One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.

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We identified 266 individuals with intragenic NSD1 mutations or 5q35 microdeletions encompassing NSD1 (referred to as "NSD1-positive individuals"), through analyses of 530 subjects with diverse phenotypes. Truncating NSD1 mutations occurred throughout the gene, but pathogenic missense mutations occurred only in functional domains (P < 2 x 10(-16)). Sotos syndrome was clinically diagnosed in 99% of NSD1-positive individuals, independent of the molecular analyses, indicating that NSD1 aberrations are essentially specific to this condition. Furthermore, our data suggest that 93% of patients who have been clinically diagnosed with Sotos syndrome have identifiable NSD1 abnormalities, of which 83% are intragenic mutations and 10% are 5q35 microdeletions. We reviewed the clinical phenotypes of 239 NSD1-positive individuals. Facial dysmorphism, learning disability, and childhood overgrowth were present in 90% of the individuals. However, both the height and head circumference of 10% of the individuals were within the normal range, indicating that overgrowth is not obligatory for the diagnosis of Sotos syndrome. A broad spectrum of associated clinical features was also present, the occurrence of which was largely independent of genotype, since individuals with identical mutations had different phenotypes. We compared the phenotypes of patients with intragenic NSD1 mutations with those of patients with 5q35 microdeletions. Patients with microdeletions had less-prominent overgrowth (P = .0003) and more-severe learning disability (P = 3 x 10(-9)) than patients with mutations. However, all features present in patients with microdeletions were also observed in patients with mutations, and there was no correlation between deletion size and the clinical phenotype, suggesting that the deletion of additional genes in patients with 5q35 microdeletions has little specific effect on phenotype. We identified only 13 familial cases. The reasons for the low vertical transmission rate are unclear, although familial cases were more likely than nonfamilial cases (P = .005) to carry missense mutations, suggesting that the underlying NSD1 mutational mechanism in Sotos syndrome may influence reproductive fitness.

body: Functional imaging can be defined as technique that gives information about the vascularity, metabolism or cellular activity of a lesion or organ. Functional Magnetic Resonance Imaging (MRI) techniques include Dynamic Contrast Enhanced (DCE) MR, Diffusion Weighted Imaging (DWI), Blood Oxygen Level Dependent (BOLD) MR and these can give more information about pathology compared to more anatomical techniques such as non contrast MRI or CT. Nuclear medicine has traditionally given functional information such as metabolism and proliferation and this has been exploited in PET by using different radiotracers. Breast cancer has a marked degree of tumoural heterogeneity reflecting clonal diversity which exhibit various metabolic and functional phenotypic characteristics. This variation can result in differential response to treatment creating a challenge in the identification of the best therapeutic regimen for each patient.

Hereditary lymphedema is a developmental disorder characterized by chronic swelling of the extremities due to dysfunction of the lymphatic vessels. Two responsible genes have been identified: the vascular endothelial growth factor receptor 3 (VEGFR3) gene, implicated in congenital lymphedema, or Milroy disease, and the forkhead-related transcription factor gene FOXC2, causing lymphedema-distichiasis. We describe three families with an unusual association of hypotrichosis, lymphedema, and telangiectasia. Using microsatellite analysis, we first excluded both VEGFR3 and FOXC2 as causative genes; we then considered the murine ragged phenotype, caused by mutations in the Sox18 transcription factor, as a likely counterpart to the human disease, because it presents a combination of hair and cardiovascular anomalies, including symptoms of lymphatic dysfunction. Two of the families were consanguineous; in affected members of these families, we identified homozygous missense mutations in the SOX18 gene, located in 20q13. The two amino acid substitutions, W95R and A104P, affect conserved residues in the first alpha helix of the DNA-binding domain of the transcription factor. In the third family, the parents were nonconsanguineous, and both the affected child and his brother, who died in utero with hydrops fetalis, showed a heterozygous nonsense mutation that truncates the SOX18 protein in its transactivation domain; this substitution was not found in genomic DNA from either parent and hence constitutes a de novo germline mutation. Thus, we show that SOX18 mutations in humans cause both recessive and dominant hypotrichosis-lymphedema-telangiectasia, suggesting that, in addition to its established role in hair and blood vessel development, the SOX18 transcription factor plays a role in the development and/or maintenance of lymphatic vessels.

Hereditary lymphedema is a chronic swelling of limbs due to dysfunction of lymphatic vessels. An autosomal dominant, congenital form of the disease, also known as "Milroy disease," has been mapped to the telomeric part of chromosome 5q, in the region 5q34-q35. This region contains a good candidate gene for the disease, VEGFR3 (FLT4), that encodes a receptor tyrosine kinase specific for lymphatic vessels. To clarify the role of VEGFR3 in the etiology of the disease, we have analyzed a family with hereditary lymphedema. We show linkage of the disease with markers in 5q34-q35, including a VEGFR3 intragenic polymorphism, and we describe an A-->G transition that cosegregates with the disease, corresponding to a histidine-to-arginine substitution in the catalytic loop of the protein. In addition, we show, by in vitro expression, that this mutation inhibits the autophosphorylation of the receptor. Thus, defective VEGFR3 signaling seems to be the cause of congenital hereditary lymphedema linked to 5q34-q35.

At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

Venous malformations (VMs) are localized defects of vascular morphogenesis. They can occur in every organ system, most commonly in skin and muscle. They can cause pain and bleeding, and in some critical locations they can be life threatening. Usually venous anomalies occur sporadically, but families with dominant inheritance have been identified. Using linkage analysis, we have established in earlier reports that some families with inherited VMs show linkage to chromosome 9p21; the mutation causes ligand-independent activation of an endothelial cell-specific receptor tyrosine kinase, TIE-2. Here we show that VMs with glomus cells (known as "glomangiomas"), inherited as an autosomal dominant trait in five families, are not linked to 9p21 but, instead, link to a new locus, on 1p21-p22, called "VMGLOM" (LOD score 12.70 at recombination fraction.00). We exclude three known positional candidate genes, DR1 (depressor of transcription 1), TGFBR3 (transforming growth factor-beta receptor, type 3), and TFA (tissue factor). We hypothesize that cutaneous venous anomalies (i.e., glomangiomas) are caused by mutations in a novel gene that may act to regulate angiogenesis, in concert with the TIE-2 signaling pathway.