CBio - Lesson 15 : Phylogenetic distance
evaluation
Course Teacher: Prof. Nir Friedman
Scribed by Roni Rasnic
09/12/13
Part I : Genomic transformation as a time matrix
Last week, we saw that the probability for a change in the genome sequence is
not linea
Introduction to Computational Biology
Lecture 11: Incomplete data and EM
Course Teacher: Prof. Nir Friedman
Scribed by Ravid Ziv
18/11/13
1
1.1
First section: Incomplete data problems
Denition of the problem
We have a Markov chain H1, H2 , .Hn and we dene
Lecture #9 : Recognizing GC Rich Areas
Scribed by Or Vainshtein
November 25, 2013
lets build an HMM that outputs a sequence with areas poor and rich with
GC characters
q > r.
x1 .x2n+m : An Gm An
In this case it clear that the picture above describes what
Introduction to Computational Biology Lecture
12: Information theory and EM algorithm
Scribed by: Miriam Manevitz 19/11/13
December 18, 2013
Information theory
Information theory is a branch of applied mathematics which was
developed in parallel to modern
CBio - Lesson 12: Practical Use of EM
Scribed by: Ma'ayan Baral
26/11/13
1
Introduction
As we learnt, guessing the initial parameters can eect the nal result given by
the EM algorithm.
1.1
Example
In order to give some intuition, let's take a look at a si
CBio - Lesson 14
Course Teacher: Prof. Nir Friedman
Scribed by Tal Arian
2/12/2013
Part I
Phylogenetic Tree Reconstruction
1
Introduction
As continuous to the previous lesson we will discuss about phylogenetic tree reconstruction:
From our data ( sequence
Introduction to Computational Biology
Lecture 14: Evolution
Course Teacher: Prof. Nir Friedman
Scribed by Se Mintzer
2/12/13
Part I
Evolutionary Trees
The evolutionary studies deal with the question - how things became what they
are today?
A tree can be u
HMM and Forward Algorithm
Elkana Baris
05/01/2013
0.1
introduction:
Until now we assumed that statistics of a given series (sequence), can be modeled
as cube throwing statistics,
A quite naive model about that probability p(x1 .xn ) = x1 x2 . xn s.t. the
CBio - Lesson 2
Course Teacher: Prof. Nir Friedman
Scribed by Tal Arian
15/10/2013
Part I
Sequence alignment
1
The alignment problem
As continuous to the previous lesson we will now dene formally the sequence alignment
problem: let : ( cfw_)2 R be a ranki
CBio - Lesson 1
Course Teacher: Prof. Nir Friedman
Scribed by Se Mintzer
14/10/13
Part I
Course Guidelines
The course will be held twice a week:
Monday 10:00-11:45, Tuesday 12:00-13:45.
Course grade:
25% scribes, 45% homework, 30% nal exam, +5 points bo
CBio - Lesson 3
Course Teacher: Prof. Nir Friedman
Scribed by Roni Rasnic
22/10/13
Part I : Motivation
Last week we saw a specic example for grading a nucleotide sequence alignment.
We used a scoring matrix which was based on the following grading functio
Lesson 4
Course Teacher: Prof. Nir Friedman
Scribe: Ayelet Blass
November 17, 2013
Part I
Estimation Problem
This is a branch of statistics that deals with estimating parameters.
Example 1. Assuming there are many letters what is the probability of there
Lecture #6: Heuristics, Branch & Bound
Noa Koman
November 23, 2013
1 First Section: Heuristics
So far we studied an algorithm for global and local alignment with time complexity T ime = O(n m) where n stands for the size of the query and m is the
size of
CBio - Lesson 5
Course Teacher: Prof. Nir Friedman
Scribed by Yair Deitcher
29/10/13
Part I
Reminder
In the last lectures we formulated the following ranking function :
(a, b) = log
P1 (a, b)
P0 (a) P0 (b)
This function receives as an input two amino aci