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HST.950J Engineering Biomedical Information: From Bioinformatics to Biosurveillance (MIT) HST.950J Engineering Biomedical Information: From Bioinformatics to Biosurveillance (MIT)

Description

This course provides an interdisciplinary introduction to the technological advances in biomedical informatics and their applications at the intersection of computer science and biomedical research. This course provides an interdisciplinary introduction to the technological advances in biomedical informatics and their applications at the intersection of computer science and biomedical research.

Subjects

biomedical informatics | biomedical informatics | bioinformatics | bioinformatics | biomedical research | biomedical research | biological computing | biological computing | biomedical computing | biomedical computing | computational genomics | computational genomics | genomics | genomics | microarrays | microarrays | proteomics | proteomics | pharmacogenomics | pharmacogenomics | genomic privacy | genomic privacy | clinical informatics | clinical informatics | biosurveillance | biosurveillance | privacy | privacy | biotechnology | biotechnology

License

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20.453J Biomedical Information Technology (BE.453J) (MIT) 20.453J Biomedical Information Technology (BE.453J) (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | imaging | medical imaging | medical imaging | metadata | metadata | medical record | medical record | DICOM | DICOM | computer architecture | computer architecture | client-server architecture | client-server architecture | SEM | SEM | TEM | TEM | OME | OME | RDF | RDF | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | clinical decision support | clinical decision support | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | 20.453 | 20.453 | 2.771 | 2.771 | HST.958 | HST.958

License

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BE.453J Biomedical Information Technology (MIT) BE.453J Biomedical Information Technology (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | imaging | medical imaging | medical imaging | metadata | metadata | medical record | medical record | DICOM | DICOM | computer architecture | computer architecture | client-server architecture | client-server architecture | SEM | SEM | TEM | TEM | OME | OME | RDF | RDF | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | clinical decision support | clinical decision support | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | 2.771J | 2.771J | 2.771 | 2.771 | HST.958J | HST.958J | HST.958 | HST.958

License

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HST.512 Genomic Medicine (MIT) HST.512 Genomic Medicine (MIT)

Description

Includes audio/video content: AV lectures. This course reviews the key genomic technologies and computational approaches that are driving advances in prognostics, diagnostics, and treatment. Throughout the semester, emphasis will return to issues surrounding the context of genomics in medicine including: what does a physician need to know? what sorts of questions will s/he likely encounter from patients? how should s/he respond? Lecturers will guide the student through real world patient-doctor interactions. Outcome considerations and socioeconomic implications of personalized medicine are also discussed. The first part of the course introduces key basic concepts of molecular biology, computational biology, and genomics. Continuing in the informatics applications portion of the course, lec Includes audio/video content: AV lectures. This course reviews the key genomic technologies and computational approaches that are driving advances in prognostics, diagnostics, and treatment. Throughout the semester, emphasis will return to issues surrounding the context of genomics in medicine including: what does a physician need to know? what sorts of questions will s/he likely encounter from patients? how should s/he respond? Lecturers will guide the student through real world patient-doctor interactions. Outcome considerations and socioeconomic implications of personalized medicine are also discussed. The first part of the course introduces key basic concepts of molecular biology, computational biology, and genomics. Continuing in the informatics applications portion of the course, lec

Subjects

genomics | genomics | genomic medicine | genomic medicine | genetics | genetics | genomic measurement | genomic measurement | microarray | microarray | informatics | informatics | bioinformatics | bioinformatics | computational biology | computational biology | machine learning | machine learning | pharmacogenomics | pharmacogenomics | complex traits | complex traits | individual pharmacology | individual pharmacology | cancer diagnostics | cancer diagnostics | genetic disease | genetic disease | biomedical | biomedical | genomes | genomes | bioethics | bioethics | integrative genomics | integrative genomics | genomic technologies | genomic technologies

License

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6.092 Bioinformatics and Proteomics (MIT) 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. 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.

