MIT OpenCourseWare

Browse materialsMIT 20.453J · Fall 2008

Biomedical Information Technology

Subjects: Biology · Mechanical Engineering · Technology

Art: Robert Hooke, Micrographia Scheme 07, 1665 · Public domain

Instructors: Prof. C. Forbes Dewey, Prof. Hanry Yu, Prof. Sourav Saha Bhowmick
Term: Fall 2008
Level: Graduate
What's inside: 1 lecture note · 1 reading · 22 other files · 2 web pages
Materials open: MIT OCW

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Source: MIT OpenCourseWare 20.453J Biomedical Information Technology, Fall 2008, Prof. C. Forbes Dewey, Prof. Hanry Yu, Prof. Sourav Saha Bhowmick — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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9 files

MIT OpenCourseWare, Prof. C. Forbes Dewey, Prof. Hanry Yu, Prof. Sourav Saha Bhowmick, 20.453J Biomedical Information Technology, Fall 2008.

License: CC BY-NC-SA 4.0 · Edition: Fall 2008 · Open the original course on OCW ↗

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Biomedical Information Technology

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Lecture Notes

Assignments

Lecture 1: 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

Lecture 2: Types and characteristics of biological and medical data - Distributed data systems - The life cycle of scientific data - Current challenges

Lecture 3: Examples from liver fibrosis - Gel electrophoresis - Microarrays - FACS and other methods - Creating biological pathways - Designing new experiments - Integrating information from the literature

Lecture 4: Data avalanche in the biomedical world and role of databases - Relational data model - ER modeling

Lecture 5: Designing good database schema - Functional dependencies - Normalization

Lecture 6: Querying relational databases using SQL

Lecture 7: Querying relational databases using SQL (cont.) - Limitations of relational data - Introduction to semi-structured data and XML

Lecture 8: Issues in querying XML data using XPath and XQuery - XML query languages - Principles of XML query processing

Lecture 9: Querying XML data (cont.) - XML and relational databases

Lecture 10: Querying graphs (molecular networks) - Querying pathways and protein sources

Lecture 11: 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

Lecture 12: 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

Lecture 13: Database approach to ontology storage and inference

Lecture 14: Creating relational databases from ontologies - OWLdb - Querying databases using ontologically-based queries

Lecture 15: Querying ontologies with SPARQL - Integrating ontologies and XML query processing - Role of ontology management in system biology

Lecture 16: 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

Lecture 17: Discussions with TAs related to assignments and term project

Lecture 18: Molecular network comparisons - Importance of molecular network comparison - Types of network comparison - Network comparison algorithms

Lecture 19: SWAN: An advanced architecture for sharing scientific information - The stakeholders, requirements, and functionality - The available technology - Workflow and usability

Lecture 20: 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

Lecture 21: Predicting drug efficacy by modeling - Current limits of predictability - Living with incomplete data - Examples of success in quantitative modeling

Lecture 22: Revolutionizing the drug discovery pipeline - The need for change - Key parts of the process - Quantitative modeling as a paradigm