Inferring the conservative causal core of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.</p> <p>Results</p> <p>In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from <it>E. coli </it>that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.</p> <p>Conclusions</p> <p>For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.</p

Inferring the conservative causal core of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.</p> <p>Results</p> <p>In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from <it>E. coli </it>that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.</p> <p>Conclusions</p> <p>For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.</p

Genome-wide association analyses identify new susceptibility loci for oral cavity and pharyngeal cancer

We conducted a genome-wide association study of oral cavity and pharyngeal cancer in 6,034 cases and 6,585 controls from Europe, North America and South America. We detected eight significantly associated loci (P < 5 x 10(-8)), seven of which are new for these cancer sites. Oral and pharyngeal cancers combined were associated with loci at 6p21.32 (rs3828805, HLA-DQB1), 10q26.13 (rs201982221, LHPP) and 11p15.4 (rs1453414, OR52N2-TRIM5). Oral cancer was associated with two new regions, 2p23.3 (rs6547741, GPN1) and 9q34.12 (rs928674, LAMC3), and with known cancer-related loci-9p21.3 (rs8181047, CDKN2B-AS1) and 5p15.33 (rs10462706, CLPTM1L). Oropharyngeal cancer associations were limited to the human leukocyte antigen (HLA) region, and classical HLA allele imputation showed a protective association with the class II haplotype HLA-DRB1*1301-HLA-DQA1*0103-HLA-DQB1*0603 (odds ratio (OR) = 0.59, P = 2.7 x 10(-9)). Stratified analyses on a subgroup of oropharyngeal cases with information available on human papillomavirus (HPV) status indicated that this association was considerably stronger in HPV-positive (OR = 0.23, P = 1.6 x 10(-6)) than in HPV-negative (OR = 0.75, P = 0.16) cancers