Additional file 1: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

A step by step example of Rank Fusion process. This file provides an example of how to get the final gene rank. (DOCX 275 kb

Additional file 2: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

GSEA gene module. This file is all the gene modules downloaded from GSEA website. (TXT 2837 kb

Additional file 3: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Breast-Cancer-Gene. This is the known breast cancer-related genes downloaded from SNP4Disease. (TXT 2 kb

Additional file 4: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Final module list. This is the refined module list after removing irrelevant genes. (TXT 2736 kb

Additional file 7: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Sourcecode. Some core code of our method. (TXT 5 kb

Additional file 5: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Parameters discussion. This file discusses the performance of MGOGP under different parameter settings. (DOCX 65 kb

Research on Single Nucleotide Polymorphisms Interaction Detection from Network Perspective

<div><p>Single Nucleotide Polymorphisms (SNPs) found in Genome-Wide Association Study (GWAS) mainly influence the susceptibility of complex diseases, but they still could not comprehensively explain the relationships between mutations and diseases. Interactions between SNPs are considered so important for deeply understanding of those relationships that several strategies have been proposed to explore such interactions. However, part of those methods perform poorly when marginal effects of disease loci are weak or absent, others may lack of considering high-order SNPs interactions, few methods have achieved the requirements in both performance and accuracy. Considering the above reasons, not only low-order, but also high-order SNP interactions as well as main-effect SNPs, should be taken into account in detection methods under an acceptable computational complexity. In this paper, a new pairwise (or low-order) interaction detection method IG (Interaction Gain) is introduced, in which disease models are not required and parallel computing is utilized. Furthermore, high-order SNP interactions were proposed to be detected by finding closely connected function modules of the network constructed from IG detection results. Tested by a wide range of simulated datasets and four WTCCC real datasets, the proposed methods accurately detected both low-order and high-order SNP interactions as well as disease-associated main-effect SNPS and it surpasses all competitors in performances. The research will advance complex diseases research by providing more reliable SNP interactions.</p></div

Four models used for simulated data generation.

<p><i>δ</i> represents the impact value of the genotype at SNP location when there is no epistasis between SNPs; <i>t</i> represents the change of impact value when there are interactions between SNPs.</p><p>Four models used for simulated data generation.</p

Mapping results of all the SNPs researched.

<p>All: the number of SNPs considered; On Gene: the number of SNPs mapped on genes; Between Gene: the number of SNPs mapped between genes; Gene: the number of genes mapped by SNPs</p><p>Mapping results of all the SNPs researched.</p

Relationship between <i>α</i> and node and edge number of the SNP interaction network constructed.

<p><i>α</i> is the threshold value.</p