Subjects

bioinformatics | bioinformatics | proteomics | proteomics | sequence analysis | sequence analysis | microarray expression analysis | microarray expression analysis | Bayesian methods | Bayesian methods | control theory | control theory | scale-free networks | scale-free networks | biotechnology applications | biotechnology applications | real-world examples | real-world examples | actual implementations | actual implementations | engineering design issues | engineering design issues | signal processing | signal processing | network theory | network theory | machine learning | machine learning | robotics | robotics

License

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7.89J Topics in Computational and Systems Biology (MIT) 7.89J Topics in Computational and Systems Biology (MIT)

Description

This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB Ph.D. program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology. This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB Ph.D. program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology.

Subjects

Computational Biology | Computational Biology | Systems Biology | Systems Biology | Genomics | Genomics | Protein Function | Protein Function | Nucleic Acid Binding Factors | Nucleic Acid Binding Factors | microarray analysis | microarray analysis | genome-wide mapping | genome-wide mapping | gene expression | gene expression | evolutionary dynamics | evolutionary dynamics | sequencing | sequencing | translation | translation | network motifs | network motifs | pathway modeling | pathway modeling | synthetic biology | synthetic biology | metagenomics | metagenomics | signal transduction | signal transduction

License

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7.90J Computational Functional Genomics (MIT) 7.90J Computational Functional Genomics (MIT)

Description

The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches. The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches.

Subjects

systems biology | systems biology | genome structure | genome structure | DNA | DNA | RNA | RNA | transcription | transcription | stem cell | stem cell | biology | biology | microarray | microarray | gene expression | gene expression | statistical data analysis | statistical data analysis | chromatin | chromatin | gene sequence | gene sequence | genomic sequence | genomic sequence | motif | motif | protein | protein | error model | error model | diagnostic | diagnostic | gene clustering | gene clustering | phenotype | phenotype | clustering | clustering | proteome | proteome | 7.90 | 7.90 | 6.874 | 6.874

License

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20.453J Biomedical Information Technology (MIT) 20.453J Biomedical Information Technology (MIT)

Description

This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig

Subjects

20.453 | 20.453 | 2.771 | 2.771 | HST.958 | HST.958 | imaging | imaging | medical imaging | medical imaging | metadata | metadata | molecular biology | molecular biology | medical records | medical records | DICOM | DICOM | RDF | RDF | OWL | OWL | SPARQL | SPARQL | SBML | SBML | CellML | CellML | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | computational biology | computational biology | clinical decision support | clinical decision support | clinical trial | clinical trial | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | pathway modeling | pathway modeling | XML | XML | SQL | SQL | relational database | relational database | biological data | biological data | ontologies | ontologies | drug development | drug development | drug discovery | drug discovery | drug target | drug target | pharmaceutical | pharmaceutical | gene sequencing | gene sequencing

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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RES.TL-002 STAR: Software Tools for Academics and Researchers (MIT) RES.TL-002 STAR: Software Tools for Academics and Researchers (MIT)

Description

The Software Tools for Academics and Researchers (STAR) program at MIT seeks to bridge the divide between scientific research and the classroom. Understanding and applying research methods in the classroom setting can be challenging due to time constraints and the need for advanced equipment and facilities. The multidisciplinary STAR team collaborates with faculty from MIT and other educational institutions to design software exploring core scientific research concepts. The goal of STAR is to develop innovative and intuitive teaching tools for classroom use. All of the STAR educational tools are freely available. To complement the educational software, the STAR website contains curriculum components/modules which can facilitate the use of STAR educational tools in a variety of educational The Software Tools for Academics and Researchers (STAR) program at MIT seeks to bridge the divide between scientific research and the classroom. Understanding and applying research methods in the classroom setting can be challenging due to time constraints and the need for advanced equipment and facilities. The multidisciplinary STAR team collaborates with faculty from MIT and other educational institutions to design software exploring core scientific research concepts. The goal of STAR is to develop innovative and intuitive teaching tools for classroom use. All of the STAR educational tools are freely available. To complement the educational software, the STAR website contains curriculum components/modules which can facilitate the use of STAR educational tools in a variety of educational

