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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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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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Business Intelligence in Biomedicine (2012) Business Intelligence in Biomedicine (2012)

Description

This course introduces the concept of business intelligence, both from a methodological point of view and from an applied point of view. Following a typical process defined in CRISP-DM, we will address all phases of a project, from problem analysis to the implementation of a system through some of the tools available in the market more commonly used. In addition, we introduce pattern discovery techniques that are common in clinical applications, as well as other tools for developing intelligent systems. There will also professional companies involved in business intelligence and health that show the applied view of the content seen on the course. The issues of data mining and application will vary depending on the background of the students enrolled. This course introduces the concept of business intelligence, both from a methodological point of view and from an applied point of view. Following a typical process defined in CRISP-DM, we will address all phases of a project, from problem analysis to the implementation of a system through some of the tools available in the market more commonly used. In addition, we introduce pattern discovery techniques that are common in clinical applications, as well as other tools for developing intelligent systems. There will also professional companies involved in business intelligence and health that show the applied view of the content seen on the course. The issues of data mining and application will vary depending on the background of the students enrolled.

Subjects

ón e Inteligencia Artificial | ón e Inteligencia Artificial | electronic health records | electronic health records | biosignals | biosignals | healthcare informatics | healthcare informatics | interoperability | interoperability | clinical image | clinical image | semantic interoperability | semantic interoperability | áticos | áticos | medical informatics | medical informatics

License

http://creativecommons.org/licenses/by-nc-sa/3.0/

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Biomedical Information Systems (2012) Biomedical Information Systems (2012)

Description

Biomedical Information System (BIS) is a course that provides a genera and interdisciplinar view of the medical informatics from the Computer Scientist perspective, considering theoretical/practicas, professional/research dimensions. This course focuses on the following topics: health services and information management, health information system development, biosignal processing, medical imaging, electronic health record formats, international standards and interoperability. Biomedical Information System (BIS) is a course that provides a genera and interdisciplinar view of the medical informatics from the Computer Scientist perspective, considering theoretical/practicas, professional/research dimensions. This course focuses on the following topics: health services and information management, health information system development, biosignal processing, medical imaging, electronic health record formats, international standards and interoperability.

Subjects

ón e Inteligencia Artificial | ón e Inteligencia Artificial | electronic health records | electronic health records | biosignals | biosignals | healthcare informatics | healthcare informatics | interoperability | interoperability | clinical image | clinical image | semantic interoperability | semantic interoperability | áticos | áticos | medical informatics | medical informatics

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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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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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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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.

Subjects

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

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.184 Health Information Systems to Improve Quality of Care in Resource-Poor Settings (MIT) HST.184 Health Information Systems to Improve Quality of Care in Resource-Poor Settings (MIT)

Description

This course is a collaborative offering of Sana, Partners in Health, and the Institute for Healthcare Improvement (IHI). The goal of this course is the development of innovations in information systems for developing countries that will (1) translate into improvement in health outcomes, (2) strengthen the existing organizational infrastructure, and (3) create a collaborative ecosystem to maximize the value of these innovations. The course will be taught by guest speakers who are internationally recognized experts in the field and who, with their operational experiences, will outline the challenges they faced and detail how these were addressed. This course is a collaborative offering of Sana, Partners in Health, and the Institute for Healthcare Improvement (IHI). The goal of this course is the development of innovations in information systems for developing countries that will (1) translate into improvement in health outcomes, (2) strengthen the existing organizational infrastructure, and (3) create a collaborative ecosystem to maximize the value of these innovations. The course will be taught by guest speakers who are internationally recognized experts in the field and who, with their operational experiences, will outline the challenges they faced and detail how these were addressed.

Subjects

health informatics | health informatics | clinical decision support | clinical decision support | health care management | health care management | public health | public health | international development | international development | developing country | developing country

License

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SP.718 Special Topics at Edgerton Center: D-Lab Health: Medical Technologies for the Developing World (MIT) SP.718 Special Topics at Edgerton Center: D-Lab Health: Medical Technologies for the Developing World (MIT)

Description

D-Lab Health provides multi-disciplinary approach to global health technology design via guest lectures and a major project based on fieldwork. We will explore the current state of global health challenges and learn how design medical technologies that address those problems. Students may travel to Nicaragua during spring break and work with health professionals, using medical technology design kits to gain field experience for their device challenge. As a final class deliverable, you will create a product design solution to address the challenges observed in the field. The resulting designs are prototyped in the summer for continued evaluation and testing. D-Lab Health provides multi-disciplinary approach to global health technology design via guest lectures and a major project based on fieldwork. We will explore the current state of global health challenges and learn how design medical technologies that address those problems. Students may travel to Nicaragua during spring break and work with health professionals, using medical technology design kits to gain field experience for their device challenge. As a final class deliverable, you will create a product design solution to address the challenges observed in the field. The resulting designs are prototyped in the summer for continued evaluation and testing.

