<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Velasco, Sheyla</style></author><author><style face="normal" font="default" size="100%">Olivares-González, Lorena</style></author><author><style face="normal" font="default" size="100%">Rodrigo, Regina</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.</style></title><secondary-title><style face="normal" font="default" size="100%">J Transl Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Transl Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinitis pigmentosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Feb 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">139</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Puerto-Camacho, Pilar</style></author><author><style face="normal" font="default" size="100%">Diaz-Martin, Juan</style></author><author><style face="normal" font="default" size="100%">Olmedo-Pelayo, Joaquín</style></author><author><style face="normal" font="default" size="100%">Bolado-Carrancio, Alfonso</style></author><author><style face="normal" font="default" size="100%">Salguero-Aranda, Carmen</style></author><author><style face="normal" font="default" size="100%">Jordán-Pérez, Carmen</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Alamo-Alvarez, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Delgado-Bellido, Daniel</style></author><author><style face="normal" font="default" size="100%">Lobo-Selma, Laura</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Sastre, Ana</style></author><author><style face="normal" font="default" size="100%">Alonso, Javier</style></author><author><style face="normal" font="default" size="100%">Grünewald, Thomas G P</style></author><author><style face="normal" font="default" size="100%">Bernabeu, Carmelo</style></author><author><style face="normal" font="default" size="100%">Byron, Adam</style></author><author><style face="normal" font="default" size="100%">Brunton, Valerie G</style></author><author><style face="normal" font="default" size="100%">Amaral, Ana Teresa</style></author><author><style face="normal" font="default" size="100%">de Alava, Enrique</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Endoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modulation of Cell-Matrix Interactions.</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Int J Mol Sci</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bone Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Endoglin</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Matrix Metalloproteinase 14</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptors, Growth Factor</style></keyword><keyword><style  face="normal" font="default" size="100%">Sarcoma, Ewing</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Aug 04</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Endoglin (ENG) is a mesenchymal stem cell (MSC) marker typically expressed by active endothelium. This transmembrane glycoprotein is shed by matrix metalloproteinase 14 (MMP14). Our previous work demonstrated potent preclinical activity of first-in-class anti-ENG antibody-drug conjugates as a nascent strategy to eradicate Ewing sarcoma (ES), a devastating rare bone/soft tissue cancer with a putative MSC origin. We also defined a correlation between ENG and MMP14 expression in ES. Herein, we show that ENG expression is significantly associated with a dismal prognosis in a large cohort of ES patients. Moreover, both ENG/MMP14 are frequently expressed in primary ES tumors and metastasis. To deepen in their functional relevance in ES, we conducted transcriptomic and proteomic profiling of in vitro ES models that unveiled a key role of ENG and MMP14 in cell mechano-transduction. Migration and adhesion assays confirmed that loss of ENG disrupts actin filament assembly and filopodia formation, with a concomitant effect on cell spreading. Furthermore, we observed that ENG regulates cell-matrix interaction through activation of focal adhesion signaling and protein kinase C expression. In turn, loss of MMP14 contributed to a more adhesive phenotype of ES cells by modulating the transcriptional extracellular matrix dynamics. Overall, these results suggest that ENG and MMP14 exert a significant role in mediating correct spreading machinery of ES cells, impacting the aggressiveness of the disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">15</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Niarakis, Anna</style></author><author><style face="normal" font="default" size="100%">Mazein, Alexander</style></author><author><style face="normal" font="default" size="100%">Kuperstein, Inna</style></author><author><style face="normal" font="default" size="100%">Phair, Robert</style></author><author><style face="normal" font="default" size="100%">Orta-Resendiz, Aurelio</style></author><author><style face="normal" font="default" size="100%">Singh, Vidisha</style></author><author><style face="normal" font="default" size="100%">Aghamiri, Sara Sadat</style></author><author><style face="normal" font="default" size="100%">Acencio, Marcio Luis</style></author><author><style face="normal" font="default" size="100%">Glaab, Enrico</style></author><author><style face="normal" font="default" size="100%">Ruepp, Andreas</style></author><author><style face="normal" font="default" size="100%">Fobo, Gisela</style></author><author><style face="normal" font="default" size="100%">Montrone, Corinna</style></author><author><style face="normal" font="default" size="100%">Brauner, Barbara</style></author><author><style face="normal" font="default" size="100%">Frishman, Goar</style></author><author><style face="normal" font="default" size="100%">Monraz Gómez, Luis Cristóbal</style></author><author><style face="normal" font="default" size="100%">Somers, Julia</style></author><author><style face="normal" font="default" size="100%">Hoch, Matti</style></author><author><style