[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118055-en":3,"doc-seo-118055-105":30,"detail-sidebar-cat-0-en-105":91},{"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},118055,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine learning approaches for personalized medicine - Ph.D. Thesis","Ph.D. thesis in Medical Sciences and Biotechnology evaluating machine learning models across multiple precision medicine tasks. The bioinformatics section discretizes gene expression levels to create interpretable granularity for biomarker analysis, including pipelines that infer survival and tumor stages from oncologic patient biomarkers. A public-health decision support system is demonstrated using the same dataset. Chemoinformatics experiments target drug toxicity, bioaccumulation prediction, and P450 enzyme bioactivity with spiking neural networks for structured compound modeling. Clinical precision medicine combines clinical variables from nearly 300 patients to forecast lymphedema risk after breast cancer therapy and releases free volumetry software. Additional work includes a Python library for biomedical equivalence testing and machine-learning tracking of octacalcium phosphate synthesis from merged XRD/FTIR descriptors, plus proteomics anomaly detection of aberrant protein expression in extracellular vesicle content.","Ph.D. Thesis  \nXXXVI cycle  \n“Machine learning approaches for personalized  \nmedicine”  \nPh.D. candidate : Mauro Nascimben Supervisor : Prof. Lia Rimondini  \nPh.D. Program in Medical Sciences and Biotechnology Department of Health Sciences  \nAcademic Discipline [Area 06-SSD med/50]  \nContents  \n1 Introduction 7  \n1.1 Overview of machine learning models for precision medicine ........ 10  \n1.2 In–silico ML models .............................. 14  \n1.3 Data mining and machine learning ...................... 21  \n2 Aim of the thesis 23  \n2.1 Chemoinformatics ............................... 24  \n2.2 Clinical precision medicine and risk stratification .............. 24  \n2.3 Bioinformatics’ biomarkers analysis ...................... 25  \n2.4 Biostatistics: equivalence analysis ....................... 26  \n2.5 Regenerative medicine: biomaterials production tracking .......... 26  \n2.6 Proteomics: anomaly expression identification ................ 27  \n2.7 Data sources .................................. 27  \n3 Chemoinformatics 29  \n3.1 Predictive toxicity ............................... 34  \n3.2 Bioaccumulation pathways prediction ..................... 39  \n3.3 P450 enzyme bioactivity prediction ...................... 40  \n3.4 Final remarks .................................. 43  \n4 Clinical precision medicine and risk stratification 45  \n4.1 Upper arm volumetry software ........................ 47  \n4.2 Hand volumetry algorithms .......................... 52  \n4.3 Algorithm–based post–breast cancer lymphedema risk stratification .... 53  \n4.4 Final remarks .................................. 56  \n5 Bioinformatics’ biomarkers analysis 59  \n5.1 Bladder cancer survival prediction ...................... 61  \n5.2 Bladder cancer tumor stage with survival prediction ............ 64  \n5.3 Machine learning based decision support system in oncology ........ 68  \n5.4 Final remarks .................................. 70  \n6 Biostatistics: equivalence analysis 71  \n6.1 equiv_med: a library for equivalence assessment .............. 72  \n6.2 Final remarks .................................. 80  \n7 Regenerative medicine: biomaterials production tracking 81  \n7.1 Octacalcium phosphate production ...................... 83  \nContents  \n7.2 ML for OCP production tracking ....................... 84  \n7.3 Final remarks .................................. 85  \n8 Proteomics: anomaly expression identification 87  \n8.1 Biomaterials’ proteomics in extracellular vesicles .............. 88  \n8.2 Application of anomaly detection to EV-related protein expression .... 89  \n8.2.1 Wet-lab experimental conditions ................... 91  \n8.2.2 Mass spectrum summary ........................ 91  \n8.2.3 Dry-lab experimental sequence .................... 92  \n8.3 Results ...................................... 96  \n8.4 Final remarks .................................. 97  \n9 Conclusions and future perspectives 99  \n9.1 Future perspectives ............................... 102  \n9.2 Personal Bibliography ............................. 104  \n9.2.1 Chemoinformatics ........................... 104  \n9.2.2 Clinical precision medicine ....................... 104  \n9.2.3 Bioinformatics ............................. 105  \n9.2.4 Biostatistics ............................... 105  \n9.2.5 Regenerative medicine ......................... 106  \n9.2.6 Proteomics ............................... 106  \nAcknowledgements 107  \nBibliography 109  \nContents  \nSummary (in english)  \nThe work carried out during the Ph.D. in Medical Sciences and Biotechnology course tested the application of machine learning models in several precision medicine topics. In bioinformatic biomarker analysis, the publications focused on discretizing the gene expression levels to obtain a manageable and insightful granularity. The works demonstrated novel analysis pipelines to detect survival and tumor stages from oncologic patients’ biomarkers. The same chapter presented a procedure for a public health decision support system ","cbCaiiaYS28yd1RY","https://ap.wps.com/l/cbCaiiaYS28yd1RY","pdf",9628283,1,119,"English","en",105,"# Introduction\n## Overview of machine learning models for precision medicine\n## In–silico ML models\n## Data mining and machine learning\n# Aim of the thesis\n## Chemoinformatics\n## Clinical precision medicine and risk stratification\n## Bioinformatics’ biomarkers analysis\n## Biostatistics: equivalence analysis\n## Regenerative medicine: biomaterials production tracking\n## Proteomics: anomaly expression identification\n## Data sources\n# Chemoinformatics\n## Predictive toxicity\n## Bioaccumulation pathways prediction\n## P450 enzyme bioactivity prediction\n# Clinical precision medicine and risk stratification\n## Upper arm volumetry software\n## Hand volumetry algorithms\n## Algorithm–based post–breast cancer lymphedema risk stratification\n# Bioinformatics’ biomarkers analysis\n## Bladder cancer survival prediction\n## Bladder cancer tumor stage with survival prediction\n## Machine learning based decision support system in oncology\n# Biostatistics: equivalence analysis\n## equiv_med: a library for equivalence assessment\n# Regenerative medicine: biomaterials production tracking\n## Octacalcium phosphate production\n## ML for OCP production tracking\n# Proteomics: anomaly expression identification\n## Biomaterials’ proteomics in extracellular vesicles\n## Application of anomaly detection to EV-related protein expression\n# Conclusions and future perspectives\n## Future perspectives","[{\"question\":\"What topics does the thesis cover in personalized medicine?\",\"answer\":\"It evaluates machine learning models for precision medicine, including chemoinformatics, clinical risk stratification, bioinformatics biomarker analysis, biostatistics equivalence testing, regenerative medicine tracking, and proteomics-based anomaly detection.\"},{\"question\":\"How are biomarker analyses approached in the bioinformatics chapter?\",\"answer\":\"Gene expression levels are discretized to obtain manageable granularity, enabling analysis pipelines that detect survival and tumor stage information from oncologic patient biomarkers.\"},{\"question\":\"Which clinical prediction problem is targeted, and how is it modeled?\",\"answer\":\"Lymphedema risk after breast cancer therapy is forecast by fusing ordinal and binary clinical variables from nearly 300 patients using a tested algorithmic approach.\"}]","Machine learning approaches for personalized medicine - 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