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That's not a lot of bioinformatics programs that I'm seeing. A lot of bachelors programs seem to focus on teaching almost exclusively the basics of BLAST and all it's boring related algorithms (basically everything in this Coursera course) and their mathematical foundations. Master's programs sometimes are a bit better with a hint of ML, but ultimately most people I've encountered there are still awfully unequipped to tackle ML problems and transfer the advances from mainstream ML to biology/biochemistry problems.
⬐ asdffAny bioinformatics program covers machine learning these days. They might not have an explicit class called 'machine learning,' but you can bet it will be covered in the lecture sequence and the cutting edge of the field will be discussed in journal clubs, rather than in lectures which are about established fundamentals.
For a pure biology undergrad who is probably med school bound, learning ML is superfluous so you don't see it in the curriculum at the undergrad level, unless there are specific concentrations offered for computational biology. A bioinformatics program may even just have you take these ML classes from the statistics or CSE department rather than offer some bioinformatics-specific section within their department.⬐ orange3xchickenI agree with this, and this was true historically as well - e.g. Biometrika - a premier journal in statistics - grew out of bioinformatics-like research.
Much of the groundwork laid for online learning & statistics/bandit algorithms/modern reinforcement learning was produced by biostatisticians working on techniques for efficient experiment design (e.g. Thompson Sampling during the 1930s in Biometrika).
The Honors Track of the UCSD series is really great.
It's super hard and as a side effect you learn a ton about very interesting, amazing, and useful algorithms that you'd never even hear about in a top notch CS program.
⬐ anderspitmanSurprised this is buried so deep. These courses are excellent.⬐ EugeleoThat sounds great! I'll check it out tomorrow.
Some people here in Europe take bioinformatics as a shorthand of "database management, pipeline construction, and scaffold building" --- I'm glad to see the course is more algorithm oriented (maybe with a bit of DS thrown in as well).
If you're a programmer/CS person interesting in genomics/bioinformatics, I can't recommend UCSD's Coursera courses enough.
The UCSD bioinformatics algorithms courses on Coursera are fantastic: https://www.coursera.org/specializations/bioinformatics
Open Source Projects/Platforms:
ADAM (Big Data Genomics, code-base I think worked currently by UC Berkeley AMPLab and Microsoft Research group focused on cancer research) https://github.com/bigdatagenomics/adam/labels/pick%20me%20u...
BioConductor (R package that is very popular for crunching different genomic analysis, RNA-Seq)https://www.bioconductor.org/developers/
Galaxy (Platform for reproducible genomics workflows in the cloud)https://wiki.galaxyproject.org/Support
Public Available Data Sets:
Cancer Genome Atlas (contains expression data for different kinds of cancer) http://cancergenome.nih.gov/
NCBI Short Reads Archive (contains the genome assembly of a lot of published papers): https://www.ncbi.nlm.nih.gov/sra
European Nucleotide Archive: http://www.ebi.ac.uk/ena, e.g.,
For a recent major paper, "The Simons Genome Diversity Project: 300 genomes from 142 diverse populations"; you can grep for "accession code"(http://www.nature.com/nature/journal/vaop/ncurrent/full/natu...), follow the link and download the whole raw assemblies of all samples here: http://www.ebi.ac.uk/ena/data/view/PRJEB9586
NYGC, JCVI, CRB, Sanger, BGI, Broad, NIH, NCBI, Janelia Farm; any research group in universities; any informatics job at pharmaceutical or pharma startups.
My all star lineup would be:
The Analytics Edge - https://www.edx.org/course/analytics-edge-mitx-15-071x-0
Design of Computer Programs - https://www.udacity.com/course/design-of-computer-programs--...
If I had more time I would love to go through the bioinformatics specialization on Coursera. They have 2 books and an exercise site (rosalind.info). It looks like great fun.
⬐ geomark+1 for The Analytics Edge.
Along the same lines but a more thorough treatment of linear regression and statistical inference is the excellent Data Analysis and Statistical Inference https://www.coursera.org/course/statistics⬐ amykharDesign of Computer Programs was decidedly the best course I ever took online. I was lucky enough to take it when it was first offered. Peter Norvig was very active in the course forum.