face="normal" font="default" size="100%">Kumar Gupta, Shailendra</style></author><author><style face="normal" font="default" size="100%">Scheel, Julia</style></author><author><style face="normal" font="default" size="100%">Borlinghaus, Hanna</style></author><author><style face="normal" font="default" size="100%">Czauderna, Tobias</style></author><author><style face="normal" font="default" size="100%">Schreiber, Falk</style></author><author><style face="normal" font="default" size="100%">Montagud, Arnau</style></author><author><style face="normal" font="default" size="100%">Ponce de Leon, Miguel</style></author><author><style face="normal" font="default" size="100%">Funahashi, Akira</style></author><author><style face="normal" font="default" size="100%">Hiki, Yusuke</style></author><author><style face="normal" font="default" size="100%">Hiroi, Noriko</style></author><author><style face="normal" font="default" size="100%">Yamada, Takahiro G</style></author><author><style face="normal" font="default" size="100%">Dräger, Andreas</style></author><author><style face="normal" font="default" size="100%">Renz, Alina</style></author><author><style face="normal" font="default" size="100%">Naveez, Muhammad</style></author><author><style face="normal" font="default" size="100%">Bocskei, Zsolt</style></author><author><style face="normal" font="default" size="100%">Messina, Francesco</style></author><author><style face="normal" font="default" size="100%">Börnigen, Daniela</style></author><author><style face="normal" font="default" size="100%">Fergusson, Liam</style></author><author><style face="normal" font="default" size="100%">Conti, Marta</style></author><author><style face="normal" font="default" size="100%">Rameil, Marius</style></author><author><style face="normal" font="default" size="100%">Nakonecnij, Vanessa</style></author><author><style face="normal" font="default" size="100%">Vanhoefer, Jakob</style></author><author><style face="normal" font="default" size="100%">Schmiester, Leonard</style></author><author><style face="normal" font="default" size="100%">Wang, Muying</style></author><author><style face="normal" font="default" size="100%">Ackerman, Emily E</style></author><author><style face="normal" font="default" size="100%">Shoemaker, Jason E</style></author><author><style face="normal" font="default" size="100%">Zucker, Jeremy</style></author><author><style face="normal" font="default" size="100%">Oxford, Kristie</style></author><author><style face="normal" font="default" size="100%">Teuton, Jeremy</style></author><author><style face="normal" font="default" size="100%">Kocakaya, Ebru</style></author><author><style face="normal" font="default" size="100%">Summak, Gökçe Yağmur</style></author><author><style face="normal" font="default" size="100%">Hanspers, Kristina</style></author><author><style face="normal" font="default" size="100%">Kutmon, Martina</style></author><author><style face="normal" font="default" size="100%">Coort, Susan</style></author><author><style face="normal" font="default" size="100%">Eijssen, Lars</style></author><author><style face="normal" font="default" size="100%">Ehrhart, Friederike</style></author><author><style face="normal" font="default" size="100%">Rex, Devasahayam Arokia Balaya</style></author><author><style face="normal" font="default" size="100%">Slenter, Denise</style></author><author><style face="normal" font="default" size="100%">Martens, Marvin</style></author><author><style face="normal" font="default" size="100%">Pham, Nhung</style></author><author><style face="normal" font="default" size="100%">Haw, Robin</style></author><author><style face="normal" font="default" size="100%">Jassal, Bijay</style></author><author><style face="normal" font="default" size="100%">Matthews, Lisa</style></author><author><style face="normal" font="default" size="100%">Orlic-Milacic, Marija</style></author><author><style face="normal" font="default" size="100%">Senff Ribeiro, Andrea</style></author><author><style face="normal" font="default" size="100%">Rothfels, Karen</style></author><author><style face="normal" font="default" size="100%">Shamovsky, Veronica</style></author><author><style face="normal" font="default" size="100%">Stephan, Ralf</style></author><author><style face="normal" font="default" size="100%">Sevilla, Cristoffer</style></author><author><style face="normal" font="default" size="100%">Varusai, Thawfeek</style></author><author><style face="normal" font="default" size="100%">Ravel, Jean-Marie</style></author><author><style face="normal" font="default" size="100%">Fraser, Rupsha</style></author><author><style face="normal" font="default" size="100%">Ortseifen, Vera</style></author><author><style face="normal" font="default" size="100%">Marchesi, Silvia</style></author><author><style face="normal" font="default" size="100%">Gawron, Piotr</style></author><author><style face="normal" font="default" size="100%">Smula, Ewa</style></author><author><style face="normal" font="default" size="100%">Heirendt, Laurent</style></author><author><style face="normal" font="default" size="100%">Satagopam, Venkata</style></author><author><style face="normal" font="default" size="100%">Wu, Guanming</style></author><author><style face="normal" font="default" size="100%">Riutta, Anders</style></author><author><style face="normal" font="default" size="100%">Golebiewski, Martin</style></author><author><style face="normal" font="default" size="100%">Owen, Stuart</style></author><author><style face="normal" font="default" size="100%">Goble, Carole</style></author><author><style face="normal" font="default" size="100%">Hu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Overall, Rupert W</style></author><author><style face="normal" font="default" size="100%">Maier, Dieter</style></author><author><style