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Title : 6.092 Bioinformatics and Proteomics (MIT)

Title : 6.092 Bioinformatics and Proteomics (MIT)

Description : This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

Description : This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

Fromsemester : January IAP

Fromsemester : January IAP

Fromyear : 2005

Fromyear : 2005

Creator :

Creator :

Creator :

Creator :

Creator :

Creator :

Date : 2005-04-27T10:02:32+05:00

Date : 2005-04-27T10:02:32+05:00

Relation : 6.092

Relation : 6.092

Language : en-US

Language : en-US

Subject : bioinformatics

Subject : bioinformatics

Subject : proteomics

Subject : proteomics

Subject : sequence analysis

Subject : sequence analysis

Subject : microarray expression analysis

Subject : microarray expression analysis

Subject : Bayesian methods

Subject : Bayesian methods

Subject : control theory

Subject : control theory

Subject : scale-free networks

Subject : scale-free networks

Subject : biotechnology applications

Subject : biotechnology applications

Subject : real-world examples

Subject : real-world examples

Subject : actual implementations

Subject : actual implementations

Subject : engineering design issues

Subject : engineering design issues

Subject : signal processing

Subject : signal processing

Subject : network theory

Subject : network theory

Subject : machine learning

Subject : machine learning

Subject : robotics

Subject : robotics