Reddit reviews Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

CLRS for algorithms/CS.

Probability and random processes for statistics.

Biological Sequence Analysis by Richard Durbin for my subfield of bioinformatics.

"Biological sequence analysis" by Durbin, Eddy, Krogh and Mitchison:
https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

also this paper by Krogh et al. might be useful:
https://pdfs.semanticscholar.org/0f88/43d140cb3b3c14afdbe0486b27d2f1016eb3.pdf

and this HMM tutorial by Rabiner:
http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf

May I ask how you are beginning to skim the surface of ML? If you're reading methods papers or something, I could see how you could start to feel like it was all really esoteric. There are a lot of more applied journals and conferences out there, even for specific fields like biology. Maybe something in your field would be a good entry point?

There are tons of ML methods that are super generalizable-- not at all overly specific. At my work (biotech), people use off-the-shelf computer vision algorithms (segmentation, registration, etc.) all the time. They use clustering and classifiers as well. Classifiers in particular are super easy to use off-the-shelf. A lot of these tools have been incorporated into statisticians bags of tricks. Certain areas of ML really do feel like "new stats" to me.

Bayesian networks is another one that is pretty broadly applicable, and sees a lot of use in computational biology. E.g. inferring gene regulatory networks, modelling genetic diversity, etc. There are bioinformatics books out there that are chock full of ML-flavored algorithms; this one is a classic-- http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713 though I'm not sure it'd be quite up your alley for synthetic & systems bio.

Googled and found a couple conferences-- might be worth skimming the proceedings

http://mlsb.cc/

http://www.eccb14.org/program/workshops/mlsb

Oh right. I slightly misunderstood what you were looking for.

This may or may not be helpful but here's another recommendation:

https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

While this book is pretty old and does not cover newer technologies and algorithms, I found it extremely helpful in understanding biological principles related to genetics and molecular evolution.

In this genomics era, we know so much more than just genetics now of course. But I mostly learned about genomics by reading tons of review papers, not a textbook. Once you study a bit on basic biology, I think reading review papers is the way to go if you want to delve into a more specific topic.

If you want to browse widely used genomic/bioinformatic resources then look at NCBI, Ensembl, and UCSC.

If you want to try some bioinformatics problems, then see Rosalind.

If you want to learn biology, then buy textbooks on genetics/molecular biology. There are many, I recommend [this] (https://www.amazon.com/Human-Evolutionary-Genetics-Origins-Peoples/dp/0815341857) for human evolution.

If you want to learn about methods and sequence analysis, then [Biological Sequence Analysis] (https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713) is excellent.

If you want to explore widely used bioinformatics tools, then start with BLAST if you don't know it already.

I don't work in genomics specically but we do a lot of next generation sequencing. I am a biologist with interests in machine learning so let me try to summarize where people in biology use it:

Microarrays: Cancer research in particular uses this, but basically every biology discipline has some applications of this. Basically what you get is thousand of signal intensities, each represeinting expression of a gene, per sample, and what you are interested in is finding genes that behave differently from sample to sample. This is an example of a high-dimensionality problem, where the number of features is much larger than the number of samples. If you want some idea of how much work has been done in this area take a look at this (list)[http://www.geneontology.org/GO.tools.microarray.shtml]. You can more or less find all kinds of statistical methods here. As a biologist i should probably mention that i believe micro-arrays have problems with reproducibility that no amount of data-analysis will solve.

Gene prediction: This is a typical genomics problem in which we are given a long DNA sequence and told to identify the genes in it. Genes have some telltale signs, but these can be located with slight differences to each other and might be completely absent. Also, genes in eukaryotes are interrupted by socalled introns that do not code for genes (this story is a lot longer in reality). Poisson statistics on dna words (k long subsequences of dna) is the classical way of finding overrepresented dna features. Newer techniques uses HMMs and conditional random fields, as machine learning oriented as it gets. (This)[http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713] is a modern classic in all things sequence related.

Phylogeny: This is another of bioinformatics major contributions to modern science. Given some model of how evolution changes the composition of a sequence, we are interested in figuring out how organisms/proteins/genes can be related and building trees that can show us these relation.

Next generation sequencing: We can now generate much more data than we can process, we need some way of filtering as the machines can be inaccurate. We also need methods to cluster sequences within specific thresholds.

Sequence searching: This is a major topic. The most cited paper in the history of science is the one that announced BLAST. Machine learning is not as used here yet, but it probably will be if something faster than the traditional alignment algorithms come up.

This was just a short and incomplete overview, if you have specific questions i would be happy to answer.

Durbin's book, Biological Sequence Analysis is very good.

Some books on stochastic processes are useful.

Some HMM material

Also, a few journals that I read: Genetics, PLoS Biology/Genetics, MBE, GBE, and Theoretical Population Biology. If access is a problem, look to PLos, arxiv, bioRxiv.

For the basics, I still think this is one of the best.

https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

Courses:

Take population genetics and computational biology. Population genetics focuses on dynamics of allele frequencies in different populations. Computational biology is anything from simulating networks of biochemical reactions to identifying patterns in DNA using hidden markov models.


Books:

http://www.amazon.com/Introduction-Systems-Biology-Mathematical-Computational/dp/1584886420/ref=sr_1_1?ie=UTF8&qid=1299531700&sr=8-1

http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713/ref=sr_1_1?s=books&ie=UTF8&qid=1299531747&sr=1-1

You can try this one:

https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

It introduces one of the key problems in bioinformatics and should be fairly readable for someone of your background. It primarily adresses your first bullet, and does not go into many details about implementations,

My one line advice: take an introductory molecular biology course or genetics course.

If you have a summer, do this:

Read "The 8th Day of Creation" if you want to learn the whole of molecular biology's roots in one book. It is written on the college-educated "lay" reader level and details the history vividly.

Then check out and admire some websites:

genome.ucsc.edu

wormbase.org

flybase.org

start messing around with it and get a feel for the scope of it. Others can add more.

Then, there are some textbooks. I know people who like this one