3) Convex optimization: a) algorithms …, b) applications … 4) General optimization: a) mixed integer programming, b) algorithms and heuristics …”. In many cases, individual faculty members took the initiative to post course materials, including video, in widely varying formats. MIT, 6.046J, Profs. Some suggestions for individual tracks are indicated by plus signs in Tables 1–4. Google Tech Talks (http://www.youtube.com/user/GoogleTechTalks/videos) are another source of seminars, though with over 1,600 videos and little organization, it's necessary to use the search function judiciously. Profs. Not only are differential equations a mainstay of mathematical biology in areas such as enzyme kinetics and population dynamics, but they are the basis of many approaches to modeling of biological systems. Finally, the Computational Biology track may call for independent study in a variety of topics in advanced mathematics and computer science as well as biological background necessary for a particular specialization. For students with a biology background: e1002632. Unfortunately a few lectures are missing, but all the slides are separately available (http://perso.uclouvain.be/paul.vandooren/DublinCourse.pdf). Coursera is offering a two-part course from Princeton, by Profs. The primary attraction of this Coursera offering is its illustrious instructor, who literally wrote the book on automata (and on databases, on algorithms, etc.). It involves such skills as sequence, expression, and functional analysis by means of a standard bioinformatics tool set, as well as an ability to write computational scripts, database queries, and simple programs. UC Davis also offers a graduate-level algorithms course given by Prof. Gusfield (http://www.cs.ucdavis.edu/~gusfield/cs222f07/videolist.html). It aims to provide students with an understanding of the role computation can play in solving problems. Cost and quality metrics and estimation. Thus if any bias exists, it is probably in favor of the practical over the theoretical, though the author's own research is somewhat more in the latter category. There is an excellent set of slides in PDF format, which should be viewed in parallel with the video lectures, and a set of practical how-to videos as well. Courses listed as alternatives to the main courses still met basic standards of quality, and in addition to offering redundancy often had other features that might appeal to specific students, for instance in terms of areas of emphasis. It would be prudent for potential students to seek a variety of opinions. Moreover there is the practical issue that extending the analysis to paid courses would open up a much larger set of alternatives, most of which are inaccessible to evaluation without expenditure. *BIOF 501A | SPECIAL TOPICS IN BIOINFORMATICS Current graphics hardware, elementary operations in two-and three-dimensional space, transformational geometry, clipping, graphics system design, standard graphics systems, individual projects.”. 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. Ideas such as dominance, backward induction, Nash equilibrium, evolutionary stability, commitment, credibility, asymmetric information, adverse selection, and signaling are discussed and applied to games played in class and to examples drawn from economics, politics, the movies, and elsewhere.”. By way of evidence, a suggested curriculum will be laid out that is supported by existing online resources. The course is open to freshmen with excellent preparation in chemistry and physics, and it aims to develop both taste for original science and intellectual skills necessary for creative research.”. This course focuses on the equations and techniques most useful in science and engineering.”. Udacity is offering a similar introductory course by Stanford Prof. Sebastian Thrun (http://www.udacity.com/overview/Course/st101). In an online learning environment, direct interaction with peers is certainly possible after a fashion, through discussion logs and the like, but to date hasn't addressed such important educational elements as the development of public speaking skills. To this, the author can only plead editorial privilege, and remind the reader that these are opinions based on one person's experience in the field. He has long held that the subject of linear algebra should be given as much or more teaching emphasis than calculus and differential equations, and the rise of Big Data is now proving him correct beyond any doubt. degree will be required to fulfill the Bioinformatics and Computational Biology M.S. -2, Health Informatics: A Current and Historical Perspective, Health Informatics: Data and Interoperability Standards, String Processing and Pattern Matching Algorithms, Proteins: Alignment, Analysis and Structure. 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. While these may be overkill for bioinformatics, it might just inspire some to seek deeper insights into structures in large datasets. Finally, for some tracks, additional study is recommended to extend certain course topics (denoted by plus signs), as discussed below under Independent Study. Those who would like to focus immediately on data-driven scientific computing could do worse than “Advanced Scientific Computing with Python” taught by Berkeley Astronomy Prof. Joshua Bloom (http://itunes.apple.com/itunes-u/astronomy-250-001-spring-2012/id497766986); this course is not particularly tied to astronomy (which is wrestling with Big Data from sky surveys rather than omics), and introduces packages ranging from statistics to visualization to parallel computing, although the resolution of the videos may lead to eye strain. “In this course, students get hands-on experience in developing software for massively parallel computing resources. He is also a long-time instructor at Stanford, and does an introductory “Computer 101” course on Coursera (https://www.coursera.org/course/cs101), yet another alternative starting point. Principles of nuclear and organellar genome structure and function: regulation of gene expression in response to environmental and developmental stimuli; clonal analysis; investigation of the molecular and genetic bases for the exceptional cellular and developmental strategies adopted by plants.”, http://webcast.berkeley.edu/playlist#c,d,PMB,2B7E0C3DBF1D43ED, Berkeley, MCB C148, Profs. These lectures, which describe on-going research in leading laboratories, feature an extensive introduction to the subject matter, making them accessible to advanced undergraduates or beginning graduate students and researchers outside of the specific field. Adam Arkin and John Doyle gave “A Short Course on Mathematical Modeling of Signaling Mechanisms in Biology” at NIH (http://videocast.nih.gov/launch.asp?9948). Copyright: © David B. Searls. Stony Brook University, CSE 549, Prof. Steven Skiena (2010), http://www.algorithm.cs.sunysb.edu/computationalbiology, “This course focuses on current problems in computational biology and bioinformatics. What follows, then, is a virtual catalog for a course of study in bioinformatics. Randy Schekman, Kunxin Luo and David Drubin (Spring 2009), http://itunes.apple.com/itunes-u/molecular-cell-biology-130/id354820424, “This course is aimed at conveying an understanding of how cellular structure and function arise as a result of the properties of cellular macromolecules. The third course in the Stanford sequence is “Programming Paradigms” (http://see.stanford.edu/SEE/courseinfo.aspx?coll=2d712634-2bf1-4b55-9a3a-ca9d470755ee), which also delves into bit-level machine details and memory management using C and C++, but then also introduces the functional paradigm (with LISP) and concurrency, as well as surveying (briefly) other languages such as Python and C#. Coursera, also now live, is being stocked with courses from academic partners Stanford, Princeton University, the University of Pennsylvania, and the University of Michigan; this list was recently augmented with a tranche of a dozen more top-tier universities. This course does not actually have any video in its original form, but a version of it re-labelled “Social Network Analysis” is being offered on Coursera in Fall 2012 (https://www.coursera.org/course/sna). Qualified students have the option to test out of BMI 713, Computing Skills for Biomedical Sciences, by completing an online assessment 1 month prior to matriculation. Optimization is a vast field, often associated with operations research or engineering disciplines but not seen as a core aspect of bioinformatics to date. This brief overview will be a useful elective for bioinformatics practitioners interested in drug discovery and/or translational research, from either a scientific or employment standpoint. 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). These principles are necessary to understanding the basic mechanisms of life and anchor the biological knowledge that is required to understand many of the challenges in everyday life, from human health and disease to loss of biodiversity and environmental quality.”. For a more computational approach, Prof. John Sowa has a well-organized but text-only “Guided Tour of Ontology” (http://www.jfsowa.com/ontology/guided.htm) that includes readings from his book “Knowledge Representation” [40]. Stanford Engineering has two advanced courses in “Convex Optimization” by the estimable Prof. Stephen Boyd (http://see.stanford.edu/see/courseinfo.aspx?coll=2db7ced4-39d1-4fdb-90e8-364129597c87 and http://see.stanford.edu/see/courseinfo.aspx?coll=523bbab2-dcc1-4b5a-b78f-4c9dc8c7cf7a); the text book is available online for free [24]. Although the technologies continue to evolve rapidly, this course provides both practical experience in recent tools and good discussions of general considerations that will carry over to whatever comes down the pike next. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.”. Stanford Prof. Daphne Koller, the other academic co-founder of Coursera, is offering a course on “Probabilistic Graphical Models,” another important flavor of machine learning that includes Bayesian nets and Markov random fields, which already have had significant impact in network bioinformatics (https://www.coursera.org/course/pgm). Storage management. Princeton on Coursera, Prof. Michael Sedgewick (Spring 2013), https://www.coursera.org/course/introACpartI, “Analytic Combinatorics aims to enable precise quantitative predictions of the properties of large combinatorial structures. Programming proficiency in an object-oriented language such as Java, C#, C++, Python, or Ruby. UC Davis also offers advanced courses through their Institute for Data Analysis and Visualization, including “Graphics Architecture” (http://itunes.apple.com/us/itunes-u/graphics-architecture-winter/id404606990), which does GPUs in-depth; “Geometric Modeling” (http://itunes.apple.com/us/itunes-u/computer-science-introduction/id389259246); and “Advanced Visualization” (http://itunes.apple.com/us/itunes-u/advanced-visualization-ecs277/id389259186). Educational grants for the creation of virtual laboratories to enrich the online learning experience might be public (or philanthropic) money well spent. Thomas Alber, Qiang Zhou and Qing Zhong (Fall 2009), http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?id=354820440, “Molecular biology of prokaryotic and eukaryotic cells and their viruses. On completion of the course, a short assessment will provide an opportunity to demonstrate what you have learned. A still more comprehensive treatment of graph theory proper is offered by Prof. L. Sunil Chandran of IISc Bangalore through NPTEL (http://nptel.iitm.ac.in/courses/106108054). The U.S. Yes His magisterial self-published