Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts. The first part, Bioinformatic Methods I, dealt with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics. This, the second part, Bioinformatic Methods II, will cover motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions. This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course although some command line work (though within a web browser) occurs in the 5th module. Bioinformatic Methods II is regularly updated, and was last updated for February 2022.

This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient's journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner. The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

This second of five courses focuses on how social determinants of health affects vulnerable populations. Focusing on four groups of vulnerable populations, students will examine how social determinants of health contribute to the poor health outcomes experienced by these populations. The topics of this course include: 1. Poverty 2. Women’s Health 3. Gender and LGBTQI+ Health 4. Family Health 5. Data Applications: t-test Analysis and Box Plot Visualization

Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization. By the end of this course, you will be able to: Read/write images of the brain in the NIfTI (Neuroimaging Informatics Technology Initiative) format Visualize and explore these images Perform inhomogeneity correction, brain extraction, and image registration (within a subject and to a template).

In this fourth of our five courses, I will go deeper into the training and education leadership skills that are helpful for nursing informatics leaders. I will also guide you through the process of preparing a course document or syllabus for the nursing informatics specialty both in academic settings and in practice or industry. Following are the course objectives: 1. Describe relevant nursing informatics course development in clinical and academic settings to understand similarities and differences in informatics teaching and education across settings. 2. Describe informatics education and training needs for diverse participants with various experience levels to enable development of appropriate training and education materials. 3. Develop a prototype course syllabus and introductory recorded message to apply learning in a simulated setting. 4. Describe the benefits of formal and informal mentoring for nursing informaticians to advance career opportunities and support the nursing informatics specialty.

Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gently-paced introduction to our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics), preparing learners to take the first course in the Specialization, "Finding Hidden Messages in DNA" (https://www.coursera.org/learn/dna-analysis). Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems. If you haven't programmed in Python before, not to worry! We provide "Just-in-Time" exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz. Lecture videos are also provided that accompany the material, but these videos are optional.

This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification. Subsequently, model-specific explanations such as Class-Activation Mapping (CAM) and Gradient-Weighted CAM are explained and implemented. The learners will understand axiomatic attributions and why they are important. Finally, attention mechanisms are going to be incorporated after Recurrent Layers and the attention weights will be visualised to produce local explanations of the model.

Solving the problems and challenges within the U.S. healthcare system requires a deep understanding of how the system works. Successful solutions and strategies must take into account the realities of the current system. This course explores the fundamentals of the U.S. healthcare system. It will introduce the principal institutions and participants in healthcare systems, explain what they do, and discuss the interactions between them. The course will cover physician practices, hospitals, pharmaceuticals, and insurance and financing arrangements. We will also discuss the challenges of healthcare cost management, quality of care, and access to care. While the course focuses on the U.S. healthcare system, we will also refer to healthcare systems in other developed countries. The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

In this path we will explore The US Federal Government Branch and Information Privacy. We will also cover US healthcare privacy related laws and privacy compliance including HIPAA, HITECH, GINA and more.

In most areas of health, data is being used to make important decisions. As a health population manager, you will have the opportunity to use data to answer interesting questions. In this course, we will discuss data analysis from a responsible perspective, which will help you to extract useful information from data and enlarge your knowledge about specific aspects of interest of the population. First, you will learn how to obtain, safely gather, clean and explore data. Then, we will discuss that because data are usually obtained from a sample of a limited number of individuals, statistical methods are needed to make claims about the whole population of interest. You will discover how statistical inference, hypothesis testing and regression techniques will help you to make the connection between samples and populations. A final important aspect is interpreting and reporting. How can we transform information into knowledge? How can we separate trustworthy information from noise? In the last part of the course, we will cover the critical assessment of the results, and we will discuss challenges and dangers of data analysis in the era of big data and massive amounts of information.  In this course, we will emphasize the concepts and we will also teach you how to effectively perform your analysis using R. You do not need to install R on your computer to follow the course, you will be able to access R and all the example data sets within the Coursera environment. This course will become part of the to-be-developed Leiden University master program Population Health Management. If you wish to find out more about this program see the last reading of this Course!