01818nas a2200265   4500008004100000022001400041245007700055210006900132260001500201300001400216490000700230520098400237653001501221653001101236653002301247653002401270653002001294653001801314100001901332700002201351700001801373700003101391700002001422856011001442       2019                        eng d  a1477-405400aA comparison of mechanistic signaling pathway activity analysis methods.0 acomparison of mechanistic signaling pathway activity analysis me  c2019 09 27  a1655-16680 v203 a<p>Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.</p>10aAlgorithms10aHumans10aPostmortem Changes10aSignal Transduction10aSystems biology10aTranscriptome1 aAmadoz, Alicia1 aHidalgo, Marta, R1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/comparison-mechanistic-signaling-pathway-activity-analysis-methods02824nas a2200397   4500008004100000022001400041245011600055210006900171260000900240300000600249490000600255520157600261653002601837653002401863653001901887653002901906653001101935653001301946653003601959653002301995653001402018653001402032653001302046653001802059100001802077700002202095700001902117700001602136700002402152700002202176700002102198700002002219700003102239700002002270856013602290       2019                        eng d  a2056-718900aDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.0 aDifferential metabolic activity and discovery of therapeutic tar  c2019  a70 v53 a<p>In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.</p>10aComputational Biology10aComputer Simulation10aDrug discovery10aGene Regulatory Networks10aHumans10aInternet10aMetabolic Networks and Pathways10aModels, Biological10aNeoplasms10aPhenotype10aSoftware10aTranscriptome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aRian, Kinza1 aSalavert, Francisco1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/differential-metabolic-activity-and-discovery-therapeutic-targets-using-summarized-metabolic02577nas a2200529   4500008004100000022001400041245008700055210006900142260001500211300000800226490000600234520103000240653001001270653001801280653001001298653001101308653002001319653001101339653002301350653002301373653001901396653002501415653001801440100002301458700002701481700002101508700002501529700001601554700001901570700001701589700002201606700002401628700003101652700002101683700002401704700001901728700002401747700001801771700001801789700002101807700002001828700002001848700002101868700002301889700002001912856011501932       2018                        eng d  a2041-172300aThe effects of death and post-mortem cold ischemia on human tissue transcriptomes.0 aeffects of death and postmortem cold ischemia on human tissue tr  c2018 02 13  a4900 v93 a<p>Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.</p>10aBlood10aCold Ischemia10aDeath10aFemale10agene expression10aHumans10aModels, Biological10aPostmortem Changes10aRNA, Messenger10aStochastic Processes10aTranscriptome1 aFerreira, Pedro, G1 aMuñoz-Aguirre, Manuel1 aReverter, Ferran1 aGodinho, Caio, P Sá1 aSousa, Abel1 aAmadoz, Alicia1 aSodaei, Reza1 aHidalgo, Marta, R1 aPervouchine, Dmitri1 aCarbonell-Caballero, José1 aNurtdinov, Ramil1 aBreschi, Alessandra1 aAmador, Raziel1 aOliveira, Patrícia1 aCubuk, Cankut1 aCurado, João1 aAguet, François1 aOliveira, Carla1 aDopazo, Joaquin1 aSammeth, Michael1 aArdlie, Kristin, G1 aGuigó, Roderic  uhttps://www.clinbioinfosspa.es/content/effects-death-and-post-mortem-cold-ischemia-human-tissue-transcriptomes02950nas a2200445   4500008004100000022001400041245009500055210006900150260001500219300001400234490000700248520156700255653002101822653002101843653002401864653003001888653004301918653002901961653001101990653002602001653001502027653001302042653001402055653001402069653001402083653001402097653002702111653002702138653001802165653002202183100001802205700002202223700001902245700002202264700002102286700002002307700003102327700002002358856012602378       2018                        eng d  a1538-744500aGene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.0 aGene Expression Integration into Pathway Modules Reveals a PanCa  c2018 11 01  a6059-60720 v783 a<p>Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .</p>10aCell Line, Tumor10aCluster Analysis10aDisease Progression10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aGene Regulatory Networks10aHumans10aKaplan-Meier Estimate10aMetabolome10amutation10aNeoplasms10aOncogenes10aPhenotype10aPrognosis10aRNA, Small Interfering10aSequence Analysis, RNA10aTranscriptome10aTreatment Outcome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/gene-expression-integration-pathway-modules-reveals-pan-cancer-metabolic-landscape02239nas a2200277   4500008004100000022001400041245011400055210006900169260001500238300000700253490000700260520126600267653002601533653004301559653001101602653004201613653002401655653001801679653002401697100002201721700001901743700001801762700003101780700002001811856013001831       2018                        eng d  a1745-615000aModels of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.0 aModels of cell signaling uncover molecular mechanisms of highris  c2018 08 22  a160 v133 a<p><b>BACKGROUND: </b>Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.</p><p><b>RESULTS: </b>Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.</p><p><b>CONCLUSION: </b>We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.</p><p><b>REVIEWERS: </b>This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.</p>10aComputational Biology10aGene Expression Regulation, Neoplastic10aHumans10aJNK Mitogen-Activated Protein Kinases10aModels, Theoretical10aNeuroblastoma10aSignal Transduction1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/models-cell-signaling-uncover-molecular-mechanisms-high-risk-neuroblastoma-and-predict02048nas a2200313   4500008004100000022001400041245012900055210006900184260001600253300001400269490000600283520099600289653002601285653002001311653002901331653001101360653001301371653001401384653002301398653002701421653002401448100002201472700001801494700001901512700002401531700003101555700002001586856012801606       2017                        eng d  a1949-255300aHigh throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.0 aHigh throughput estimation of functional cell activities reveals  c2017 Jan 17  a5160-51780 v83 a<p>Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.</p>10aComputational Biology10agene expression10aGene Regulatory Networks10aHumans10amutation10aNeoplasms10aPrecision Medicine10aSequence Analysis, RNA10aSignal Transduction1 aHidalgo, Marta, R1 aCubuk, Cankut1 aAmadoz, Alicia1 aSalavert, Francisco1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/high-throughput-estimation-functional-cell-activities-reveals-disease-mechanisms-and02685nas a2200349   4500008004100000022001400041245011500055210006900170260001600239300001400255490000700269520162000276653001201896653002201908653001101930653001301941653001301954653001101967653002401978653002002002653003102022653001302053100003102066700001902097700002002116700002202136700001802158700001802176700002802194700002002222856009302242       2017                        eng d  a1367-481100aReference genome assessment from a population scale perspective: an accurate profile of variability and noise.0 aReference genome assessment from a population scale perspective   c2017 Nov 15  a3511-35170 v333 a<p><b>Motivation: </b>Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.</p><p><b>Results: </b>The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.</p><p><b>Availability and implementation: </b>This tool is freely available at http://gitlab.com/carbonell/ces.</p><p><b>Contact: </b>jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.</p><p><b>Supplementary information: </b>Supplementary data are available at Bioinformatics online.</p>10aAnimals10aGenetic Variation10aGenome10aGenomics10aGenotype10aHumans10aModels, Statistical10aQuality Control10aReproducibility of Results10aSoftware1 aCarbonell-Caballero, José1 aAmadoz, Alicia1 aAlonso, Roberto1 aHidalgo, Marta, R1 aCubuk, Cankut1 aConesa, David1 aLópez-Quílez, Antonio1 aDopazo, Joaquin  uhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx48203274nas a2200469   4500008004100000022001400041245010000055210006900155260001600224520184400240653001202084653000802096653001802104653002402122653001902146653000802165100002102173700001902194700001702213700002402230700002302254700002902277700002302306700002302329700002102352700001902373700002202392700003302414700002302447700001602470700002602486700002802512700002002540700002002560700002702580700002002607700002702627700001902654700002702673700002502700856007902725       2016                        eng d  a1537-171900a267 