Subjects

structural biology | structural biology | molecular 3-D viewer | molecular 3-D viewer | Protein Data Bank | Protein Data Bank | genetic cross | genetic cross | genetic cross simulator | genetic cross simulator | DNA | DNA | ORF | ORF | Open Reading Frame | Open Reading Frame | genomic gene expression | genomic gene expression | microarray analysis | microarray analysis | hydrological analysis | hydrological analysis | watersheds | watersheds | molecular dynamics | molecular dynamics | atomistic materials modeling | atomistic materials modeling | distributed computer cluster | distributed computer cluster | Elastic Compute Cloud | Elastic Compute Cloud | EC2 | EC2 | parallel programming | parallel programming | OpenMP | OpenMP | OpenMPI | OpenMPI

License

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RES.10-001 Making Science and Engineering Pictures (MIT) RES.10-001 Making Science and Engineering Pictures (MIT)

Description

Includes audio/video content: AV lectures. This collection of videos teaches how to use a flatbed scanner to create photographs of science and engineering. It is part of the interdisciplinary course taught at MIT called “Visual Strategies for Scientists and Engineers” that provides instruction in best practices for creating more effective graphics and photographs to support and communicate research in science and engineering.About the InstructorFelice Frankel is an award-winning science photographer and research scientist in the Center for Materials Science and Engineering at the Massachusetts Institute of Technology. Felice's images have been internationally published in books, journals, and magazines, including the New York Times, Nature, Science, National Geographic, and Di Includes audio/video content: AV lectures. This collection of videos teaches how to use a flatbed scanner to create photographs of science and engineering. It is part of the interdisciplinary course taught at MIT called “Visual Strategies for Scientists and Engineers” that provides instruction in best practices for creating more effective graphics and photographs to support and communicate research in science and engineering.About the InstructorFelice Frankel is an award-winning science photographer and research scientist in the Center for Materials Science and Engineering at the Massachusetts Institute of Technology. Felice's images have been internationally published in books, journals, and magazines, including the New York Times, Nature, Science, National Geographic, and Di

Subjects

scientific photography | scientific photography | journal submissions | journal submissions | PDMS photography | PDMS photography | microfluidic devices | microfluidic devices | microarrays | microarrays | drug delivery device | drug delivery device | petri dishes | petri dishes | E. Coli growth | E. Coli growth | flatbed scanner images | flatbed scanner images | human physiome chip | human physiome chip | lung on a chip | lung on a chip | electronic camera | electronic camera | microscale solar cells | microscale solar cells | solar cell | solar cell | E-Ink | E-Ink | tomato images | tomato images | music box | music box | Venus’ flower basket | Venus’ flower basket | Soft microfluidic sensor | Soft microfluidic sensor | paper-based microfluidics | paper-based microfluidics | diagnostic device | diagnostic device | macro photography | macro photography

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 7.91 | 20.490 | 20.490 | 20.390 | 20.390 | 7.36 | 7.36 | 6.802 | 6.802 | 6.874 | 6.874 | HST.506 | HST.506 | computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | artificial intelligence | artificial intelligence | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation

License

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation | BE.490J | BE.490J | 7.91 | 7.91 | 7.36 | 7.36 | BE.490 | BE.490 | 20.490 | 20.490

License

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

License

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HST.950J Engineering Biomedical Information: From Bioinformatics to Biosurveillance (MIT)

Description

This course provides an interdisciplinary introduction to the technological advances in biomedical informatics and their applications at the intersection of computer science and biomedical research.

Subjects

biomedical informatics | bioinformatics | biomedical research | biological computing | biomedical computing | computational genomics | genomics | microarrays | proteomics | pharmacogenomics | genomic privacy | clinical informatics | biosurveillance | privacy | biotechnology

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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20.453J Biomedical Information Technology (BE.453J) (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | medical imaging | metadata | medical record | DICOM | computer architecture | client-server architecture | SEM | TEM | OME | RDF | semantic web | BioHaystack | database | schema | ExperiBase | genomics | proteomics | bioinformatics | clinical decision support | microarray | gel electrophoresis | diagnosis | 20.453 | 2.771 | HST.958

License

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BE.453J Biomedical Information Technology (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | medical imaging | metadata | medical record | DICOM | computer architecture | client-server architecture | SEM | TEM | OME | RDF | semantic web | BioHaystack | database | schema | ExperiBase | genomics | proteomics | bioinformatics | clinical decision support | microarray | gel electrophoresis | diagnosis | 2.771J | 2.771 | HST.958J | HST.958

License

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 20.490 | 20.390 | 7.36 | 6.802 | 6.874 | HST.506 | computational biology | systems biology | bioinformatics | artificial intelligence | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation

License

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7.89J Topics in Computational and Systems Biology (MIT)

Description

This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB Ph.D. program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology.

