Some recent paper on how disease gene network works and the metastasis of cancer. Machine Learning is a good tool for study the relation between individual gene and disease. here are the papers:
Infectious Disease Modeling of Social Contagion in Networks
Alison L. Hill1,2*, David G. Rand1,3, Martin A. Nowak1,4,5,Nicholas A. Christakis6,7,8
Information, trends, behaviors and even health states may spread between contacts in a social network, similar to disease transmission. However, a major difference is that as well as being spread infectiously, it is possible to acquire this state spontaneously. For example, you can gain knowledge of a particular piece of information either by being told about it, or by discovering it yourself. In this paper we introduce a mathematical modeling framework that allows us to compare the dynamics of these social contagions to traditional infectious diseases. We can also extract and compare the rates of spontaneous versus contagious acquisition of a behavior from longitudinal data and can use this to predict the implications for future prevalence and control strategies. As an example, we study the spread of obesity, and find that the current rate of becoming obese is about 2 per year and increases by 0.5 percentage points for each obese social contact, while the rate of recovering from obesity is 4per year. The rates of spontaneous infection and transmission have steadily increased over time since 1970, driving the increase in obesity prevalence. Our model thus provides a quantitative way to analyze the strength and implications of social contagions.
I just read a book on statistical learning, The Elements of Statistical Learning(2ed). The important of this this book do not need me buck. The authors are so kind, and server they e-print of this online freely and they set up an web for supplementary.
Here is their website: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ . Wish you can find the beauty of statistical learning.
Recently, I just read a online book on Human Genome. Its an well write book, thanks the author for share. Enjoy the Book:
Table of Contents
Two new paper report in nature and The New England Journal of Medicine. This would be a good new for cancer patients. see the paper:
Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma
Nature Volume:467 ,Pages:596–599
B-RAF is the most frequently mutated protein kinase in human cancers1. The finding that oncogenic mutations in BRAF are common in melanoma2, followed by the demonstration that these tumours are dependent on the RAF/MEK/ERK pathway3, offered hope that inhibition of B-RAF kinase activity could benefit melanoma patients. Herein, we describe the structure-guided discovery of PLX4032 (RG7204), a potent inhibitor of oncogenic B-RAF kinase activity. Preclinical experiments demonstrated that PLX4032 selectively blocked the RAF/MEK/ERK pathway in BRAF mutant cells and caused regression of BRAF mutant xenografts4. Toxicology studies confirmed a wide safety margin consistent with the high degree of selectivity, enabling Phase 1 clinical trials using a crystalline formulation of PLX4032 (ref. 5). In a subset of melanoma patients, pathway inhibition was monitored in paired biopsy specimens collected before treatment initiation and following two weeks of treatment. This analysis revealed substantial inhibition of ERK phosphorylation, yet clinical evaluation did not show tumour regressions. At higher drug exposures afforded by a new amorphous drug formulation4, 5, greater than 80% inhibition of ERK phosphorylation in the tumours of patients correlated with clinical response. Indeed, the Phase 1 clinical data revealed a remarkably high 81% response rate in metastatic melanoma patients treated at an oral dose of 960 mg twice daily5. These data demonstrate that BRAF-mutant melanomas are highly dependent on B-RAF kinase activity.
Support vector machines (SVMs) are a set of supervised learning methods, they could used in statistical patterns classification and recognize. As its easy extension and could be deal the high dimension data, its could be used widely in computational biology. Here are two paper published in Education issue of Plos Computational, which introduced Machine Learning and SVMs to Computational Biology:
1. Support Vector Machines and Kernels for Computational Biology
Asa Ben-Hur1#, Cheng Soon Ong2,3#¤, Sören Sonnenburg4, Bernhard Schölkopf3, Gunnar Rätsch2*
1 Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America, 2 Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany, 3 Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 4 Fraunhofer Institute FIRST, Berlin, Germany
The increasing wealth of biological data coming from a large variety of platforms and the continued development of new high-throughput methods for probing biological systems require increasingly more sophisticated computational approaches. Putting all these data in simple-to-use databases is a first step; but realizing the full potential of the data requires algorithms that automatically extract regularities from the data, which can then lead to biological insight.
A very good news that the US physicians are turning to genomic tools to diagnose puzzling conditions. This must be a big chance for post-genomics research.
here is the news:
US clinics quietly embrace whole-genome sequencing
—Physicians are turning to genomic tools to diagnose puzzling conditions.
Author: Brendan Borrell
Whole-genome sequencing of patients’ DNA is already helping physicians make treatment decisions.JAMES KING-HOLMES / SCIENCE PHOTO LIBRARY
Advances in genome science and technology offer a deeper understanding of biology while at the same time improving the practice of medicine. Genomics information could tell us more story on our health.In 2008, Nature offered a special focus on “Personal Genomics” to focus on the on how genomes information help us known our health. As the price of sequencing is deducing, someday can say that my gene, my health. Here is a snapshot from Nature.