Nature report “Much about James Franklin Crow, who died on 4 January two weeks short of his 96th birthday, challenges our sense of scale. Over seven decades, he contributed to an astonishing array of topics in genetics, and the list of his students and postdocs reads like a who’s who. One of them, the pioneering geneticist Motoo Kimura, wrote that getting Crow as his adviser after a period of uncertainty was such a joy it was like “meeting Buddha in Hell”. Crow also played the viola for 45 years with the Madison Symphony Orchestra. He once performed with the great violin soloist Yehudi Menuhin.”

http://www.nature.com/nature/journal/v481/n7382/full/481444a.html

Genetics: WITH this issue we begin a series of Perspectives and Review articles honoring our colleague James F. Crow, who, along with this journal, celebrated his 95th birthday this year. Why honor Jim? The answer is obvious to the many who have the privilege to know him: a gentleman and scholar of the highest order, he represents the best of our field.

http://www.genetics.org/content/189/4/1127.full

UW-Madison: The UW-Madison community is mourning the loss of a legend: James F. Crow, professor emeritus of genetics, who passed away peacefully at his home on Jan. 4, two weeks shy of his 96th birthday.

http://www.news.wisc.edu/20193

NYT: James F. Crow, a leader in the field of population genetics who helped shape public policy toward atomic radiation damage and the use of DNA in the courtroom, died last Wednesday at his home in Madison, Wis. He was 95.

http://www.nytimes.com/2012/01/11/science/james-f-crow-population-genetics-pioneer-dies-at-95.html

Following code are for r times n-fold CV:


n <- length(y) # sample size
folds <- trunc(folds) #the # number of folds
if (folds < 2) stop("folds should be greater than or equal to 2.\n")
if (folds > n) stop("folds should be less than or equal to the number of observations.\n")
cuts <- cv.repeats <- list()
set.seed(1234)
for(r in 1:repeats)
{
perm <- sample(1:n) #Sampling a random integer between 1:n
repeat.models <- NULL
for(k in 1:folds) #randomly divide the training set in to 10 folds
{
tst <- perm[seq(k, n, by=folds)] #
trn <- setdiff(1:n, tst)
cuts[[k]] <- list(trn=trn, tst=tst)
}
cat('Starting classification of repeat:',r,'\n')
if(parallel) repeat.models <- mclapply(1:folds, classify, cuts=cuts, pb=pb, x=x, y=y,cv.repeat=r, ...)
else repeat.models <- lapply(1:folds, classify, cuts=cuts, pb=pb, x=x, y=y, cv.repeat=r, ...)
if(length(repeat.models) != folds)
{ geterrmessage()
stop("One or more processes did not return. May be due to lack of memory.\n")
}
cat('All models of repeat:',r,'have been trained.\n')
cv.repeats[[r]] <- repeat.models
}


pseudocode for Greedy approach (suboptimal solution)

Define overlap ( si, sj ) as the length of the longest prefix of sj that matches a suffix of si.

1. Calculate pairwise overlap of strings
2. Merge a pair with maximum overlap
3. Repeat 1. – 3. until there is only one string

R codes:

#///////////////////////////////////////////////////////////////////#
#//  Overlap alignment for SSP                                      #
#//                                                                 #
#// Pseudocode for Greedy approach (suboptimal solution):           #
#//        Input sequences S={s1,s2,...,sn}                         #
#//        while(S!={})                                             #
#//            Calculte pairwise overlap of strings                 #
#//            Merge a pair with maximum overlap                    #
#//        endwhile                                                 #
#//                                                                 #
#// Program author: Yupeng Cun                                      #
#// Create data:  20,Apr.,2011                                      #
#// Latest update:                                                  #
#///////////////////////////////////////////////////////////////////#

The goals of the Human Genome Project were to learn the order of the 3 billion units of DNA that go into making a human genome, as well as to identify all of the genes located in this vast amount of data. By 2003, almost all of the pairs of chemicals that make up the units had been put in the correct sequence—enough for a pronouncement of success. The individual genes within the long strands of DNA, and the elements that control the genes, are still in the process of being identified. Current counts indicate that the human genome contains 22,000 to 23,000 genes.

