Naval Postgraduate School's Prof. Neil Rowe has a book entitled “Artificial Intelligence through Prolog” that presents some core topics using logic programming (favored by this author) and is now available online for free [39]. The Biomedical Informatics Program is an interdisciplinary graduate and postdoctoral training program, part of the Department of Biomedical Data Science. However, the field of bioinformatics by its nature may offer the best chance for finding ways to involve distance learners directly in ongoing scientific research, and that would seem to be a worthy goal for the burgeoning online education movement. Yes No, Is the Subject Area "Lectures" applicable to this article? The optional programming assignments require Java or Python. To simply learn Python if you already have significant experience in some other language, try Nick Parlante's video tutorial at Google (https://code.google.com/edu/languages/google-python-class). Nor does classroom learning by itself, virtual or otherwise, fully prepare one for establishing real-world error models, dealing with missing data, establishing a statistical case for some result, arguing and defending scientific positions, navigating the publication process, and sundry other practical skills. Though now a bit dated, a freely accessible online version of the 2nd edition is available from the National Center for Biotechnology Information (NCBI) of the U.S. National Institutes of Health (NIH) (http://www.ncbi.nlm.nih.gov/books/NBK7580). Students interested in robotic technologies, for instance in control of laboratory automation, should consider Stanford Prof. Oussama Khatib's course “Introduction to Robotics” (http://see.stanford.edu/see/courseinfo.aspx?coll=86cc8662-f6e4-43c3-a1be-b30d1d179743). This is a relatively short but well-constructed course that was yet another variation on Stanford Engineering's courseware initiatives. Only in a minority of cases were videos of lectures posted. An associated textbook is also freely available online, a chapter at a time [8]. Less than two years ago, the author published an online bioinformatics curriculum in this journal and made the claim (with some important caveats) that a sufficient number and variety of free video courses had made their way to the web that it was possible to obtain a reasonably comprehensive bioinformatics education on one's laptop .In that compilation of courseware, only a … Moreover, there is every indication that the instruction can be effective; the U.S. Department of Education, in an exhaustive meta-analysis of 51 published head-to-head trials, found that “on average, students in online learning conditions performed better than those receiving face-to-face instruction” [2]. Students in the Bioinformatics, MS Online program have the opportunity to take advantage of research and internship opportunities. San Diego Profs. The theory has emerged over recent decades as essential both for the scientific analysis of algorithms in computer science and for the study of scientific models in many other disciplines, including probability theory, statistical physics, computational biology and information theory. Next we introduce principles of genetics and then apply them in clinical genetics and other large-scale sequencing projects. MIT also offers a higher-level course called “Performance Engineering of Software Systems” that focuses on performance analysis, algorithmic techniques for high performance, instruction-level optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems (http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-172-performance-engineering-of-software-systems-fall-2010). This topic falls outside the usual definition of bioinformatics, but the course has so many useful and interesting elements, including imaging, cell culture and tissue engineering, cardiovascular and renal physiology, and immunology, not to mention product development, that it is likely to make an intriguing elective. Even the most imaginative web technology will only go so far in this regard, and probably not far enough in the case of wet biology. Berkeley, Computer Science 61B, Prof. Paul Hilfinger (Fall 2011), http://webcast.berkeley.edu/playlist#c,d,Computer_Science,63AE13B304CE443E, “Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays, strings, and hash tables. Tom Strachan and Andrew Reed, now in its 4th edition, goes deeper into modern techniques [5]. The emphasis here is more on how the algorithms work than on how to use them. The University of Pennsylvania's Prof. Michael Kearns mixes a little game theory and network theory together in “Networked Life,” also on Coursera (https://www.coursera.org/course/networks). Differential and Integral Calculus. We start with the basics, DNA sequencing and the genome project, then move to high throughput sequencing methods and applications. View on publisher site Alert me about new mentions. While math packages can handle much of the dirty work, the real pros need to understand what's under the hood. It closely coordinates video lectures with detailed