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1,038
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
- Nucleic Acids Res
, 2013
"... Complete knowledge of all direct and indirect inter-actions between proteins in a given cell would represent an important milestone towards a com-prehensive description of cellular mechanisms and functions. Although this goal is still elusive, consid-erable progress has been made—particularly for ce ..."
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Cited by 183 (9 self)
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Complete knowledge of all direct and indirect inter-actions between proteins in a given cell would represent an important milestone towards a com-prehensive description of cellular mechanisms and functions. Although this goal is still elusive, consid-erable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available inter-action data is desirable, including lower-quality data and/or computational predictions. The STRING database
Smoothing Proximal Gradient Method for General Structured Sparse Learning
"... We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group ..."
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Cited by 55 (7 self)
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We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, based on the ℓ1/ℓ2 mixed-norm penalty, and 2) graph-guided fusion penalty. For both types of penalties, due to their non-separability, developing an efficient optimization method has remained a challenging problem. In this paper, we propose a general optimization approach, called smoothing proximal gradient method, which can solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured-sparsityinducing penalties. Our approach is based on a general smoothing technique of Nesterov [17]. It achieves a convergence rate faster than the standard first-order method, subgradient method, and is much more scalable than the most widely used interior-point method. Numerical results are reported to demonstrate the efficiency and scalability of the proposed method. 1
Distributing responsibilities
- The Journal of Political Philosophy
, 2001
"... helicase senataxin suppresses the antiviral transcriptional ..."
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Cited by 30 (1 self)
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helicase senataxin suppresses the antiviral transcriptional
Irizarry R: The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Research 2011, 39(suppl 1):D1011
"... data repositories to begin cataloging the human and murine transcriptomes ..."
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Cited by 23 (4 self)
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data repositories to begin cataloging the human and murine transcriptomes
DAVID-WS: a stateful web service to facilitate gene/protein list analysis
- Bioinformatics
, 2012
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Recapitulation of premature ageing with ipscs from hutchinsongilford progeria syndrome
- Nature
, 2011
"... Hutchinson-Gilford progeria syndrome (HGPS) is a rare and fatal human premature aging disease1–5, characterized by premature arteriosclerosis and degeneration of vascular smooth muscle cells (SMCs)6–8. HGPS is caused by a single-point mutation in the LMNA gene, resulting in the generation of progeri ..."
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Cited by 20 (0 self)
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Hutchinson-Gilford progeria syndrome (HGPS) is a rare and fatal human premature aging disease1–5, characterized by premature arteriosclerosis and degeneration of vascular smooth muscle cells (SMCs)6–8. HGPS is caused by a single-point mutation in the LMNA gene, resulting in the generation of progerin, a truncated splicing mutant of lamin A. Accumulation of progerin leads to various aging-associated nuclear defects including disorganization of nuclear lamina and loss of heterochromatin9–12. Here, we report the generation of induced pluripotent stem cells (iPSCs) from fibroblasts obtained from patients with HGPS. HGPS-iPSCs show absence of progerin, and more importantly, lack the nuclear envelope and epigenetic alterations normally associated with premature aging. Upon differentiation of HGPS-iPSCs, progerin and its aging-associated phenotypic consequences are restored. Specifically, directed differentiation of HGPS-iPSCs to SMCs leads to the appearance of premature senescence phenotypes associated with vascular aging. Additionally, our studies identify DNA-dependent protein kinase catalytic subunit (DNAPKcs) as a downstream target of progerin. The absence of nuclear DNAPK holoenzyme correlates with premature as well as physiological aging. Since progerin also
Inferring disease and gene set associations with rank coherence in networks
- Bioinformatics
, 2011
"... A computational challenge to validate the candidate disease genes identified in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease ..."
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Cited by 14 (2 self)
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A computational challenge to validate the candidate disease genes identified in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease causative genes are incomplete. We propose a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. Assuming coherent associations between the genes ranked by their relevance to the query gene set, and the disease phenotypes ranked by their relevance to the hidden target disease phenotypes of the query gene set, we formulate a learning framework maximizing the rank coherence with respect to the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation, and two variants are introduced to find the optimal solution of the framework. We evaluated the rcNet algorithms and existing baseline methods with both leave-one-out cross-validation and a task of predicting recently discovered diseasegene associations in OMIM. The experiments demonstrated that the rcNet algorithms achieved the best overall rankings compared to the baselines. To further validate the reproducibility of the performance, we
H (2011) COSINE: condition-specific subnetwork identification using a global optimization method
- Bioinformatics
"... Motivation: The identification of condition specific sub-networks from gene expression profiles has important biological applications, ranging from the selection of disease-related biomarkers to the discovery of pathway alterations across different phenotypes. Although many methods exist for extract ..."
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Cited by 14 (0 self)
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Motivation: The identification of condition specific sub-networks from gene expression profiles has important biological applications, ranging from the selection of disease-related biomarkers to the discovery of pathway alterations across different phenotypes. Although many methods exist for extracting these sub-networks, very few existing approaches simultaneously consider both the differential expression of individual genes and the differential correlation of gene pairs, losing potentially valuable information in the data. Results: In this article, we propose a new method, COSINE (COndition SpecIfic sub-NEtwork), which employs a scoring function that jointly measures the condition-specific changes of both ‘nodes’ (individual genes) and ‘edges ’ (gene–gene co-expression). It uses the genetic algorithm to search for the single optimal sub-network
IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks
- Nucleic Acids Res
, 2012
"... Integrative multi-species prediction (IMP) is an inter-active web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides a framework for biol ..."
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Cited by 12 (4 self)
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Integrative multi-species prediction (IMP) is an inter-active web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides a framework for biologists to analyze their candidate gene sets in the context of functional networks, as they expand or focus these sets by mining functional relationships pre-dicted from integrated high-throughput data. IMP integrates prior knowledge and data collections from multiple organisms in its analyses. Through flexible and interactive visualizations, researchers can compare functional contexts and interpret the behavior of their gene sets across organisms. Additionally, IMP identifies homologs with conserved functional roles for knowledge transfer, allowing for accurate function predictions even for biological processes that have very few experimental annota-tions in a given organism. IMP currently supports seven organisms (Homo sapiens, Mus musculus, Rattus novegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans and Saccharomyces cerevisiae), does not require any registration or installation and is freely available for use at
An Efficient Proximal Gradient Method for General Structured Structured Sparse Learning
"... We study the problem of learning regression models regularized by the structured sparsity-inducing penalty which encodes the prior structural information. We consider two most widely adopted structures as motivating examples: (1) group structure (might overlap) which is encoded via ℓ1/ℓ2 mixed norm ..."
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Cited by 12 (0 self)
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We study the problem of learning regression models regularized by the structured sparsity-inducing penalty which encodes the prior structural information. We consider two most widely adopted structures as motivating examples: (1) group structure (might overlap) which is encoded via ℓ1/ℓ2 mixed norm penalty; (2) graph structure which is encoded in graph-guided fusion penalty. For both structures, due to the non-separability of the penalties, developing an efficient optimization method has remained a challenge. In this paper, we propose a general proximalgradient method which can solve the structured sparse learning problems with a smooth convex loss and a class of structured penalties, including our motivating examples. It achieves a faster convergence rate than subgradient method; and is more efficient and scalable than interior point method as shown in simulation studies. 1
