From syndrome families to functional genomics

Brunner, Han G.; Driel, M.A. Van

doi:10.1038/nrg1383

Cited by 163 publications

(143 citation statements)

References 53 publications

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“…We have limited the use of the term syndrome to patterns of anomalies that are causally related but which are not necessarily pathogenetically related. We acknowledge that identifying families or communities of overlapping but still distinct syndromes, that are caused by mutations in the same gene or by genes working in the same pathway or network, can be very helpful for patient care and research alike [Pinsky, 1974;Brunner and van Driel, 2004]. Examples are the ciliopathies [Davis and Katsanis, 2012], rasopathies [Tidyman and Rauen, 2009], and laminopathies [Worman, 2012].…”

Section: Syndromementioning

confidence: 99%

Elements of morphology: General terms for congenital anomalies

Hennekam

Biesecker

Allanson

et al. 2013

American J of Med Genetics Pt A

123

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An international group of clinicians working in the field of dysmorphology has established a process for the standardization of terms used to describe human morphology. The goals are to standardize these terms and develop consensus regarding their definitions. This project will increase the usefulness and precision of descriptions of the human phenotype and facilitate reliable comparisons of phenotypic findings among clinicians and researchers in medicine, developmental biology, and genetics. Here we define and illustrate the general terms that describe congenital anomalies as related to human conditions.

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Section: Syndromementioning

confidence: 99%

Elements of morphology: General terms for congenital anomalies

Hennekam

Biesecker

Allanson

et al. 2013

American J of Med Genetics Pt A

123

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“…proteins (Ideker and Sharan, 2008;Goh et al, 2007). They also showed that individual genes associated with particular or similar phenotypes are likely to reside in the same biological functional modules and protein complexes, as molecular machines that integrate and coordinate multiple gene products to perform biological functions (Goh et al, 2007;Brunner and Van, 2004;Yang et al, 2011). The observations of genetic modular organization of human diseases suggest that the common biological characteristics are associated with the diseasecausative genes of particular disease phenotypes.…”

Section: Introductionmentioning

confidence: 97%

Biological Feature Selection and Disease Gene Identification using New Stepwise Random Forests

Hwang¹

2017

Industrial Engineering and Management Systems

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Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

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“…Here, n is a sufficiently large number that is used to obtain a representative sampling {α (1) , α (2) , α (3) , ..., α (n) } of the population of association scores for seed sets that match the size and degree distribution of S (we use n = 1000 in our experiments). -We then estimate the mean of this distribution as μ S = 1≤i≤n α (i) /n and the standard deviation as…”

Section: Reference Models For Statistical Adjustmentmentioning

confidence: 99%

“…Identification of disease-associated genes is an important step toward enhancing our understanding of the cellular mechanisms that drive human diseases, with profound applications in modeling, diagnosis, prognosis, and therapeutic intervention [1]. Genomewide linkage and association studies in healthy and affected populations provide chromosomal regions containing up to 300 candidate genes possibly associated with genetic diseases [2].…”

Section: Introductionmentioning

confidence: 99%

Role of Centrality in Network-Based Prioritization of Disease Genes

Erten

Koyutürk

2010

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Abstract. High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the notion that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Random walk and network propagation based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths. However, as we demonstrate in this paper, such methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Here, we propose several statistical correction schemes that aim to account for the degree distribution of known disease and candidate genes. We show that, while the proposed schemes are very effective in detecting loosely connected disease genes that are missed by existing approaches, this improvement might come at the price of more false negatives for highly connected genes. Motivated by these results, we develop uniform prioritization methods that effectively integrate existing methods with the proposed statistical correction schemes. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that the resulting hybrid schemes outperform existing methods in prioritizing candidate disease genes.

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From syndrome families to functional genomics

Cited by 163 publications

References 53 publications

Elements of morphology: General terms for congenital anomalies

Elements of morphology: General terms for congenital anomalies

Biological Feature Selection and Disease Gene Identification using New Stepwise Random Forests

Role of Centrality in Network-Based Prioritization of Disease Genes

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