One of the early hopes of the genomic project was to pinpoint specific genes that caused common diseases. Scientists now think the answer is more complex, with many diseases the result of multiple genes interacting. Nevertheless, the information garnered from the genome project has the potential to forever transform healthcare. Many believe that genome-based medicine, frequently called personalized medicine, is the future of healthcare—the next logical step in a world in which more is known about human genetics, disease, and wellness than ever before.

Of all the scientific and social promises that stem from advances in our understanding of the human genome, genomic medicine may be the most eagerly awaited. The prospect of examining a person’s entire genome, or at least a large portion of it, in order to make individualized risk predictions and treatment decisions is tantalizingly within reach.

This discussion will explain the genomic basis of personalized medicine and explore its potential for good as well as its possible risks.

1. What is the human genome?
2. What is personalized medicine?
3. Is personalized medicine only for sick people?
4. How might I get my genomic profile or learn my predisposition for certain medical conditions? What things should I consider before doing so?
5. Where can I look for more information about personalized medicine and genome science?

What is the human genome?

The human genome is the blueprint for each person’s body, influencing how we look, our genetic predispositions for certain medical conditions, how well our bodies fight disease or metabolize food, and which therapies our bodies do and do not respond to.

The genome consists of an organism’s total DNA, including its genes. DNA—the famous “double helix”—is composed of four chemicals, which are repeated many times in different sequences. (The names of the chemicals are abbreviated as A, T, C, and G. That’s why DNA is sometimes referred to as a code with a four-letter alphabet.) The sequence of the chemicals dictates the type of organism that develops, as well as other critical life functions. The human genome contains approximately 3 billion pairs of these chemicals.

Genes are believed to make up only about 2 percent of the human genome, with the rest consisting of “noncoding” regions, thought to regulate the function of genes and contribute to the structural integrity of chromosomes.

2. What is personalized medicine?

Personalized medicine is a young but rapidly advancing field of healthcare that is informed by each person’s unique clinical, genetic, genomic, and environmental information. Because these factors are different for every person, the nature of diseases—including their onset, their course, and how they might respond to drugs or other interventions—is as individual as the people who have them.

Personalized medicine is about making the treatment as individualized as the disease. It involves identifying genetic, genomic, and clinical information that allows accurate predictions to be made about a person’s susceptibility of developing disease, the course of disease, and its response to treatment.

In order for personalized medicine to be used effectively by healthcare providers and their patients, these findings must be translated into precise diagnostic tests and targeted therapies. This has begun to happen in certain areas, such as testing patients genetically to determine their likelihood of having a serious adverse reaction to various cancer drugs.

Because the 2003 sequencing of the human genome provided crucial insight into the biological workings behind countless medical conditions, scientists and physicians are advancing the field of personalized medicine at a fast pace. It is not yet an established part of clinical practice, but a number of top-tier medical institutions now have personalized medicine programs, and many are actively conducting both basic research and clinical studies in genomic medicine.

Specific advantages that personalized medicine may offer patients and clinicians include:

• Ability to make more informed medical decisions
• Higher probability of desired outcomes thanks to better-targeted therapies
• Reduced probability of negative side effects
• Focus on prevention and prediction of disease rather than reaction to it
• Earlier disease intervention than has been possible in the past
• Reduced healthcare costs

Personalized medicine is not to be confused with “genetic medicine.” Genetics, a field more than 50 years old, is the study of heredity. It examines individual genes and their effects as they relate to biology and medicine. “Single cell” genetic diseases include muscular dystrophy, cystic fibrosis, and sickle cell anemia. (However, even these seemingly “simple” hereditary disorders can be influenced by other genes, as well as by environmental factors such as diet and exposure to toxins.)

Genomic and personalized medicine aims to tackle more complex diseases, such as cancer, heart disease, and diabetes, for years believed to be influenced primarily by environmental factors and their interaction with the human genome. It is now understood that because these diseases have strong multigene components—and in some cases might be caused by errors in the DNA between genes instead of within genes—they can be better understood using a whole-genome approach.

3. Is personalized medicine only for sick people?

Definitely not. Because an individual’s genome influences his or her likelihood of developing (or not developing) a broad range of medical conditions, personalized medicine focuses strongly on wellness and disease prevention.

For example, if a person’s genomic information indicates a higher-than-average risk of developing diabetes or a particular form of cancer, that person may choose a lifestyle, or sometimes be prescribed medications, to better regulate the aspects of health and wellness over which he or she has control. The person may benefit in the long run from making preventive lifestyle choices that will help counteract the biological risk.