Spanish exomes reveal population-specific differences in disease-related genetic variation.0 a267 Spanish exomes reveal populationspecific differences in dise  c2016 Jan 133 aRecent results from large-scale genomic projects suggest that allele frequencies, which are highly relevant for medical purposes, differ considerably across different populations. The need for a detailed catalogue of local variability motivated the whole exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population. Like in other studies, a considerable number of rare variants were found (almost one third of the described variants). There were also relevant differences in allelic frequencies in polymorphic variants, including about 10,000 polymorphisms private to the Spanish population. The allelic frequencies of variants conferring susceptibility to complex diseases (including cancer, schizophrenia, Alzheimer disease, type 2 diabetes and other pathologies) were overall similar to those of other populations. However, the trend is the opposite for variants linked to Mendelian and rare diseases (including several retinal degenerative dystrophies and cardiomyopathies) that show marked frequency differences between populations. Interestingly, a correspondence between differences in allelic frequencies and disease prevalence was found, highlighting the relevance of frequency differences in disease risk. These differences are also observed in variants that disrupt known drug binding sites, suggesting an important role for local variability in population-specific drug resistances or adverse effects. We have made the Spanish population variant server web page that contains population frequency information for the complete list of 170,888 variant positions we found publicly available (http://spv.babelomics.org/), We show that it if fundamental to determine population-specific variant frequencies in order to distinguish real disease associations from population-specific polymorphisms.10adisease10aNGS10apolymorphisms10aPopulation genomics10aprioritization10aSNP1 aDopazo, Joaquín1 aAmadoz, Alicia1 aBleda, Marta1 aGarcía-Alonso, Luz1 aAlemán, Alejandro1 aGarcia-Garcia, Francisco1 aRodriguez, Juan, A1 aDaub, Josephine, T1 aMuntané, Gerard1 aRueda, Antonio1 aVela-Boza, Alicia1 aLópez-Domingo, Francisco, J1 aFlorido, Javier, P1 aArce, Pablo1 aRuiz-Ferrer, Macarena1 aMéndez-Vidal, Cristina1 aArnold, Todd, E1 aSpleiss, Olivia1 aAlvarez-Tejado, Miguel1 aNavarro, Arcadi1 aBhattacharya, Shomi, S1 aBorrego, Salud1 aSantoyo-López, Javier1 aAntiňolo, Guillermo  uhttps://mbe.oxfordjournals.org/content/early/2016/02/17/molbev.msw005.full01971nas a2200301   4500008004100000022001400041245010200055210006900157260001500226520100600241653002101247653002201268653002601290653001901316653002101335653002601356653001501382653002401397100002401421700002101445700001901466700001801485700002001503700001901523700003101542700002101573856007501594       2016                        eng d  a1362-496200aActionable pathways: interactive discovery of therapeutic targets using signaling pathway models.0 aActionable pathways interactive discovery of therapeutic targets  c2016 May 23 aThe discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.10aactionable genes10aDisease mechanism10adrug action mechanism10aDrug discovery10apathway analysis10apersonalized medicine10asignalling10atherapeutic targets1 aSalavert, Francisco1 aHidago, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aMedina, Ignacio1 aCrespo, Daniel1 aCarbonell-Caballero, José1 aDopazo, Joaquín  uhttp://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full02445nas a2200469   4500008004100000022001400041245008300055210006900138260001600207300001400223490000700237520108700244653001501331653002101346653002201367653001601389653002101405653000801426653001201434653002001446653002001466100002001486700002401506700002901530700003101559700001701590700002401607700002301631700002201654700002401676700002101700700001801721700002201739700001901761700003601780700002301816700002301839700002001862700002001882700002001902856005301922       2015                        eng d  a1362-496200aBabelomics 5.0: functional interpretation for new generations of genomic data.0 aBabelomics 50 functional interpretation for new generations of g  c2015 Apr 20  aW117-W1210 v433 aBabelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.10ababelomics10adata integration10agene set analysis10ainteractome10anetwork analysis10aNGS10aRNA-seq10aSystems biology10atranscriptomics1 aAlonso, Roberto1 aSalavert, Francisco1 aGarcia-Garcia, Francisco1 aCarbonell-Caballero, José1 aBleda, Marta1 aGarcía-Alonso, Luz1 aSanchis-Juan, Alba1 aPerez-Gil, Daniel1 aMarin-Garcia, Pablo1 aSánchez, Rubén1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aHernansaiz-Ballesteros, Rosa, D1 aAlemán, Alejandro1 aTárraga, Joaquín1 aMontaner, David1 aMedina, Ignacio1 aDopazo, Joaquin  uhttp://nar.oxfordjournals.org/content/43/W1/W11701871nas a2200337   4500008004100000022001400041245010900055210006900164260001600233300001000249490000600259520081400265653001501079653002601094653001501120653002101135653001801156653002001174653001101194653001901205653001401224653002001238653001301258653002401271100001901295700003001314700001901344700002401363700002001387856012601407       2015                        eng d  a2045-232200aUsing activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.0 aUsing activation status of signaling pathways as mechanismbased   c2015 Dec 18  a184940 v53 a<p>Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA). </p>10aAlgorithms10aAntineoplastic Agents10abiomarkers10aCell Line, Tumor10aCell Survival10agene expression10aHumans10aLethal Dose 5010aNeoplasms10aPhosphorylation10aProteins10aSignal Transduction1 aAmadoz, Alicia1 aSebastián-Leon, Patricia1 aVidal, Enrique1 aSalavert, Francisco1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/using-activation-status-signaling-pathways-mechanism-based-biomarkers-predict-drug02249nas a2200313   4500008004100000022001400041245008200055210006900137260001600206300000800222490000600230520134100236653002201577653001201599653001501611653002001626100003001646700001901676700001901695700001601714700002001730700001901750700001901769700002401788700002401812700002001836700002101856856005801877       2014                        eng d  a1752-050900aUnderstanding disease mechanisms with models of signaling pathway activities.0 aUnderstanding disease mechanisms with models of signaling pathwa  c2014 Oct 25  a1210 v83 aBackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.10aDisease mechanism10apathway10asignalling10aSystems biology1 aSebastián-Leon, Patricia1 aVidal, Enrique1 aMinguez, Pablo1 aConesa, Ana1 aTarazona, Sonia1 aAmadoz, Alicia1 aArmero, Carmen1 aSalavert, Francisco1 aVidal-Puig, Antonio1 aMontaner, David1 aDopazo, Joaquín  uhttp://www.biomedcentral.com/1752-0509/8/121/abstract00806nas a2200241   4500008004100000245008100041210006900122260000700191300000800198490000600206100003000212700001900242700001900261700001600280700002000296700001900316700001900335700003200354700002400386700002000410700002000430856011400450       2014                        eng d00aUnderstanding disease mechanisms with models of signaling pathway activities0 aUnderstanding disease mechanisms with models of signaling pathwa  c10  a1210 v81 aSebastián-Leon, Patricia1 aVidal, Enrique1 aMinguez, Pablo1 aConesa, Ana1 aTarazona, Sonia1 aAmadoz, Alicia1 aArmero, Carmen1 aTorres, Francisco, Salavert1 aVidal-Puig, Antonio1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/understanding-disease-mechanisms-models-signaling-pathway-activities-102621nas a2200325   4500008004100000022001400041245012100055210006900176260001600245300000900261490000700270520154400277653002101821653001901842653002901861653002001890653003401910653001301944653001101957653003201968100002402000700002002024700001902044700001902063700002402082700001902106700002002125700002002145856013002165       2012                        eng d  a1362-496200aDiscovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.0 aDiscovering the hidden subnetwork component in a ranked list of   c2012 Nov 01  ae1580 v403 a<p>Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.</p>10aBipolar Disorder10aFanconi Anemia10aGene Regulatory Networks10aGenes, Neoplasm10aGenome-Wide Association Study10aGenomics10aHumans10aProtein Interaction Mapping1 aGarcía-Alonso, Luz1 aAlonso, Roberto1 aVidal, Enrique1 aAmadoz, Alicia1 aDe Maria, Alejandro1 aMinguez, Pablo1 aMedina, Ignacio1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/discovering-hidden-sub-network-component-ranked-list-genes-or-proteins-derived-genomic