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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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7.342 Personal Genomics and Medicine: What's in Your Genome? (MIT) 7.342 Personal Genomics and Medicine: What's in Your Genome? (MIT)

Description

Human genome sequencing has revolutionized our understanding of disease susceptibility, drug metabolism and human ancestry. This course will explore how these advances have been made possible by revolutionary new sequencing methodologies that have decreased costs and increased throughput of genome analysis, making it possible to examine genetic correlates for a variety of biological processes and disorders. The course will combine discussions of primary scientific research papers with hands-on data analysis and small group presentations. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a Human genome sequencing has revolutionized our understanding of disease susceptibility, drug metabolism and human ancestry. This course will explore how these advances have been made possible by revolutionary new sequencing methodologies that have decreased costs and increased throughput of genome analysis, making it possible to examine genetic correlates for a variety of biological processes and disorders. The course will combine discussions of primary scientific research papers with hands-on data analysis and small group presentations. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a

Subjects

genome sequencing | genome sequencing | genome analysis | genome analysis | disease susceptibility | disease susceptibility | drug metabolism | drug metabolism | human ancestry | human ancestry | mitochondrial DNA | mitochondrial DNA | tyrosine kinase inhibitors | tyrosine kinase inhibitors | BCR-ABL gene fusion | BCR-ABL gene fusion | PCSK9 inhibitors | PCSK9 inhibitors | hypercholesterolemia | hypercholesterolemia | genetic testing | genetic testing | next generation sequencing | next generation sequencing | Single-nucleotide polymorphisms (SNPs) | Single-nucleotide polymorphisms (SNPs) | copy number variations (CNVs) | copy number variations (CNVs) | genome-wide association studies (GWAS) | genome-wide association studies (GWAS) | Chronic myelogenous leukemia (CML) | Chronic myelogenous leukemia (CML) | mosaics | mosaics | chimeras | chimeras | bioinformatics | bioinformatics

License

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7.16 Experimental Molecular Biology: Biotechnology II (MIT) 7.16 Experimental Molecular Biology: Biotechnology II (MIT)

Description

The course applies molecular biology and reverse genetics approaches to the study of apoptosis, or programmed cell death (PCD), in Drosophila cells. RNA interference (RNAi), or double stranded RNA-mediated gene silencing, will be used to inhibit expression of candidate apoptosis-related genes in cultured Drosophila cells. Teams of 2 or 3 students will design and carry out experiments to address questions about the genes involved in the regulation and execution of PCD in this system. Some projects involve the use of DNA damaging agents or other cytotoxic chemicals or drugs to help understand the pathways that control a cell's decision to undergo apoptosis. Instruction and practice in written and oral communication are provided. The course applies molecular biology and reverse genetics approaches to the study of apoptosis, or programmed cell death (PCD), in Drosophila cells. RNA interference (RNAi), or double stranded RNA-mediated gene silencing, will be used to inhibit expression of candidate apoptosis-related genes in cultured Drosophila cells. Teams of 2 or 3 students will design and carry out experiments to address questions about the genes involved in the regulation and execution of PCD in this system. Some projects involve the use of DNA damaging agents or other cytotoxic chemicals or drugs to help understand the pathways that control a cell's decision to undergo apoptosis. Instruction and practice in written and oral communication are provided.

Subjects

RNAi | RNAi | RNA interference | RNA interference | programmed cell death | programmed cell death | Drosophilia | Drosophilia | PCD | PCD | mRNA | mRNA | lab notebook | lab notebook | scientific writing | scientific writing | RT-PCR | RT-PCR | S2 RNA | S2 RNA | S2 | S2 | cell culture | cell culture | genetic transcription | genetic transcription | dsRNA | dsRNA | bioinformatics | bioinformatics

License

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9.520 Statistical Learning Theory and Applications (MIT) 9.520 Statistical Learning Theory and Applications (MIT)

Description

This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses

Subjects

supervised learning | supervised learning | statistical learning | statistical learning | multivariate function | multivariate function | Support Vector Machines | Support Vector Machines | regression | regression | classification | classification | VC theory | VC theory | computer vision | computer vision | computer graphics | computer graphics | bioinformatics | bioinformatics

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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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

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9.520 Statistical Learning Theory and Applications (MIT) 9.520 Statistical Learning Theory and Applications (MIT)

Description

Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject. Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Subjects

supervised learning | supervised learning | statistical learning | statistical learning | multivariate function | multivariate function | Support Vector Machines | Support Vector Machines | regression | regression | classification | classification | VC theory | VC theory | computer vision | computer vision | computer graphics | computer graphics | bioinformatics | bioinformatics