Subjects

global health | global health | medicine | medicine | developing nation | developing nation | third world | third world | disease | disease | disease prevention | disease prevention | vaccine | vaccine | immunization | immunization | drug | drug | health diagnostic | health diagnostic | medical informatics | medical informatics | appropriate technology | appropriate technology | sustainable development | sustainable development | co-creation | co-creation | inequality | inequality | poverty | poverty | poor | poor | medical device | medical device | medical device design | medical device design | innovation | innovation | prototyping | prototyping | medical ethics | medical ethics | infant mortality | infant mortality

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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8.592 Statistical Physics in Biology (MIT) 8.592 Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; Considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.Technical RequirementsAny number of biological sequence comparison software tools can be used to import the .fna files found on this course site. Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; Considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.Technical RequirementsAny number of biological sequence comparison software tools can be used to import the .fna files found on this course site.

Subjects

Bioinformatics | Bioinformatics | DNA | DNA | gene finding | gene finding | sequence comparison | sequence comparison | phylogenetic trees | phylogenetic trees | biopolymers | biopolymers | DNA double helix | DNA double helix | secondary structure of RNA | secondary structure of RNA | protein folding | protein folding | protein motors | membranes | protein motors | membranes | cellular networks | cellular networks | neural networks | neural networks | evolution | evolution | statistical physics | statistical physics | molecular biology | molecular biology | deoxyribonucleic acid | deoxyribonucleic acid | genes | genes | genetics | genetics | gene sequencing | gene sequencing | phylogenetics | phylogenetics | double helix | double helix | RNA | RNA | ribonucleic acid | ribonucleic acid | force | force | motion | motion | packaging | packaging | protein motors | protein motors | membranes | membranes | biochemistry | biochemistry | genome | genome | optimization | optimization | partitioning | partitioning | pattern recognition | pattern recognition | collective behavior | collective behavior

License

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HST.921 Information Technology in the Health Care System of the Future (MIT) HST.921 Information Technology in the Health Care System of the Future (MIT)

Description

Includes audio/video content: AV faculty introductions. This innovative, trans-faculty subject teaches how information technologies (IT) are reshaping and redefining the health care marketplace through improved economies of scale, greater technical efficiencies in the delivery of care to patients, advanced tools for patient education and self-care, network integrated decision support tools for clinicians, and the emergence of e-commerce in health care. Student tutorials provide an opportunity for interactive discussion. Interdisciplinary project teams comprised of Harvard and MIT graduate students in medicine, business, law, education, engineering, computer science, public health, and government collaborate to design innovative IT applications. Projects are presented during the final class Includes audio/video content: AV faculty introductions. This innovative, trans-faculty subject teaches how information technologies (IT) are reshaping and redefining the health care marketplace through improved economies of scale, greater technical efficiencies in the delivery of care to patients, advanced tools for patient education and self-care, network integrated decision support tools for clinicians, and the emergence of e-commerce in health care. Student tutorials provide an opportunity for interactive discussion. Interdisciplinary project teams comprised of Harvard and MIT graduate students in medicine, business, law, education, engineering, computer science, public health, and government collaborate to design innovative IT applications. Projects are presented during the final class

Subjects

health care | health care | health care policy | health care policy | patient behavior | patient behavior | information management | information management | medical informatics | medical informatics | medical records | medical records | health record | health record | online medicine | online medicine | PHR | PHR | EHR | EHR | patient privacy | patient privacy | entrepreneurship | entrepreneurship | start-up | start-up | innovation | innovation | cybermedicine | cybermedicine | telemedicine | telemedicine | non-profit | non-profit | pharmaceutical | pharmaceutical | insurance | insurance | hospital | hospital | doctor | doctor | patient | patient | medicine | medicine | social networking | social networking | economies of scale | economies of scale | patient education | patient education | self-care | self-care | network integration | network integration | decision support tools | decision support tools | disease managment | disease managment | health economics | health economics | clinical effectiveness | clinical effectiveness | medical software | medical software | mobile applications | mobile applications | intellectual property | intellectual property

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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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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8.592J Statistical Physics in Biology (MIT) 8.592J Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution. Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Subjects

