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Merge pull request #11 from NIGMS/google-instance-testing
Updated Submodule05 heading; Added links to bottom of each submodule
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‎GoogleCloud/Submodule01_Biomarker_Concepts.ipynb

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"metadata": {},
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"# Analysis of Biomedical Data for Biomarker Discovery\n",
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"<a id=\"submodule-1:-introduction-to-biomarkers\"></a>\n",
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"## Submodule 1: Introduction to Biomarkers\n",
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"### Dr. Christopher L. Hemme\n",
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"### Director, [RI-INBRE Molecular Informatics Core](https://web.uri.edu/riinbre/mic/)\n",
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"## Overview\n",
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"\n",
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"This Jupyter Notebook introduces the concept of biomarkers for disease diagnosis and prognosis. It begins with a general overview of biomarkers, including their definition, desirable properties (specificity, sensitivity, accuracy, cost-effectiveness, etc.), and different types (qualitative, quantitative at macro and molecular scales, and omics-based). The notebook also discusses the FDA's BEST (Biomarkers, EndpointS, and other Tools) glossary for classifying biomarkers by clinical role. It then delves into considerations for biomarker development, such as distinguishing correlation from causation, the use and limitations of animal models, the importance of human variability in clinical trials, and the need for new biomarkers to improve upon existing methods. Five case studies illustrate common clinical biomarkers: PSA for prostate cancer, ALP for liver injury, BRCA variants for breast cancer, serum creatinine for kidney injury, and microbial pathogens for periodontal disease. Finally, the notebook includes interactive quizzes to reinforce the concepts and suggests next steps, directing users to optional submodules on R, linear models, and exploratory analysis before proceeding to a case study on renal ischemia reperfusion injury."
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"This Jupyter Notebook introduces the concept of biomarkers for disease diagnosis and prognosis. It begins with a general overview of biomarkers, including their definition, desirable properties (specificity, sensitivity, accuracy, cost-effectiveness, etc.), and different types (qualitative, quantitative at macro and molecular scales, and omics-based). The notebook also discusses the FDA's BEST (Biomarkers, Endpoints, and other Tools) glossary for classifying biomarkers by clinical role. It then delves into considerations for biomarker development, such as distinguishing correlation from causation, the use and limitations of animal models, the importance of human variability in clinical trials, and the need for new biomarkers to improve upon existing methods. Five case studies illustrate common clinical biomarkers: PSA for prostate cancer, ALP for liver injury, BRCA variants for breast cancer, serum creatinine for kidney injury, and microbial pathogens for periodontal disease. Finally, the notebook includes interactive quizzes to reinforce the concepts and suggests next steps, directing users to optional submodules on R, linear models, and exploratory analysis before proceeding to a case study on renal ischemia reperfusion injury."
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"+ **What biomarkers are**: Including the broad definition and its practical meaning in biomedical research and other fields.\n",
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"+ **Desirable properties of biomarkers**: Covering specificity, sensitivity, accuracy, cost-effectiveness, ease of use/interpretation, and rapid turnaround time. It emphasizes the trade-offs often encountered in real-world applications.\n",
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"+ **Different types of biomarkers**: Distinguishing between qualitative (physiological) biomarkers, quantitative biomarkers at both the macro and molecular scales, and omics-based biomarkers. It highlights the advantages and limitations of each type.\n",
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"+ **The BEST (Biomarkers, EndpointS and other Tools) glossary**: Introducing the FDA's classification of biomarkers based on their clinical roles (Susceptibility/Risk, Diagnostic, Monitoring, Prognostic, Predictive, Pharmacodynamic/Response, and Safety).\n",
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"+ **The BEST (Biomarkers, Endpoints and other Tools) glossary**: Introducing the FDA's classification of biomarkers based on their clinical roles (Susceptibility/Risk, Diagnostic, Monitoring, Prognostic, Predictive, Pharmacodynamic/Response, and Safety).\n",
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"+ **Challenges in biomarker research**: Addressing the issues of correlation vs. causation, the use and limitations of non-human model organisms, the importance of accounting for human variability in clinical trials, and the need for a new biomarker to offer improvements over existing methods.\n",
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"+ **Examples of common clinical biomarkers**: Providing case studies of Prostate-Specific Antigen (PSA), Alkaline Phosphatase (ALP), BRCA variants, Serum Creatinine, and Microbial Pathogens as illustrative examples of different biomarker types and their applications in diagnosing diseases."
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"Remember to move to the next notebook or shut down your instance if you are finished."
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]
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{
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"cell_type": "markdown",
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"id": "70225d3d-dcf8-4317-87f5-1f561fbf74f2",
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"metadata": {},
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"<div style=\"display: flex; justify-content: center; margin-top: 20px; width: 100%;\"> \n",
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" <div style=\"display: flex; justify-content: space-between; width: 50%;\"> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/README.md#contents>README</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=\"#submodule-1:-introduction-to-biomarkers\">Top of this page</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule02_Intro_to_R_Data_Structures.ipynb#overview>Next section</a>\n",
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" </div> \n",
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" </div>\n",
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"</div>"
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]
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},
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"version": "4.4.1"
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‎GoogleCloud/Submodule02_Intro_to_R_Data_Structures.ipynb

