Our new methods final launched on Nature Protocols, where we developed a series of methods and related C++/R combined software package, Sclust（around 1.5Gb,大文件谨慎载）. In Sclust, you can do copy number calling, cancer tissue purity estimating and clone and subclone structure inferring from normal-tumor paired whole genome/exon sequencing data.
1. 可以准确地做copy number calling， tumor purity estimating，subclonal inferring；
A new R package, netClass, has been release. netClass integrate network information, such as protein-protein interaction network or KEGG, to mRNA classification, but also incorporate miRNA to mRNA with mi-mRNA interaction network for biomarker discovery. This methods we called stSVM and already published in PloS ONE (Cun et al 2013). Apart from stSVM, we also implement the flowing methods in netClass:
AEP (average gene expression of pathway), Guo et al., BMC Bioinformatics 2005, 6:58.
PAC (pathway activitive classification), Lee E, et al., PLoS Comput Biol 4(11): e1000217.
hubc (Hub nodes classification), Taylor et al.(2009) Nat. Biotech.: doi: 10.1038/nbt.152
frSVM (filter via top ranked genes), Cun et al. arXiv:1212.3214 ; Winter etal., PLoS Comput Biol 8(5): e1002511.
stSVM (network smoothed t-statistic) , Cun et al., PloS One,.
‘Translational Bioinformatics’ is a collection of PLOS Computational Biology Education articles which reads as a “book” to be used as a reference or tutorial for a graduate level introductory course on the science of translational bioinformatics.
Translational bioinformatics is an emerging field that addresses the current challenges of integrating increasingly voluminous amounts of molecular and clinical data. Its aim is to provide a better understanding of the molecular basis of disease, which in turn will inform clinical practice and ultimately improve human health.
The concept of a translational bioinformatics introductory book was originally conceived in 2009 by Jake Chen and Maricel Kann. Each chapter was crafted by leading experts who provide a solid introduction to the topics covered, complete with training exercises and answers. The rapid evolution of this field is expected to lead to updates and new chapters that will be incorporated into this collection.
Collection editors: Maricel Kann, Guest Editor, and Fran Lewitter, PLOS Computational Biology Education Editor.
Download the full Translational Bioinformatics collection here: PDF
In our recent publication in BMC bioinformatics, we acompared a great deal of feature selection methods to finding prognostic biomakers in 6 breast cancer gene expresion data. No methods show significant performacne in prediction accuracy, feature selection stability and biogical interprety, which against previeous reseach results: current network-based appraoch did not show much benift in our analysis. Meanwhile, A group from NKI also show the simliar results in PloS One. The R codes for these algorithms in our paper is availiable as request.
How to find the robust biomarkers in the genomics data are first step to personalized medicine. Here we take a short review on how machine leaning works in find biomarkers and current aproach in this area. for more interesting technology, please see the following papers.
Bonn-Aachen International Center for IT (B-IT), Dahlmannstr. 2, 53113 Bonn, Germany
Abstract: Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.
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.
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.