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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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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HST.950J Biomedical Computing (MIT) HST.950J Biomedical Computing (MIT)

Description

Analyzes computational needs of clinical medicine reviews systems and approaches that have been used to support those needs, and the relationship between clinical data and gene and protein measurements. Topics: the nature of clinical data; architecture and design of healthcare information systems; privacy and security issues; medical expertsystems; introduction to bioinformatics. Case studies and guest lectures describe contemporary systems and research projects. Term project using large clinical and genomic data sets integrates classroom topics. Analyzes computational needs of clinical medicine reviews systems and approaches that have been used to support those needs, and the relationship between clinical data and gene and protein measurements. Topics: the nature of clinical data; architecture and design of healthcare information systems; privacy and security issues; medical expertsystems; introduction to bioinformatics. Case studies and guest lectures describe contemporary systems and research projects. Term project using large clinical and genomic data sets integrates classroom topics.

Subjects

HST.950 | HST.950 | medical informatics | medical informatics | bioinformatics | bioinformatics | developing countries | developing countries | medical data | medical data | clinical data | clinical data | probabilistic models | probabilistic models | graphical models | graphical models | information theory | information theory | decision support | decision support | expert systems | expert systems | personal health records | personal health records | bayesian networks | bayesian networks | bayesian models | bayesian models | health information systems | health information systems | public health informatics | public health informatics | predictive genomics | predictive genomics | patient data privacy | patient data privacy

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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21A.850J The Anthropology of Cybercultures (MIT) 21A.850J The Anthropology of Cybercultures (MIT)

Description

This course explores a range of contemporary scholarship oriented to the study of 'cybercultures,' with a focus on research inspired by ethnographic and more broadly anthropological perspectives. Taking anthropology as a resource for cultural critique, the course will be organized through a set of readings chosen to illustrate central topics concerning the cultural and material practices that comprise digital technologies. We'll examine social histories of automata and automation; the trope of the 'cyber' and its origins in the emergence of cybernetics during the last century; cybergeographies and politics; robots, agents and humanlike machines; bioinformatics and artificial life; online sociality and the cyborg imaginary; ubiquitous and mobile computing; ethnographies of research and This course explores a range of contemporary scholarship oriented to the study of 'cybercultures,' with a focus on research inspired by ethnographic and more broadly anthropological perspectives. Taking anthropology as a resource for cultural critique, the course will be organized through a set of readings chosen to illustrate central topics concerning the cultural and material practices that comprise digital technologies. We'll examine social histories of automata and automation; the trope of the 'cyber' and its origins in the emergence of cybernetics during the last century; cybergeographies and politics; robots, agents and humanlike machines; bioinformatics and artificial life; online sociality and the cyborg imaginary; ubiquitous and mobile computing; ethnographies of research and

Subjects

21A.850 | 21A.850 | STS.484 | STS.484 | automata | automata | robotics | robotics | cybernetics | cybernetics | artificial intelligence | artificial intelligence | student work | student work | Turing test | Turing test | bioinformatics | bioinformatics | construction of identity | construction of identity | mobile computing | mobile computing | cybergeographies | cybergeographies | virtual reality | virtual reality | geek culture | geek culture | hackers | hackers | free software | free software | posthuman | posthuman

License

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HST.510 Genomics, Computing, Economics, and Society (MIT) HST.510 Genomics, Computing, Economics, and Society (MIT)

Description

This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports. This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports.

Subjects

genomics | genomics | bioengineering | bioengineering | biological engineering | biological engineering | personalized medicine | personalized medicine | informatics | informatics | bioinformatics | bioinformatics | human genome | human genome | stem cells | stem cells | genetically modified organisms | genetically modified organisms | biophysics | biophysics | bioethics | bioethics | society | society | bioeconomics | bioeconomics | statistics | statistics | modeling | modeling | datamining | datamining | systems biology | systems biology | technology development | technology development | biotechnology | biotechnology | public policy | public policy | health policy | health policy | business | business | economics | economics

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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Computation and genetics

Description

Resistance to drugs in bacteria can be aquired by swapping genes between individual bacteria. Computer programs developed by Dr Iqbal enable doctors to predict which antibiotics will be met with drug resistance, enabling the selection of the right drug. BIOINFORMATICS & PATHOGEN GENOMICS Dr Zamin Iqbal studies the DNA of bacteria and parasites, and compares the genomes of individual pathogens to track the spread of antibiotic resistance. Pathogens accumulate small genetic changes over time, and by tracking these changes, it is possible to map the spread of an infection. This enables better surveillance of pathogen evolution, within a host, within a hospital and across the world. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

computational | genetics | bioinformatics | pathogens | drug resistance | DNA | antibiotic resistance | computational | genetics | bioinformatics | pathogens | drug resistance | DNA | antibiotic resistance

License

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HST.947 Medical Artificial Intelligence (MIT) HST.947 Medical Artificial Intelligence (MIT)

Description

This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers. This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers.