analysis exercises, with tutorial handouts and code supplied, using R and Bioconductor. Stanford, Prof. Jennifer Widom (Fall 2011), “This course covers database design and the use of database management systems for applications. Differential Equations is essential, and Linear Algebra highly recommended. (See individual course descriptions above for explanations of source abbreviations and further elaboration of requirements.) Kolmogorov complexity. Consequently a solid foundation in probability is de rigeur, particularly in preparation for data mining and machine learning applications. Moreover, cognitive psychology offers both a theoretical basis and empirical evidence for the benefits of multimedia learning [3]. James Wells and Michelle Arkin (http://www.ibioseminars.org/lectures/bio-techniques/james-wellsmichelle-arkin.html), “Imatinib (Gleevec) as a Paradigm of Targeted Cancer Therapies” by Dr. Brian Druker (http://www.ibioseminars.org/lectures/cell-bio-a-med/brian-druker.html), “Protein Kinases; Structure, Function, and Regulation” by Dr. Susan Taylor (http://www.ibioseminars.org/lectures/bio-mechanisms/susan-taylor.html), and “Seven Transmembrane Receptors” by Dr. Robert Lefkowitz (http://ibioseminars.org/lectures/cell-bio-a-med/robert-lefkowitz-1.html). Refreshers are widely available online, including vintage videos made four decades ago at MIT (when the author of this article was learning the subject there!) However, in the latter positions he was directly or indirectly responsible for hiring well over a hundred scientists and engineers for bioinformatics-related roles. Besides introducing machine learning, which should be pursued further in the next course listed, this course introduces knowledge representation, important as a foundation for biological ontologies; Bayesian nets, useful in biological network causal analysis; and natural language understanding, which is highly relevant to biomedical text mining. Eric Lander, Robert Weinberg, Tyler Jacks, Hazel Sive, Graham Walker, Sallie Chisholm, and Dr. Michelle Mischke (Fall 2011), http://ocw.mit.edu/courses/biology/7-01sc-fundamentals-of-biology-fall-2011, “Fundamentals of Biology focuses on the basic principles of biochemistry, molecular biology, genetics, and recombinant DNA. Thus, a useful adjunct to online learning in bioinformatics might be a portfolio of suggested projects based on real-world datasets that would help exercise the skills of trainees, perhaps in the context of an online community of peers. While lacking any videos, Stanford Prof. Russ Altman's course “Representations and Algorithms for Computational Molecular Biology” has a wealth of notes, slides, readings, and other useful links (http://helix-web.stanford.edu/bmi214-2006). UC Davis also offers a graduate-level algorithms course given by Prof. Gusfield (http://www.cs.ucdavis.edu/~gusfield/cs222f07/videolist.html). Asymptotic equipartition property. While there are almost 15 years' worth of these videos available for mining, the online student might be well advised to make a habit of tuning in to the live streaming of these events, for more of a flavor of the campus experience. Biochemical processes and principles in membrane structure and function, intracellular trafficking and subcellular compartmentation, cytoskeletal architecture, nucleocytoplasmic transport, signal transduction mechanisms, and cell cycle control.”, This upper-level Berkeley course in their Biochemistry and Molecular Biology track, which is subtitled “Macromolecular Synthesis and Cellular Function,” is a thorough introduction to basic cellular information processing and as such is important background for bioinformatics. A good textbook entitled “Introduction to Probability” by Swarthmore College Prof. Charles Grinstead and Dartmouth College Prof. J. Laurie Snell is available in a free online version [11]. Curriculum. Ragu Balakrishnan and Stephen Cauley (Summer 2009), http://www.networkmaths.ie/videos/list_videos.php?course=opt-2, “1) The basic optimization problem: a) general formulation, b) special cases, c) motivating examples. HMMs are not covered in the core courses above (though they are introduced in the course below), nor are there the same sort of user-friendly environments for HMMs, but there are toolkits that the bioinformatics student can use to study the associated algorithms, such as HMMoC (http://biowiki.org/HMMoC) or HMMConverter (http://people.cs.ubc.ca/~irmtraud/hmmconverter), as well as R packages. Note that the Stanford series as a whole thus teaches the languages Java, then C++, and finally a bit of LISP, Python, etc., while the Berkeley series does Python, then Java, and then C. The latter ordering is probably more appropriate for bioinformatics. There is an accompanying set of very polished course notes (http://cs229.stanford.edu/materials.html). Not only is natural language processing (NLP) technology important in biological text mining applications, but grammars and parsing are relevant to several aspects of sequence analysis. 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