face="normal" font="default" size="100%">Bauch, Angela</style></author><author><style face="normal" font="default" size="100%">Gyori, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Bachman, John A</style></author><author><style face="normal" font="default" size="100%">Vega, Carlos</style></author><author><style face="normal" font="default" size="100%">Grouès, Valentin</style></author><author><style face="normal" font="default" size="100%">Vazquez, Miguel</style></author><author><style face="normal" font="default" size="100%">Porras, Pablo</style></author><author><style face="normal" font="default" size="100%">Licata, Luana</style></author><author><style face="normal" font="default" size="100%">Iannuccelli, Marta</style></author><author><style face="normal" font="default" size="100%">Sacco, Francesca</style></author><author><style face="normal" font="default" size="100%">Nesterova, Anastasia</style></author><author><style face="normal" font="default" size="100%">Yuryev, Anton</style></author><author><style face="normal" font="default" size="100%">de Waard, Anita</style></author><author><style face="normal" font="default" size="100%">Turei, Denes</style></author><author><style face="normal" font="default" size="100%">Luna, Augustin</style></author><author><style face="normal" font="default" size="100%">Babur, Ozgun</style></author><author><style face="normal" font="default" size="100%">Soliman, Sylvain</style></author><author><style face="normal" font="default" size="100%">Valdeolivas, Alberto</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Helikar, Tomáš</style></author><author><style face="normal" font="default" size="100%">Puniya, Bhanwar Lal</style></author><author><style face="normal" font="default" size="100%">Modos, Dezso</style></author><author><style face="normal" font="default" size="100%">Treveil, Agatha</style></author><author><style face="normal" font="default" size="100%">Olbei, Marton</style></author><author><style face="normal" font="default" size="100%">De Meulder, Bertrand</style></author><author><style face="normal" font="default" size="100%">Ballereau, Stephane</style></author><author><style face="normal" font="default" size="100%">Dugourd, Aurélien</style></author><author><style face="normal" font="default" size="100%">Naldi, Aurélien</style></author><author><style face="normal" font="default" size="100%">Noël, Vincent</style></author><author><style face="normal" font="default" size="100%">Calzone, Laurence</style></author><author><style face="normal" font="default" size="100%">Sander, Chris</style></author><author><style face="normal" font="default" size="100%">Demir, Emek</style></author><author><style face="normal" font="default" size="100%">Korcsmaros, Tamas</style></author><author><style face="normal" font="default" size="100%">Freeman, Tom C</style></author><author><style face="normal" font="default" size="100%">Augé, Franck</style></author><author><style face="normal" font="default" size="100%">Beckmann, Jacques S</style></author><author><style face="normal" font="default" size="100%">Hasenauer, Jan</style></author><author><style face="normal" font="default" size="100%">Wolkenhauer, Olaf</style></author><author><style face="normal" font="default" size="100%">Wilighagen, Egon L</style></author><author><style face="normal" font="default" size="100%">Pico, Alexander R</style></author><author><style face="normal" font="default" size="100%">Evelo, Chris T</style></author><author><style face="normal" font="default" size="100%">Gillespie, Marc E</style></author><author><style face="normal" font="default" size="100%">Stein, Lincoln D</style></author><author><style face="normal" font="default" size="100%">Hermjakob, Henning</style></author><author><style face="normal" font="default" size="100%">D'Eustachio, Peter</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Kitano, Hiroaki</style></author><author><style face="normal" font="default" size="100%">Barillot, Emmanuel</style></author><author><style face="normal" font="default" size="100%">Auffray, Charles</style></author><author><style face="normal" font="default" size="100%">Balling, Rudi</style></author><author><style face="normal" font="default" size="100%">Schneider, Reinhard</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">COVID-19 Disease Map Community</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antiviral Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytokines</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Host Microbial Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Cellular</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Humoral</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Innate</style></keyword><keyword><style  face="normal" font="default" size="100%">Lymphocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Myeloid Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Viral Proteins</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e10387</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34664389?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.</style></title><secondary-title><style face="normal" font="default" size="100%">Signal Transduct Target Ther</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Signal Transduct Target Ther</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Chemistry</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">drug repositioning</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Docking Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Targeted Therapy</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33311438?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Fanconi Anemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Jul 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">370</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31266445?dopt=Abstract</style></custom1></record></records></xml>