01028nas a2200313   4500008004100000022001400041245008100055210006900136260001200205300001400217490000700231653001500238653002600253653002400279653001100303653002300314653002000337653003200357100001500389700002000404700002600424700001700450700002400467700002100491700002600512700002500538710004000563856011100603       2021                        eng d  a1548-710500aDOME: recommendations for supervised machine learning validation in biology.0 aDOME recommendations for supervised machine learning validation   c2021 10  a1122-11270 v1810aAlgorithms10aComputational Biology10aGuidelines as Topic10aHumans10aModels, Biological10aResearch Design10aSupervised Machine Learning1 aWalsh, Ian1 aFishman, Dmytro1 aGarcia-Gasulla, Dario1 aTitma, Tiina1 aPollastri, Gianluca1 aHarrow, Jennifer1 aPsomopoulos, Fotis, E1 aTosatto, Silvio, C E1 aELIXIR Machine Learning Focus Group  uhttps://www.clinbioinfosspa.es/content/dome-recommendations-supervised-machine-learning-validation-biology05409nas a2201477   4500008004100000022001400041245007800055210006900133260001200202300001400214490000700228520122900235653002601464653001401490653001101504653001501515653003501530653002001565653003801585100001901623700001901642700001801661700002301679700002401702700002301726700002101749700002801770700001801798700002501816700002401841700002701865700001801892700002301910700002001933700001501953700002201968700002001990700002702010700002102037700002202058700001802080700001902098700002702117700002002144700001902164700002002183700002102203700001702224700002102241700002302262700002002285700002202305700002002327700001802347700002302365700001802388700002102406700002602427700001902453700001702472700001802489700002402507700001902531700001602550700001702566700001602583700002102599700002102620700002102641700002102662700002202683700002202705700002102727700001602748700001702764700001502781700002102796700001702817700002402834700002202858700002002880700002402900700001802924700001602942700002302958700002102981700002403002700001703026700002603043700002403069700002503093700002403118700002203142700002103164700001703185700001903202700001803221700001803239700001303257700001703270700001603287700001903303700002703322700002003349700001703369700002203386700001903408700001903427700002603446700001903472700002903491700002103520700001603541700002203557700001603579700002003595700002403615700001903639700002403658700002203682700002003704700001803724710003303742710004903775856010703824       2021                        eng d  a1546-170X00aReporting guidelines for human microbiome research: the STORMS checklist.0 aReporting guidelines for human microbiome research the STORMS ch  c2021 11  a1885-18920 v273 a<p>The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.</p>10aComputational Biology10aDysbiosis10aHumans10aMicrobiota10aObservational Studies as Topic10aResearch Design10aTranslational Science, Biomedical1 aMirzayi, Chloe1 aRenson, Audrey1 aZohra, Fatima1 aElsafoury, Shaimaa1 aGeistlinger, Ludwig1 aKasselman, Lora, J1 aEckenrode, Kelly1 avan de Wijgert, Janneke1 aLoughman, Amy1 aMarques, Francine, Z1 aMacIntyre, David, A1 aArumugam, Manimozhiyan1 aAzhar, Rimsha1 aBeghini, Francesco1 aBergstrom, Kirk1 aBhatt, Ami1 aBisanz, Jordan, E1 aBraun, Jonathan1 aBravo, Hector, Corrada1 aBuck, Gregory, A1 aBushman, Frederic1 aCasero, David1 aClarke, Gerard1 aCollado, Maria, Carmen1 aCotter, Paul, D1 aCryan, John, F1 aDemmer, Ryan, T1 aDevkota, Suzanne1 aElinav, Eran1 aEscobar, Juan, S1 aFettweis, Jennifer1 aFinn, Robert, D1 aFodor, Anthony, A1 aForslund, Sofia1 aFranke, Andre1 aFurlanello, Cesare1 aGilbert, Jack1 aGrice, Elizabeth1 aHaibe-Kains, Benjamin1 aHandley, Scott1 aHerd, Pamela1 aHolmes, Susan1 aJacobs, Jonathan, P1 aKarstens, Lisa1 aKnight, Rob1 aKnights, Dan1 aKoren, Omry1 aKwon, Douglas, S1 aLangille, Morgan1 aLindsay, Brianna1 aMcGovern, Dermot1 aMcHardy, Alice, C1 aMcWeeney, Shannon1 aMueller, Noel, T1 aNezi, Luigi1 aOlm, Matthew1 aPalm, Noah1 aPasolli, Edoardo1 aRaes, Jeroen1 aRedinbo, Matthew, R1 aRühlemann, Malte1 aSartor, Balfour1 aSchloss, Patrick, D1 aSchriml, Lynn1 aSegal, Eran1 aShardell, Michelle1 aSharpton, Thomas1 aSmirnova, Ekaterina1 aSokol, Harry1 aSonnenburg, Justin, L1 aSrinivasan, Sujatha1 aThingholm, Louise, B1 aTurnbaugh, Peter, J1 aUpadhyay, Vaibhav1 aWalls, Ramona, L1 aWilmes, Paul1 aYamada, Takuji1 aZeller, Georg1 aZhang, Mingyu1 aZhao, Ni1 aZhao, Liping1 aBao, Wenjun1 aCulhane, Aedin1 aDevanarayan, Viswanath1 aDopazo, Joaquin1 aFan, Xiaohui1 aFischer, Matthias1 aJones, Wendell1 aKusko, Rebecca1 aMason, Christopher, E1 aMercer, Tim, R1 aSansone, Susanna-Assunta1 aScherer, Andreas1 aShi, Leming1 aThakkar, Shraddha1 aTong, Weida1 aWolfinger, Russ1 aHunter, Christopher1 aSegata, Nicola1 aHuttenhower, Curtis1 aDowd, Jennifer, B1 aJones, Heidi, E1 aWaldron, Levi1 aGenomic Standards Consortium1 aMassive Analysis and Quality Control Society  uhttps://www.clinbioinfosspa.es/content/reporting-guidelines-human-microbiome-research-storms-checklist01723nas a2200277   4500008004100000022001400041245009000055210006900145260001600214300001100230490000700241520077800248653002801026653002301054653003001077653003801107653003201145653001301177653002001190653002401210100001901234700002501253700002001278700002001298856012701318       2007                        eng d  a1367-481100aFunctional profiling of microarray experiments using text-mining derived bioentities.0 aFunctional profiling of microarray experiments using textmining   c2007 Nov 15  a3098-90 v233 a<p><b>MOTIVATION: </b>The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented.</p>10aArtificial Intelligence10aDatabases, Protein10aGene Expression Profiling10aInformation Storage and Retrieval10aNatural Language Processing10aProteins10aResearch Design10aSystems Integration1 aMinguez, Pablo1 aAl-Shahrour, Fátima1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/functional-profiling-microarray-experiments-using-text-mining-derived-bioentities-0