07193nas a2202077 4500008004100000022001400041245010000055210006900155260001200224300001100236490000700247520130900254653002101563653002601584653002201610653001301632653001401645653001601659653002301675653003101698653003201729653001101761653002301772653002201795653002101817653001601838653003601854653001801890653003201908653001501940653002401955653001301979653002601992653001902018100002302037700001902060700002202079700002102101700001802122700002702140700001902167700002602186700002602212700001802238700001902256700001702275700002202292700002102314700001902335700002902354700001802383700001602401700002902417700001802446700002302464700002202487700002002509700002002529700002702549700002102576700001702597700001802614700002402632700002102656700001602677700002102693700001902714700002302733700002302756700002002779700001702799700001902816700002402835700002102859700002402880700001702904700002302921700002402944700001902968700002002987700001903007700001903026700002903045700002303074700002003097700001703117700001803134700002403152700003303176700002003209700002003229700001603249700001503265700001803280700001903298700002603317700002703343700002003370700002403390700001803414700002403432700002203456700002203478700001903500700002003519700002103539700001803560700001503578700002203593700002303615700001703638700001903655700002403674700001703698700001803715700001703733700002303750700001803773700001803791700002303809700002103832700001703853700002203870700002003892700001803912700001803930700002303948700002103971700002503992700001804017700002004035700001704055700001904072700001704091700002104108700002504129700002704154700002404181700001604205700002104221700002504242700001704267700002004284700001804304700002504322700002404347700002304371700002104394700001904415700002204434700001804456700001604474700002204490700002004512700001804532700002504550700001904575700002204594700002404616700002304640700002004663700002304683700002204706700002304728700002304751700002604774700002004800700002204820700002004842700002304862700002104885700001804906700002404924710003504948856013204983 2021 eng d a1744-429200aCOVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.0 aCOVID19 Disease Map a computational knowledge repository of viru c2021 10 ae103870 v173 a

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

10aAntiviral Agents10aComputational Biology10aComputer Graphics10aCOVID-1910aCytokines10aData Mining10aDatabases, Factual10aGene Expression Regulation10aHost Microbial Interactions10aHumans10aImmunity, Cellular10aImmunity, Humoral10aImmunity, Innate10aLymphocytes10aMetabolic Networks and Pathways10aMyeloid Cells10aProtein Interaction Mapping10aSARS-CoV-210aSignal Transduction10aSoftware10aTranscription Factors10aViral Proteins1 aOstaszewski, Marek1 aNiarakis, Anna1 aMazein, Alexander1 aKuperstein, Inna1 aPhair, Robert1 aOrta-Resendiz, Aurelio1 aSingh, Vidisha1 aAghamiri, Sara, Sadat1 aAcencio, Marcio, Luis1 aGlaab, Enrico1 aRuepp, Andreas1 aFobo, Gisela1 aMontrone, Corinna1 aBrauner, Barbara1 aFrishman, Goar1 aGómez, Luis, Cristóbal1 aSomers, Julia1 aHoch, Matti1 aGupta, Shailendra, Kumar1 aScheel, Julia1 aBorlinghaus, Hanna1 aCzauderna, Tobias1 aSchreiber, Falk1 aMontagud, Arnau1 ade Leon, Miguel, Ponce1 aFunahashi, Akira1 aHiki, Yusuke1 aHiroi, Noriko1 aYamada, Takahiro, G1 aDräger, Andreas1 aRenz, Alina1 aNaveez, Muhammad1 aBocskei, Zsolt1 aMessina, Francesco1 aBörnigen, Daniela1 aFergusson, Liam1 aConti, Marta1 aRameil, Marius1 aNakonecnij, Vanessa1 aVanhoefer, Jakob1 aSchmiester, Leonard1 aWang, Muying1 aAckerman, Emily, E1 aShoemaker, Jason, E1 aZucker, Jeremy1 aOxford, Kristie1 aTeuton, Jeremy1 aKocakaya, Ebru1 aSummak, Gökçe, Yağmur1 aHanspers, Kristina1 aKutmon, Martina1 aCoort, Susan1 aEijssen, Lars1 aEhrhart, Friederike1 aRex, Devasahayam, Arokia Bal1 aSlenter, Denise1 aMartens, Marvin1 aPham, Nhung1 aHaw, Robin1 aJassal, Bijay1 aMatthews, Lisa1 aOrlic-Milacic, Marija1 aRibeiro, Andrea, Senff1 aRothfels, Karen1 aShamovsky, Veronica1 aStephan, Ralf1 aSevilla, Cristoffer1 aVarusai, Thawfeek1 aRavel, Jean-Marie1 aFraser, Rupsha1 aOrtseifen, Vera1 aMarchesi, Silvia1 aGawron, Piotr1 aSmula, Ewa1 aHeirendt, Laurent1 aSatagopam, Venkata1 aWu, Guanming1 aRiutta, Anders1 aGolebiewski, Martin1 aOwen, Stuart1 aGoble, Carole1 aHu, Xiaoming1 aOverall, Rupert, W1 aMaier, Dieter1 aBauch, Angela1 aGyori, Benjamin, M1 aBachman, John, A1 aVega, Carlos1 aGrouès, Valentin1 aVazquez, Miguel1 aPorras, Pablo1 aLicata, Luana1 aIannuccelli, Marta1 aSacco, Francesca1 aNesterova, Anastasia1 aYuryev, Anton1 ade Waard, Anita1 aTurei, Denes1 aLuna, Augustin1 aBabur, Ozgun1 aSoliman, Sylvain1 aValdeolivas, Alberto1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aRian, Kinza1 aHelikar, Tomáš1 aPuniya, Bhanwar, Lal1 aModos, Dezso1 aTreveil, Agatha1 aOlbei, Marton1 aDe Meulder, Bertrand1 aBallereau, Stephane1 aDugourd, Aurélien1 aNaldi, Aurélien1 aNoël, Vincent1 aCalzone, Laurence1 aSander, Chris1 aDemir, Emek1 aKorcsmaros, Tamas1 aFreeman, Tom, C1 aAugé, Franck1 aBeckmann, Jacques, S1 aHasenauer, Jan1 aWolkenhauer, Olaf1 aWilighagen, Egon, L1 aPico, Alexander, R1 aEvelo, Chris, T1 aGillespie, Marc, E1 aStein, Lincoln, D1 aHermjakob, Henning1 aD'Eustachio, Peter1 aSaez-Rodriguez, Julio1 aDopazo, Joaquin1 aValencia, Alfonso1 aKitano, Hiroaki1 aBarillot, Emmanuel1 aAuffray, Charles1 aBalling, Rudi1 aSchneider, Reinhard1 aCOVID-19 Disease Map Community uhttps://www.clinbioinfosspa.es/content/covid19-disease-map-computational-knowledge-repository-virus-host-interaction-mechanisms01962nas a2200277 4500008004100000022001400041245011500055210006900170260001600239300000800255490000700263520105900270653002301329653001901352653001301371653001101384653002101395653001401416653001301430653002401443100002701467700002401494700002001518700002001538856012601558 2019 eng d a1471-210500aExploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.0 aExploring the druggable space around the Fanconi anemia pathway  c2019 Jul 02 a3700 v203 a

BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.

RESULTS: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.

CONCLUSIONS: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

10aDatabases, Factual10aFanconi Anemia10aGenomics10aHumans10aMachine Learning10aPhenotype10aProteins10aSignal Transduction1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aLoucera, Carlos1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/exploring-druggable-space-around-fanconi-anemia-pathway-using-machine-learning-and