Posts Tagged ‘cbb752’
Historical notes and algorithm development
The original purpose of the algorithm described by Needleman and Wunsch was to find similarities in the amino acid sequences of two proteins.
Needleman and Wunsch describe their algorithm explicitly for the case when the alignment is penalized solely by the matches and mismatches, and gaps have no penalty (d=0). The original publication from 1970 suggests the recursion …
A better dynamic programming algorithm with quadratic running time for the same problem (no gap penalty) was first introduced by David Sankoff in 1972. Similar quadratic-time algorithms were discovered independently by T. K. Vintsyuk in 1968 for speech processing ("time warping"), and by Robert A. Wagner and Michael J. Fischer in 1974 for string matching.
Visualization of Power Analysis http://amarder.GITHUB.io/power-analysis/ Useful sliders giving one a feel of the #statistics
How does multiple-testing correction work
http://www.nature.com/nbt/journal/v27/n12/abs/nbt1209-1135.html Intuition for teaching: genome-wide error rate on a single gene v family
The role of regulatory variation in complex traits and disease : Nature Reviews Genetics : Nature Publishing GroupJune 12, 2016
Reg. variation in cplx traits by @LeonidKruglyak
http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html nice teaching figure for #eQTLs, showing how mostly cis + hotspots http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html
Plant endophytes as a platform for discovery-based undergraduate science education : Abstract : Nature Chemical BiologyMarch 26, 2016
Ex. of extreme project oriented course
What’s the EM #algorithm?
http://www.nature.com/nbt/journal/v26/n8/full/nbt1406.html Description of its essence in simple contexts (ie coin toss) & as soft version of kmeans
What is the expectation maximization algorithm? : Article : Nature Biotechnology
Nature Biotechnology 26, 897 – 899 (2008)
Chuong B Do & Serafim Batzoglou
The expectation maximization algorithm arises in many computational biology applications that involve probabilistic models. What is it good for, and how does it work?
without too much math
quick video for visualizing the power of kernels in SVM