textbook for these courses includes a treatment of microarray analysis to discover “eigengenes” [9]. Learning from course notes only, or even disembodied audio, simply doesn't have the immediacy of the visual experience of a lecture hall or even a tablet-based screencast. The courses above offer taster menus of various aspects of computer science and only basic programming skills, and as such are appropriate for bioinformatics professionals who need exposure to programming but will not be doing it for a living. These are the resources I am using: 1. There may well be other paths, and certainly a variety of more specialized ones, but these broad categories would seem to be a useful start. UCLA Prof. Bob Goldberg teaches an honors collegium entitled “Genetic Engineering in Medicine, Law, & Agriculture” that focuses on a range of legal and ethical issues in biotechnology (http://www.mcdb.ucla.edu/Research/Goldberg/HC70A_W12/videos.php). See also a seminar by Dr. Sangeeta Bhatia on “Tissue Engineering” (http://www.ibioseminars.org/lectures/cell-bio-a-med/sangeeta-bhatia.html). Electives are at the option of the student, but certain of these are indicated as recommended, and several at least should be taken as time permits. A friendlier user environment is provided by tools like Weka (http://www.cs.waikato.ac.nz/ml/weka), widely used in teaching, or Orange, which has add-ons for bioinformatics and text mining (http://orange.biolab.si); both are open source. Students should obtain and become proficient in machine learning tools, which can be done from R or Octave (as a free alternative to MatLab) environments (see above). This NPTEL course offers a significantly more detailed view of gene regulation than the courses above, though it overlaps with them. As is the case for biology, there are myriad individual seminars online in computer science. Even so, the reader has a right to question both the author's qualifications and methodology in offering these opinions. But the offerings are only getting better and more numerous, and so any imperfections in the current collection should be increasingly easy to correct with the passage of time. Prof. Ullman recommends portions of his free online textbook “Foundations of Computer Science” as preparation [15]. Bioinformatics is the application of mathematical, statistical, and computational approaches to understand biological processes. The trend to structured presentation and high production quality then accelerated remarkably, and took an entrepreneurial turn. Basic elements of the theory are important in machine learning approaches to data mining and appear frequently in bioinformatics tools and algorithms, including sequence motif analysis and many other applications. The second entry, “Microbial Genetics and Genomics,” starts halfway through the actual course with the lectures of Prof. Glass, focusing on comparative genomics, and includes an extended exercise in annotation of a new microbial genome from the Joint Genome Institute. The University of Illinois at Urbana-Champaign conducted a Summer School on “Computational Approaches for Simulation of Biological Systems” in 2003 that posted a number of videos relating to biophysical modeling and bioinformatics analyses of macromolecular structures, a topic otherwise underrepresented here (http://www.ks.uiuc.edu/Training/SumSchool/lectures.html). An Online Bioinformatics Curriculum.pdf. Recent meetings include ones on Machine Learning in Systems Biology, Cancer Bioinformatics, Pattern Recognition in Bioinformatics, Learning and Inference in Computational Systems Biology, and many more, amounting to a total of some 200 talks to date. Sarda, Umesh Bellur, and Rushikesh Joshi through NPTEL (http://nptel.iitm.ac.in/video.php?subjectId=106101061). Primary topics will include DNA sequence assembly, DNA/protein sequence assembly, DNA/protein sequence comparison, hybridization array analysis, RNA and protein folding, and phylogenic trees.”. Analysis of Variance.”. This course exposes students to techniques of abstraction at several levels: (a) within a programming language, using higher-order functions, manifest types, data-directed programming, and message-passing; (b) between programming languages, using functional and rule-based languages as examples.”. In any case, most of the courses below require reading at least selections from one or more textbooks in close coordination with the lectures (though in a surprising number of cases the textbooks are freely available online). The “Machine Learning Summer School” that took place at Cambridge University in 2009 has 20 introductory and specialized tutorials of 2–3 hours each in a coordinated video and slide format (http://videolectures.net/mlss09uk_cambridge). To post a program offered by your institution please use this form . The book “Human Molecular Genetics” by Drs. Hanspeter Pfister and Nicolas Pinto (Spring 2011), http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?id=429428651, Provider description. Biologists nowadays often have some training in bioethics but for computer scientists it may be more novel, yet increasingly important given new capacities for mining Big Data. https://doi.org/10.1371/journal.pcbi.1002632, Editor: Fran Lewitter, Despite the title of this course, it brings hardware into the picture only as it relates to designing fast and memory-efficient code. MIT, 18.06SC, Prof. Gilbert Strang (Fall 2011), http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011, “This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering.”. Course will only be necessary for serious theorists a relatively short but well-constructed course that yet. Acclaimed teacher and the material covered is absolutely central to current network theory of microarray analysis to discover “ ”... 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