Biomedical information technology

Lectures: 2 sessions / week, 1.5 hours / session

Biology and medicine are moving into a new era that is characterized as being "data-rich." In biological research, a single laboratory can produce terabytes of data per month that needs to be shared across the research community. Drug development involves analyzing hundreds of compounds with laboratory tests that generate huge amounts of data that must be analyzed and shared. Clinical trials assay thousands of individual data elements on hundreds of patients over many time points.

The objective of this course is to provide the students with the knowledge to address these challenges. We focus on the storage, integration, querying and management of heterogeneous, voluminous, geographically dispersed biomedical data. In addition to primary data, such as experimental data, the methods also address derived data such as those from analyzed microscope images. Examples of pathway analysis methods and the sharing and storage of the data that they generate will be presented. Querying across multiple databases is described, where the databases can be as diverse as microarray experiments, curated databases compiled by domain experts, or biomedical images. Other data sources include medical records, information on disease, references to literature, and biological pathways predicting protein expression. Several current examples from biological research will be presented.

1.00 Introduction to Computers and Engineering Problem Solving; 6.001 Structure and Interpretation of Computer Programs; or experience with Web-based computing.

There is no recommended text book for this course simply because to the best of our knowledge there is no single text book available that can address the breath and depth of the issues in this course. Hence the reference materials will be lecture notes, research experience of the course instructors in biomedical data management, and a set of research papers.

A term paper is required of all students. The subject of the term paper is the choice of the student, and can as examples be a driving problem in research, a new idea for managing biomedical data, or an improvement on an existing system.

Instructors Key:

CFD = Prof. C. Forbes Dewey, Jr. (MIT)
SSB = Prof. Sourav S. Bhowmick (NTU, Singapore)
HY = Prof. Hanry Yu (NUS, Singapore)

Biomedical information technology today

- Grand challenge problems in biology and medicine
- The key role of information technology
- Semantics, ontologies, and standards
- Pathway modeling
- Term paper instructions

Types and characteristics of biological and medical data

- Distributed data systems
- The life cycle of scientific data
- Current challenges

Examples from liver fibrosis

- Gel electrophoresis
- Microarrays
- FACS and other methods
- Creating biological pathways
- Designing new experiments
- Integrating information from the literature

Data avalanche in the biomedical world and role of databases

- Relational data model
- ER modeling

Designing good database schema

- Functional dependencies
- Normalization

Querying relational databases using SQL (cont.)

- Limitations of relational data
- Introduction to semi-structured data and XML

Issues in querying XML data using XPath and XQuery

- XML query languages
- Principles of XML query processing

Querying XML data (cont.)

- XML and relational databases

Querying graphs (molecular networks)

- Querying pathways and protein sources

Data integration without semantics

- Issues related to biological data integration
- Standards for publishing and sharing data
- Examples and usage such as the DICOM standard
- Biological databases and supporting organizations

Definitions and importance of ontologies

- Standards for publishing and sharing ontologies (OWL, RDF)
- Examples and usage of ontologies in life sciences
- Using unique identifiers and other semantic standards

Creating relational databases from ontologies

- OWLdb
- Querying databases using ontologically-based queries

Querying ontologies with SPARQL

- Integrating ontologies and XML query processing
- Role of ontology management in system biology

Modeling and computing pathways

- Modeling and representation of pathways (SBML, CellML)
- Challenges of managing disparate sources of pathways
- Cell designer, cytosolve, and other computational environments

Molecular network comparisons

- Importance of molecular network comparison
- Types of network comparison
- Network comparison algorithms

SWAN: An advanced architecture for sharing scientific information

- The stakeholders, requirements, and functionality
- The available technology
- Workflow and usability

Building a distributed pathway-enabled information system for biological research

- Scope of the data sources and the application constraints
- Workflow and usability
- Technical considerations
- Accommodating the future

Predicting drug efficacy by modeling

- Current limits of predictability
- Living with incomplete data
- Examples of success in quantitative modeling

Revolutionizing the drug discovery pipeline

- The need for change
- Key parts of the process
- Quantitative modeling as a paradigm

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