Genomic medicine may help determine a person’s risk of developing several specific medical conditions, including:

• Cancer
• Cardiovascular disease
• Neurodegenerative diseases
• Diabetes
• Obesity
• Neuropsychiatric disorders

Researchers are actively investigating the genomic and genetic mechanisms behind—and developing predictive testing for—such diverse medical conditions as:

• Infectious diseases, from HIV/AIDS to the common cold
• Ovarian cancer
• Cardiovascular disease
• Diabetes
• Metabolic abnormalities
• Neuropsychiatric conditions, such as epilepsy
• Adverse drug reactions
• Environmental exposure to toxins

4. How might I get my genome profile or learn my predisposition for certain medical conditions? What things should I consider before doing so?

If you are interested in learning about your genome profile or your genetic risk for specific medical conditions, start by speaking with your physician. Other resources include genetic counselors and reputable medical centers with genome science or personalized medicine programs.

In addition, a number of companies that test the DNA of paying consumers—such as deCode Genetics, 23andMe, and Navigenics—are being launched around the world. However, caution is advised in using these services. While some states now regulate direct-to-consumer genetic tests, regulation is not yet standardized. Experts recommend that consumers seek interpretation of test results from a professional who specializes in this type of testing.

Even on-site at your doctor’s office, genetic analysis or genome profiling can raise a number of questions relating to ethics, privacy, quality of life, and whether or not findings currently have any real clinical value.

While some people may not wish to know their genetic risk for some conditions—such as their chances for developing a devastating and untreatable condition such as Alzheimer’s disease—others may find such information has personal utility. For instance, they may use the knowledge they gain to plan for the future. Our DNA can provide other insights, as well, such as who our ancestors were.

The Genetic Information Non-Discrimination Act of 2008 (GINA) prohibits the use of genetic/genomic information by health insurance companies for determining a person’s eligibility for insurance or determining insurance premiums—as well as by employers for making decisions about functions such as hiring and firing, assigning jobs, and promoting and demoting. Because consumers should now be able to obtain their genome profiles and other genetic information without fear of retribution, GINA is reinvigorating the field of genomic testing.

Both medical providers and members of the public must better understand these important topics before widespread genetic testing is conducted and made a standard of medical practice. As familiarity with genetic and genomic testing grows, experts will need to carefully assess its utility for patient care to make informed decisions about how to best integrate them into medical practice.

5. Where can I look for more information about personalized medicine and genome science?

There are a number of valuable online resources about genome science and its implications for personalized medicine, with definitions, illustrations, statistics, and frequently asked questions. Those resources include:

• About the Human Genome Project
• Age of Personalized Medicine
• Duke Institute for Genome Sciences and Policy
• Duke Personalized Medicine Web site
• Genetics and Genomics for Patients and the Public (National Human Genome Research Institute)
• National Office of Public Health Genomics (Center for Disease Control and Prevention)

Last reviewed on 1/20/11

Lecture 3

lecture 4

for more lectures, see here:
Machine Learning by StanfordUniversity

http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

http://www.youtube.com/p/A89DCFA6ADACE599?hl=en_US&fs=1

• International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).
• Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

. last issue on Nature and Science published lost of paper and news of the progress in this area.  Human Genome project(HGP) do bring the genome technology to all ares in biology and changing the research style. So many data come form the high-through put machine, its a golden age for computational biologist. See more form here:

• Science: A Celebration of the Genome, Part I: http://www.sciencemag.org/content/331/6017/546.1.full?rss=1
• Nature:The genome at ten, http://www.nature.com/news/specials/humangenome/index.html

Initial impact of the sequencing of the human genome
Eric S. Lander

On 15 February 2001, a decade ago this week, Nature published a 62-page paper entitled ‘Initial sequencing and analysis of thehuman genome’, reporting a first global look at the contents ofthe human genetic code. The paper1 marked a milestone in the inter-national Human Genome Project (HGP), a discovery programme con-ceived in the mid-1980s and launched in 1990. The same week, Science published a paper2 from the company Celera Genomics, reporting a draft human sequence based on their own prodigious data, as well as data from the public HGP.
The human genome has had a certain tendency to incite passion and excess: from early jeremiads that the HGP would strangle research by consuming the NIH budget (it never rose to more than 1.5%); to frenzied coverage of a late-breaking genome race between public and private protagonists; to a White House announcement of the draft human sequence in June 2000, 8 months before scientific papers had actually been written, peer-reviewed and published; to breathless promises from Wall Street and the press about the imminence of genetic ‘crystal balls’ and genome-based panaceas; to a front-page news story on the tenth anniversary of the announcement that chided genome scientists for not yet having cured most diseases.