8.592 | 8.592 | HST.452 | HST.452 | Statistical physics | Statistical physics | Bioinformatics | Bioinformatics | DNA | DNA | gene finding | gene finding | sequence comparison | sequence comparison | phylogenetic trees | phylogenetic trees | biopolymers | biopolymers | DNA double helix | DNA double helix | secondary structure of RNA | secondary structure of RNA | protein folding | protein folding | protein motors | protein motors | membranes | membranes | cellular networks | cellular networks | neural networks | neural networks | evolution | evolution

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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9.520-A Networks for Learning: Regression and Classification (MIT) 9.520-A Networks for Learning: Regression and Classification (MIT)

Description

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theori The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theori

Subjects

Learning | Learning | Perspective | Perspective | Regularized | Regularized | Kernel Hilbert Spaces | Kernel Hilbert Spaces | Approximation | Approximation | Nonparametric | Nonparametric | Ridge Approximation | Ridge Approximation | Networks | Networks | Finance | Finance | Statistical Learning Theory | Statistical Learning Theory | Consistency | Consistency | Empirical Risk | Empirical Risk | Minimization Principle | Minimization Principle | VC-Dimension | VC-Dimension | VC-bounds | VC-bounds | Regression | Regression | Structural Risk Minimization | Structural Risk Minimization | Support Vector Machines | Support Vector Machines | Kernel Engineering | Kernel Engineering | Computer Vision | Computer Vision | Computer Graphics | Computer Graphics | Neuroscience | Neuroscience | Approximation Error | Approximation Error | Approximation Theory | Approximation Theory | Bioinformatics | Bioinformatics | Bagging | Bagging | Boosting | Boosting | Wavelets | Wavelets | Frames | Frames

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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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15.795 Seminar in Operations Management (MIT) 15.795 Seminar in Operations Management (MIT)

Description

This seminar will explore the purposes and development of Technology Roadmaps for systematically mapping out possible development paths for various technological domains and the industries that build on them. Data of importance for such roadmaps include rates of innovation, key bottlenecks, physical limitations, improvement trendlines, corporate intent, and value chain and industry evolutionary paths. The course will build on ongoing work on the MIT Communications Technology Roadmap project, but will explore other domains selected from Nanotechnology, Bio-informatics, Geno/Proteino/Celleomics, Neurotechnology, Imaging & Diagnostics, etc. Thesis and Special Project opportunities will be offered. This seminar will explore the purposes and development of Technology Roadmaps for systematically mapping out possible development paths for various technological domains and the industries that build on them. Data of importance for such roadmaps include rates of innovation, key bottlenecks, physical limitations, improvement trendlines, corporate intent, and value chain and industry evolutionary paths. The course will build on ongoing work on the MIT Communications Technology Roadmap project, but will explore other domains selected from Nanotechnology, Bio-informatics, Geno/Proteino/Celleomics, Neurotechnology, Imaging & Diagnostics, etc. Thesis and Special Project opportunities will be offered.

Subjects

technology development | technology development | operations management | operations management | roadmap | roadmap | nnovation | nnovation | bottleneck | bottleneck | corporate intent | corporate intent | value chain | value chain | nanotechnology | nanotechnology | bioninformatics | bioninformatics | neurotechnology | neurotechnology | imaging | imaging | diagnostics | diagnostics | innovation | innovation

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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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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21M.269 Studies in Western Music History: Quantitative and Computational Approaches to Music History (MIT) 21M.269 Studies in Western Music History: Quantitative and Computational Approaches to Music History (MIT)

Description

The disciplines of music history and music theory have been slow to embrace the digital revolutions that have transformed other fields' text-based scholarship (history and literature in particular). Computational musicology opens the door to the possibility of understanding—even if at a broad level—trends and norms of behavior of large repertories of music. This class presents the major approaches, results, and challenges of computational musicology through readings in the field, gaining familiarity with datasets, and hands on workshops and assignments on data analysis and "corpus" (i.e., repertory) studies. Class sessions alternate between discussion/lecture and labs on digital tools for studying music. A background in music theory and/or history is required, and ex The disciplines of music history and music theory have been slow to embrace the digital revolutions that have transformed other fields' text-based scholarship (history and literature in particular). Computational musicology opens the door to the possibility of understanding—even if at a broad level—trends and norms of behavior of large repertories of music. This class presents the major approaches, results, and challenges of computational musicology through readings in the field, gaining familiarity with datasets, and hands on workshops and assignments on data analysis and "corpus" (i.e., repertory) studies. Class sessions alternate between discussion/lecture and labs on digital tools for studying music. A background in music theory and/or history is required, and ex

Subjects

music informatics | music informatics | computational methods | computational methods | statistical analysis | statistical analysis | musicology | musicology | music theory | music theory | open source software | open source software

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

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