課程目錄: 大數(shù)據(jù)科學與BD2K-LINCS數(shù)據(jù)協(xié)調(diào)和集成中心培訓

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        大數(shù)據(jù)科學與BD2K-LINCS數(shù)據(jù)協(xié)調(diào)和集成中心培訓

         

         

         

        The Library of Integrated Network-based Cellular Signatures (LINCS) Program Overview

        This module provides an overview of the concept behind the LINCS program;

        and tutorials on how to get started with using the LINCS L1000 dataset.

        Metadata and Ontologies

        This module includes a broad high level description of the concepts behind metadata

        and ontologies and how these are applied to LINCS datasets.

        Serving Data with APIs

        In this module we explain the concept of accessing data through

        an application programming interface (API).

        Bioinformatics Pipelines

        This module describes the important concept of a Bioinformatics pipeline.

        The Harmonizome

        This module describes a project that integrates many resources that contain knowledge about genes and proteins.

        The project is called the Harmonizome,

        and it is implemented as a web-server application available at: http://amp.pharm.mssm.edu/Harmonizome/

        Data Normalization

        This module describes the mathematical concepts behind data normalization.

        Data Clustering

        This module describes the mathematical concepts behind data clustering,

        or in other words unsupervised learning - the identification

        of patterns within data without considering the labels associated with the data.

        Midterm Exam

        The Midterm Exam consists of 45 multiple choice questions which covers modules 1-7.

        Some of the questions may require you to perform some analysis

        with the methods you learned throughout the course on new datasets.

        Enrichment Analysis

        This module introduces the important concept of performing gene set enrichment analyses.

        Enrichment analysis is the process of querying gene sets from genomics

        and proteomics studies against annotated gene sets collected from prior biological knowledge.

        Machine Learning

        This module describes the mathematical concepts of supervised machine learning,

        the process of making predictions from examples

        that associate observations/features/attribute with one or more properties that we wish to learn/predict.

        Benchmarking

        This module discusses how Bioinformatics pipelines can be compared and evaluated.

        Interactive Data Visualization

        This module provides programming examples on how

        to get started with creating interactive web-based data visualization elements/figures.

        Crowdsourcing Projects

        This final module describes opportunities to work on LINCS related projects that go beyond the course.

        Final Exam

        The Final Exam consists of 60 multiple choice questions which covers all of the modules

        of the course. Some of the questions may require you to perform some analysis with

        the methods you learned throughout the course on new datasets.