" 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 development; and geeks, gamers and hacktivists. We'll close by considering the implications for all of these topics of emerging reconceptualizations of sociomaterial relations, informed by feminist science and technology studies."

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Glossary of terms used in the bioinformatics course

This undergraduate activity introduces students to bioinformatics. During the guided activity students will access the National Center for Biotechnology Information's (NCBI) genetic sequence database to obtain and study DNA sequence entries relating to the chicken ovalbumin mRNA and genomic sequences.

This project is designed to give the student experience using the bioinformatics tools that have been taught in previous modules in an independent project. The student will use the project outline to develop a bioinformatics problem or question according to their interests, and determine the appropriate tools to answer the question.

This is an introduction to the bioinformatics website provided by the National Center for Biotechnology Information (NCBI). It includes an overview of the basic mission of NCBI and an introduction to the most commonly used biological databases available on the website and the tools for viewing and analyzing the data.

This session introduces bioinformatics using a case study of pathogenic bacterial identification via a Howard Hughes Medical Institute's virtual lab and NCBI web database searches. Another goal is to get the students thinking, writing and talking about the impact of the human genome project. Our students do the exercise independently coming together in the laboratory to present and discuss their findings--this feature makes the exercise feasible for large or small classes with limited laboratory computer resources. The sub-theme of this session is the use of virtual laboratories (vlabs) re-enforcing scientific concepts and methods to supplement lectures, tutorials or "wet" labs.

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 educational journal article addresses the implementation of bioinformatics in the classroom. The author explains how bioinformatics could play a key role for science students pursuing higher education, foster inquiry learning of content that has often been taught in a dry manner, provide the thread that ties classes together, improve biology teaching, enhance the learning of biotech issues and ethics, expose students to real-world science, and significantly help to reform biology teaching and improve learning. The article includes links to bioinformatics resources, information about how to get involved in bioinformatics, and a glossary of terms.

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.

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 required of all students. Reading is assigned from the contemporary literature, and there is occasional homework.

" 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 design with the ability to carry out a significant independent project. This course was offered as part of the Singapore-MIT Alliance (SMA) program as course number SMA 5304."

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 required of all students. Reading is assigned from the contemporary literature, and there is occasional homework.

Computer laboratory modules for the Introduction to Bioinformatics course. This course is designed for the beginning graduate student or advanced undergraduate in the biosciences. The goal is to introduce the student to various biologically relevant databases, methods to effectively search the databases, and an overall view of the various aspects of computational biology.

This case study takes a combined directed and discussion approach to explore risk factors for breast cancer. After a preparatory reading assignment, students assess various medical histories derived from actual women with breast cancer and rank their overall risk for breast cancer and make recommendations for risk reduction. The task is complicated by the different and often combined sources of risk (e.g., reproductive history, hormone replacement therapy and family history). Originally written for an introductory biology course, the case study could easily be adapted for upper division curses in genetics, physiology, or biochemistry to explore the biological and biochemical basis underlying various risk factors.

In this case, developed for an introductory genetics class, students meet a woman whose family has a history of colon cancer. Students create a pedigree based on information from the case and discuss what it means to be genetically predisposed to cancer. Using bioinformatics tools from the NCBI database, students identify and examine the mutation in the woman's APC gene that results in genetic predisposition to colon cancer. Finally, they investigate the biological function of the APC protein to understand why this mutation contributes to the development of cancer and determine whether APC is a proto-oncogene, tumor suppressor gene, or genome stability gene.

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

This chapter introduces biology instructors to computer assisted learning through the development and use of locally-authored biology software (courseware). The selection of authoring software (authoring languages), approaches to the development of courseware, and courseware programs are presented and discussed. Specific courseware programs on a variety of biological topics are presented for viewing, examination, and evaluation. A questionnaire that may be used to evaluate courseware is presented.

This information database provides an easy way of accessing the sequences and all-inclusive annotation data on the structures of the cyanobacterial genomes. Cyanobacteria carry a complete set of genes for oxygenic photosynthesis, and are believed to be the ancient ancestors of chloroplast. Maintained by the Kazusa DNA Research Institute, Cyanobase contains information and sequences for Synechocystis, Anabaena, Thermosynechococcus elongatus, Gloeobacter violaceus, Prochlorococcus marinus, Synechococcus, Chlorobium tepidum, and Rhodopseudomonas palustris. It also includes links to CyanoMutants, a database depository of published and unpublished functional and genetic data on Synechocystis 6803 mutants with known mutations, and CyanoGenes.