitivity, and to support phenocopying variant biomarker discovery.","Claremont Colleges  \nScholarship @ Claremont  \n\n| CMC Senior Theses | CMC Student Scholarship |\n| --- | --- |\n| 2024\u003Cbr>Employing a Machine Learning Approach in Precision Oncology to Predict PIK3CA Functional Status and Identify Phenocopying Variants of Deleterious PIK3CA Mutations\u003Cbr>Javier Castillo\u003Cbr>Follow this and additional works at: [https://scholarship.claremont.edu/cmc_theses](https://scholarship.claremont.edu/cmc_theses)\u003Cbr> Part of the Disease Modeling Commons, Genetic Processes Commons, Neoplasms Commons, and the Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons |  |\n\nRecommended Citation  \nCastillo, Javier, \"Employing a Machine Learning Approach in Precision Oncology to Predict PIK3CA Functional Status and Identify Phenocopying Variants of Deleterious PIK3CA Mutations\" (2024) . CMC Senior Theses. 3580.  \n[https://scholarship.claremont.edu/cmc_theses/3580](https://scholarship.claremont.edu/cmc_theses/3580)  \nThis Open Access Senior Thesis is brought to you by Scholarship@Claremont. It has been accepted for inclusion in this collection by an authorized administrator. For more information, please contact [scholarship@claremont.edu](scholarship@claremont.edu).  \nEmploying a Machine Learning Approach in Precision Oncology to Predict PIK3CA Functional Status and Identify Phenocopying Variants of Deleterious PIK3CA Mutations  \nA Thesis Presented  \nby  \nJavier Castillo  \nTo the Keck Science Department  \nof  \nClaremont McKenna, Scripps, and Pitzer Colleges In Partial Fulfillment of  \nThe Degree of Bachelor of Arts  \nSenior Thesis in Biology  \nApril 22, 2024  \nTABLE OF CONTENTS  \nABSTRACT....................................................................................................................................2  \nINTRODUCTION......................................................................................................................... 3  \nThe PI3K/AKT/mTOR Pathway................................................................................................4  \nPI3K Signaling in Cancer.......................................................................................................... 6  \nPI3K Inhibitors...........................................................................................................................8  \nMachine Learning Models of Gene Expression.........................................................................9  \nLogistic Regression in Medical and Clinical Studies.............................................................. 11  \nSpecific Aims........................................................................................................................... 12  \nMETHODS................................................................................................................................... 13  \nTraining Pan-Cancer Classifier................................................................................................ 13  \nEvaluating Pan-Cancer Classifier............................................................................................ 16  \nPan-Cancer Classifier Benchmark Analysis............................................................................ 17  \nCell Line Validation................................................................................................................. 18  \nEXPECTED RESULTS............................................................................................................... 19  \nEvaluating Pan-Cancer Classifier............................................................................................ 19  \nPan-Cancer Benchmark Analysis............................................................................................ 22  \nCell Line Validation................................................................................................................. 24  \nFUTURE DIRECTIONS.....................................................................................","cbCain2cw8koP3YX","https://ap.wps.com/l/cbCain2cw8koP3YX","pdf",2494899,1,32,"English","en",105,"# ABSTRACT\n# INTRODUCTION\n## The PI3K/AKT/mTOR Pathway\n## PI3K Signaling in Cancer\n## PI3K Inhibitors\n## Machine Learning Models of Gene Expression\n## Logistic Regression in Medical and Clinical Studies\n## Specific Aims\n# METHODS\n## Training Pan-Cancer Classifier\n## Evaluating Pan-Cancer Classifier\n## Pan-Cancer Classifier Benchmark Analysis\n## Cell Line Validation\n# EXPECTED RESULTS\n## Evaluating Pan-Cancer Classifier\n## Pan-Cancer Benchmark Analysis\n## Cell Line Validation\n# FUTURE DIRECTIONS\n# ACKNOWLEDGEMENTS\n# REFERENCES","[{\"question\":\"What problem does the machine learning approach address in precision oncology?\",\"answer\":\"It targets malfunctioning genes and pathways in cancer by leveraging transcriptome information that is underused in precision oncology to help identify hidden responders.\"},{\"question\":\"How does the method predict PIK3CA functional status?\",\"answer\":\"It integrates RNA-seq, copy number, and mutation data from tumors and uses learned gene-specific weights to infer the functional status of PIK3CA within the PI3K/AKT/mTOR pathway.\"},{\"question\":\"How will classifier predictions be evaluated using drug response data?\",\"answer\":\"The trained classifier is applied to cell line datasets that include pharmacological profiling for PI3K inhibitors Buparlisib and Copanlisib, testing whether classifier scores correlate with sensitivity to these drugs.\"}]","Employing a Machine Learning Approach in Precision Oncology to Predict PIK3CA Functional Status and Identify Phenocopying Variants of Deleterious PIK3CA Mutations - 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