Omicslogic Data Science

The rapid growth of high-throughput data, including -omics technologies, gave rise to a significant demand for data science skills and experience with bioinformatics methods of analysis. To help introduce biologists, clinicians and students to cutting edge bioinformatics methods and commonly used data science concepts, our team designed an online bioinformatics training program called OmicsLogic. This online summer program is designed for Data science beginners students interested in data-driven research questions.

Upcoming Events

  1. 12
    January
    Session 1: Omics: Introduction to data types and properties
    09:00 - 10:00
    Online

    Session Topics Overview of commonly used “omics” data  NGS, Mass-Spec, phenotypic data (genomics, transcriptomics, metagenomics) Phenotypes: clinical, imaging, metadata (research, clinical, biotech, pharma) The...

  2. 14
    January
    Session 2: Big Data Challenges and Opportunities (conceptual and computational)
    09:00 - 10:00
    Online

    Session Topics Availability and variability of data  Unprecedented Detail and volume Data heterogeneity, complexity, and noise Need for structure and reproducibility

  3. 19
    January
    Session 3: Cleaning, loading and processing data (Logical steps and a practice)
    09:00 - 10:00
    Online

    Session Topics Analysis logic: from raw reads to a table of expression (RNA-seq example) Common sources of unwanted technical variation  pre-processing steps, filtering and...

  4. 21
    January
    Session 4: Exploratory data analysis: data summary and effective visualization
    09:00 - 10:00
    Online

    Summary statistics (histogram, boxplot, a scatterplot of 2 samples compared to each other, Excel “summary statistics” operation) Visualization of practice data – compare the...

  5. 26
    January
    Session 5: Hands-on: handling large and complex data
    09:00 - 10:00
    Online

    Session Topics Learn how to make statistical representations of the data and how to address missing or data errors. How do you compare the...

  6. 28
    January
    Session 6 – Introduction to Machine Learning (ML) and Artificial Intelligence (AI)
    09:00 - 10:00
    Online

    Session Topics Hypothesis testing 101: compare conditions and find the p-value Data-driven discovery: discover groups or conditions Process of inference for a machine versus...

  7. 02
    February
    Session 7 – Unsupervised Machine Learning: dimensionality reduction and clustering
    09:00 - 10:00
    Online

    Session Topics Finding patterns in the data and methods of data mining. PCA, k-means, h-clustering (run example on T-Bio and then open the script...

  8. 04
    February
    Session 8 – Supervised Machine Learning: classification and feature selection
    09:00 - 10:00
    Online

    Session Topics Conceptual Introduction: Known sample data is used to train the computer to use these patterns to correlate to unknown data. Binary decision...

  9. 09
    February
    Session 9 – Model accuracy and validation
    09:00 - 10:00
    Online

    Session Topics Technical accuracy (ROC curve) Logical or biological relevance (compare feature selection with PCA by subtype or clinical phenotype) Trained Model validation: Learning...

  10. 11
    February
    Session 10 – ML in production – getting results with ML
    09:00 - 10:00
    Online

    Session Topics The interaction between artificial intelligence and human  Differences between ML and AI In what ways can AI support human research and decision...

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Introduction to Bioinformatics

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