Subjects

Introduces representations | techniques | and architectures used to build applied systems | Introduces representations | techniques | and architectures used to build applied systems | computational intelligence | computational intelligence | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | problem-solving paradigms | problem-solving paradigms | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | learning paradigms | learning paradigms | Speculations on the contributions of human vision and language systems to human intelligence | Speculations on the contributions of human vision and language systems to human intelligence | Meets with HST.947 spring only | Meets with HST.947 spring only | 4 Engineering Design Points | 4 Engineering Design Points | artificial intelligence | artificial intelligence | applied systems | applied systems | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systems | diagnosis | diagnosis | clinical simulation | clinical simulation | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics

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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7.15 Experimental Molecular Genetics (MIT) 7.15 Experimental Molecular Genetics (MIT)

Description

This project-based laboratory course provides students with in-depth experience in experimental molecular genetics, using modern methods of molecular biology and genetics to conduct original research. The course is geared towards students (including sophomores) who have a strong interest in a future career in biomedical research. This semester will focus on chemical genetics using Caenorhabditis elegans as a model system. Students will gain experience in research rationale and methods, as well as training in the planning, execution, and communication of experimental biology. WARNING NOTICE The experiments described in these materials are potentially hazardous and require a high level of safety training, special facilities and equipment, and supervision by appropriate individuals. You bear This project-based laboratory course provides students with in-depth experience in experimental molecular genetics, using modern methods of molecular biology and genetics to conduct original research. The course is geared towards students (including sophomores) who have a strong interest in a future career in biomedical research. This semester will focus on chemical genetics using Caenorhabditis elegans as a model system. Students will gain experience in research rationale and methods, as well as training in the planning, execution, and communication of experimental biology. WARNING NOTICE The experiments described in these materials are potentially hazardous and require a high level of safety training, special facilities and equipment, and supervision by appropriate individuals. You bear

Subjects

molecular genetics | molecular genetics | molecular biology | molecular biology | chemical genetics | chemical genetics | Caenorhabditis elegans | Caenorhabditis elegans | experimental biology | experimental biology | bioinformatics | bioinformatics | genetic linkage | genetic linkage | SNP mapping | SNP mapping | RNAi | RNAi | Gibson assembly | Gibson assembly | cDNA | cDNA | PCR | PCR | Primer design | Primer design | RNA extraction | RNA extraction | chemotaxis assay | chemotaxis assay | Next Generation Sequencing | Next Generation Sequencing

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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9.520 Statistical Learning Theory and Applications (MIT)

Description

This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses

Subjects

supervised learning | statistical learning | multivariate function | Support Vector Machines | regression | classification | VC theory | computer vision | computer graphics | bioinformatics

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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OeRBITAL Project (Open educational Resources for Biologists Involved in Teaching And Learning)

Description

OeRBITAL is a discovery project co-ordinated by the UK Centre for Bioscience, working with a number of Discipline Consultants tasked to explore OER repositories to discover the most suitable resources for the attention of their discipline communities. Around 300 Open Educational Resources in areas relating to Bioscience disciplines have been identified by our experts, and evaluated for inclusion in a number of discipline-specific curated collections, as a means of highlighting these key resources for the benefit of the wider Bioscience academic community.

Subjects

ukoer | oer | biochemistry | oerbital | bioscience | biology | biomaths | pharmacology | neuroscience | physiology | cell biology | cancer biology | plant sciences | enzymology | ecology | marine biology | microbiology | molecular genetics | molecular biology | bioinformatics | ethics | Biological sciences | C000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Curated collection of Molecular Biology resources

Description

This is an evaluated collection of links to resources for learning and teaching subjects relating to Molecular Biology. This forms part of the UK Centre for Bioscience OeRBITAL project.

Subjects

ukoer | oerbital | molecular biology | molecular structure and function | cell biology | signal transduction | biotechnology | bioinformatics | bioenergetics and metabolism | Biological sciences | C000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Curated collection of Bioinformatics resources

Description

This is an evaluated collection of links to resources for learning and teaching subjects relating to Bioinformatics. This forms part of the UK Centre for Bioscience OeRBITAL project.

Subjects

ukoer | bioinformatics | oerbital | protein structure analysis | computational and systems biology | Biological sciences | C000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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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

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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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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