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"metadata": {},
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"# Analysis of Biomedical Data for Biomarker Discovery\n",
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"<a id=\"top2\"></a>\n",
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"## Submodule 2: Introduction to R Data Structures\n",
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"### Dr. Christopher L. Hemme\n",
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"### Director, [RI-INBRE Molecular Informatics Core](https://web.uri.edu/riinbre/mic/)\n",
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"\n",
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"Remember to move to the next notebook or shut down your instance if you are finished."
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},
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{
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"cell_type": "markdown",
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"id": "41a4286e-76a7-4544-9fde-2083b466eee9",
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"<div style=\"display: flex; justify-content: center; margin-top: 20px; width: 100%;\"> \n",
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" <div style=\"display: flex; justify-content: space-between; width: 50%;\"> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule01_Biomarker_Concepts.ipynb#overview>Previous section</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=\"#top2\">Top of this page</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule03_Intro_to_Linear_Models.ipynb#overview>Next section</a>\n",
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"execution_count": null,
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"id": "4bc8ace1-3512-41d9-bf88-93e80a93f3c0",
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"source": []
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‎GoogleCloud/Submodule03_Intro_to_Linear_Models.ipynb

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"source": [
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"# Analysis of Biomedical Data for Biomarker Discovery\n",
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"<a id=\"top3\"></a>\n",
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"## Submodule 3: Introduction to Linear Models\n",
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"### Dr. Christopher L. Hemme\n",
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"### Director, [RI-INBRE Molecular Informatics Core](https://web.uri.edu/riinbre/mic/)\n",
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"Remember to move to the next notebook or shut down your instance if you are finished."
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"<div style=\"display: flex; justify-content: center; margin-top: 20px; width: 100%;\"> \n",
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" <div style=\"display: flex; justify-content: space-between; width: 50%;\"> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule02_Intro_to_R_Data_Structures.ipynb#overview>Previous section</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=\"#top3\">Top of this page</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule04_Intro_to_Exploratory_Analysis.ipynb#overview>Next section</a>\n",
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" </div> \n",
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"</div>"
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‎GoogleCloud/Submodule04_Intro_to_Exploratory_Analysis.ipynb

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"# Analysis of Biomedical Data for Biomarker Discovery\n",
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"<a id=\"top4\"></a>\n",
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"## Submodule 4: Introduction to Exploratory Analysis\n",
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"### Dr. Christopher L. Hemme\n",
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"### Director, [RI-INBRE Molecular Informatics Core](https://web.uri.edu/riinbre/mic/)\n",
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"Remember to move to the next notebook or shut down your instance if you are finished."
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"<div style=\"display: flex; justify-content: center; margin-top: 20px; width: 100%;\"> \n",
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" <div style=\"display: flex; justify-content: space-between; width: 50%;\"> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule03_Intro_to_Linear_Models.ipynbd#overview>Previous section</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=\"#top4\">Top of this page</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule05_IRI_Case_Study.ipynb#overview>Next section</a>\n",
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‎GoogleCloud/Submodule05_IRI_Case_Study.ipynb

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"# Principles of Biomarker Discovery\n",
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"# Analysis of Biomedical Data for Biomarker Discovery\n",
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"<a id=\"top5\"></a>\n",
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"## Submodule 5: Rat Renal Ischemia Reperfusion Injury Case Study\n",
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"### Dr. Christopher L. Hemme\n",
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"### Director, [RI-INBRE Molecular Informatics Core](https://web.uri.edu/riinbre/mic/)\n",
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"Remember to move to the next notebook or shut down your instance if you are finished."
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"cell_type": "markdown",
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"id": "5c1808d8-c522-463a-8cc6-eac5fd2290d2",
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"<div style=\"display: flex; justify-content: center; margin-top: 20px; width: 100%;\"> \n",
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" <div style=\"display: flex; justify-content: space-between; width: 50%;\"> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule04_Intro_to_Exploratory_Analysis.ipynb#overview>Previous section</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=\"#top5\">Top of this page</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule06_Linear_and_Logistic_Regression_Biomarkers.ipynb#overview>Next section</a>\n",
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‎GoogleCloud/Submodule06_Linear_and_Logistic_Regression_Biomarkers.ipynb

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"# Analysis of Biomedical Data for Biomarker Discovery\n",
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"<a id=\"top6\"></a>\n",
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"## Submodule 6: Linear and Logistic Regression for Comparison of Quantitative Biomarkers\n",
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"### Dr. Christopher L. Hemme\n",
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"### Director, [RI-INBRE Molecular Informatics Core](https://web.uri.edu/riinbre/mic/)\n",
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"Remember to move to the next notebook or shut down your instance if you are finished."
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"<div style=\"display: flex; justify-content: center; margin-top: 20px; width: 100%;\"> \n",
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" <div style=\"display: flex; justify-content: space-between; width: 50%;\"> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule05_IRI_Case_Study.ipynb#overview>Previous section</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=\"#top6\">Top of this page</a> \n",
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" </div> \n",
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" <div> \n",
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" <a href=https://github.com/NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery/blob/master/GoogleCloud/Submodule07_Exploratory_Proteomic_Analysis.ipynb#overview>Next section</a>\n",
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