Social network, machine learning and disease-genes

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.

Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations

Yuanfang Guan1,2, Cheryl L. Ackert-Bicknell3, Braden Kell3,Olga G. Troyanskaya2,4, Matthew A. Hibbs3*

Many recent efforts to understand the genetic origins of complex diseases utilize statistical approaches to analyze phenotypic traits measured in genetically well-characterized populations. While these quantitative genetics methods are powerful, their success is limited by sampling biases and other confounding factors, and the biological interpretation of results can be challenging since these methods are not based on any functional information for candidate loci. On the other hand, the functional genomics field has greatly expanded in past years, both in terms of experimental approaches and analytical algorithms. However, functional approaches have been applied to understanding phenotypes in only the most basic ways. In this study, we demonstrate that functional genomics can complement traditional quantitative genetics by analytically extracting protein function information from large collections of high throughput data, which can then be used to predict genotype-phenotype associations. We applied our prediction methodology to the laboratory mouse, and we experimentally confirmed a role in osteoporosis for two of our predictions that were not candidates from any previous quantitative genetics study. The ability of our approach to produce accurate and unique predictions implies that functional genomics can complement quantitative genetics and can help address previous limitations in identifying disease genes.

Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis

Chen Yao emailHongdong Li emailChenggui Zhou emailLin Zhang emailJinfeng Zou email and Zheng Guo email

BMC Systems Biology 2010, 4:151doi:10.1186/1752-0509-4-151

Published: 9 November 2010

Abstract (provisional)

Background

It has been suggested that, in the human protein-protein interaction network, changes of co-expression between highly connected proteins (“hub”) and their interaction neighbours might have important roles in cancer metastasis and be predictive disease signatures for patient outcome. However, for a cancer, such disease signatures identified from different studies have little overlap.

Results

Here, we propose a systemic approach to evaluate the reproducibility of disease signatures at multiple levels, on the basis of some statistically testable biological models. Using two datasets for breast cancer metastasis, we showed that different signature hubs identified from different studies were highly consistent in terms of significantly sharing interaction neighbours and displaying consistent co-expression changes with their overlapping neighbours, whereas the shared interaction neighbours were significantly over-represented with known cancer genes and enriched in pathways deregulated in breast cancer pathogenesis. Then, we showed that the signature hubs identified from the two datasets were highly reproducible at the protein interaction and pathway levels in three other independent datasets.

Conclusions

Our results provide a possible biological model that different signature hubs altered in different patient cohorts could disturb the same pathways associated with cancer metastasis through their interaction neighbours.

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