The goal of this review is to step back and assess the fruits of the HGP from a scientific standpoint, addressing three questions: what have we learned about the human genome itself over the past decade? How has the human sequence propelled our understanding of human biology, medicine, evolution and history? What is the road ahead?

The past decade has shown the power of genomic maps and catalogues for biomedical research. By providing a comprehensive scaffold, the human sequence has made it possible for scientists to assemble often fragmentary information into landscapes of biological structure and function: maps of evolutionary conservation, gene transcription, chromatin structure, methylation patterns, genetic variation, recombinational distance, linkage disequilibrium, association to inherited diseases, genetic alterations in cancer, selective sweeps during human history and three-dimensional organization in the nucleus. By providing a framework to cross-reference information across species, it has connected the biology of model systems to the physiology of the human. Furthermore, by providing comprehensive catalogues of genomic information, it has enabled genes and proteins to be recognized based on unique ‘tags’—allowing, for example, RNA transcripts to be assayed with arrays of oligonucleotide probes and proteins by detection of short peptide fragments in a mass spectrometer. In turn, these measurements have been used to construct ‘cellular signatures’ characteristic of specific cell types, states and responses, and catalogues of the contents of organelles such as the mitochondria.

The intensity of interest can be seen in the 2.5 million queries per week on the major genome data servers and in the flowering of a rich field of computational biology. The greatest impact of genomics has been the ability to investigate biological phenomena in a comprehensive, unbiased, hypothesis-free manner. In basic biology, it has reshaped our view of genome physiology, including the roles of protein-coding genes, non-coding RNAs and regulatory sequences.

In medicine, genomics has provided the first systematic approaches to discover the genes and cellular pathways underlying disease. Whereas candidate gene studies yielded slow progress, comprehensive approaches have resulted in the identification of ,2,850 genes underlying rare Mendelian diseases, ,1,100 loci affecting common polygenic disorders and ,150 new recurrent targets of somatic mutation in cancer. These
discoveries are propelling research throughout academia and industry.

The following sections contain only a small number of citations due to space limitations; a more extensive bibliography tied to each section can be found as Supplementary Information.

Kernel methods and Support Vector Machines

The tutorial will introduce the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the v-trick, various kernels and an overview over applications of kernel methods.

Kernel methods and Support Vector Machines

Alexander J. Smola

6 videos

Many problems in computational biology and chemistry can be formalized as classical statistical problems, e.g., pattern recognition, regression or dimension reduction, with the caveat that the data are often not vectors. Indeed objects such as gene sequences, small molecules, protein 3D structures or phylogenetic trees, to name just a few, have particular structures which contain relevant information for the statistical problem but can hardly be encoded into finite-dimensional vector representations. Kernel methods are a class of algorithms well suited for such problems. Indeed they extend the applicability of many statistical methods initially designed for vectors to virtually any type of data, without the need for explicit vectorization of the data. The price to pay for this extension to non-vectors is the need to define a positive definite kernel between the objects, formally equivalent to an implicit vectorization of the data.

#!html


Kernel Methods in Computational Biology

Jean-Philippe Vert

3 videos

Optimization Algorithms in Machine Learning

Stephen J. Wright

Computer Sciences Department, University of Wisconsin – Madison

NIPS 2010 – Optimization provides a valuable framework for thinking about, formulating, and solving many problems in machine learning. Since specialized techniques for the quadratic programming problem arising in support vector classification were developed in the 1990s, there has been more and more cross-fertilization between optimization and machine learning, with the large size and computational demands of machine learning applications driving much recent algorithmic research in optimization. This tutorial reviews the major computational paradigms in machine learning that are amenable to optimization algorithms, then discusses the algorithmic tools that are being brought to bear on such applications. We focus particularly on such algorithmic tools of recent interest as stochastic and incremental gradient methods, online optimization, augmented Lagrangian methods, and the various tools that have been applied recently in sparse and regularized optimization (excerpt taken from the course description).

lecture 1

lecture 2

lecture 3

4

5