Subjects

Computational Biology | Systems Biology | Genomics | Protein Function | Nucleic Acid Binding Factors | microarray analysis | genome-wide mapping | gene expression | evolutionary dynamics | sequencing | translation | network motifs | pathway modeling | synthetic biology | metagenomics | signal transduction

License

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7.90J Computational Functional Genomics (MIT)

Description

The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches.

Subjects

systems biology | genome structure | DNA | RNA | transcription | stem cell | biology | microarray | gene expression | statistical data analysis | chromatin | gene sequence | genomic sequence | motif | protein | error model | diagnostic | gene clustering | phenotype | clustering | proteome | 7.90 | 6.874

License

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20.453J Biomedical Information Technology (MIT)

Description

This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig

Subjects

20.453 | 2.771 | HST.958 | imaging | medical imaging | metadata | molecular biology | medical records | DICOM | RDF | OWL | SPARQL | SBML | CellML | semantic web | BioHaystack | database | schema | ExperiBase | genomics | proteomics | bioinformatics | computational biology | clinical decision support | clinical trial | microarray | gel electrophoresis | diagnosis | pathway modeling | XML | SQL | relational database | biological data | ontologies | drug development | drug discovery | drug target | pharmaceutical | gene sequencing

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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RES.TL-002 STAR: Software Tools for Academics and Researchers (MIT)

Description

The Software Tools for Academics and Researchers (STAR) program at MIT seeks to bridge the divide between scientific research and the classroom. Understanding and applying research methods in the classroom setting can be challenging due to time constraints and the need for advanced equipment and facilities. The multidisciplinary STAR team collaborates with faculty from MIT and other educational institutions to design software exploring core scientific research concepts. The goal of STAR is to develop innovative and intuitive teaching tools for classroom use. All of the STAR educational tools are freely available. To complement the educational software, the STAR website contains curriculum components/modules which can facilitate the use of STAR educational tools in a variety of educational

Subjects

structural biology | molecular 3-D viewer | Protein Data Bank | genetic cross | genetic cross simulator | DNA | ORF | Open Reading Frame | genomic gene expression | microarray analysis | hydrological analysis | watersheds | molecular dynamics | atomistic materials modeling | distributed computer cluster | Elastic Compute Cloud | EC2 | parallel programming | OpenMP | OpenMPI

License

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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.

Subjects

bioinformatics | proteomics | sequence analysis | microarray expression analysis | Bayesian methods | control theory | scale-free networks | biotechnology applications | real-world examples | actual implementations | engineering design issues | signal processing | network theory | machine learning | robotics

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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HST.512 Genomic Medicine (MIT)

Description

This course reviews the key genomic technologies and computational approaches that are driving advances in prognostics, diagnostics, and treatment. Throughout the semester, emphasis will return to issues surrounding the context of genomics in medicine including: what does a physician need to know? what sorts of questions will s/he likely encounter from patients? how should s/he respond? Lecturers will guide the student through real world patient-doctor interactions. Outcome considerations and socioeconomic implications of personalized medicine are also discussed. The first part of the course introduces key basic concepts of molecular biology, computational biology, and genomics. Continuing in the informatics applications portion of the course, lecturers begin each lecture block with a scen

Subjects

genomics | genomic medicine | genetics | genomic measurement | microarray | informatics | bioinformatics | computational biology | machine learning | pharmacogenomics | complex traits | individual pharmacology | cancer diagnostics | genetic disease | biomedical | genomes | bioethics | integrative genomics | genomic technologies

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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