Results 231 to 240 of about 1,718,808 (353)

The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

Journal of Molecular Biology, 2016
G. V. Zundert, J. Rodrigues, M. Trellet, C. Schmitz, P. Kastritis, E. Karaca, A. Melquiond, M. Dijk, S. J. D. Vries, A. Bonvin   +9 more
semanticscholar   +1 more source

MicroRNAs in seminal plasma are able to discern infertile men at increased risk of developing testicular cancer

Molecular Oncology, Volume 19, Issue 4, Page 1188-1202, April 2025.
Male infertility is a known risk factor for the development of testicular cancer. In this paper, we analyzed the expression profile of a microRNA panel by real‐time PCR and validated the results by digital PCR. Experimental and bioinformatics analyses allowed us to identify possible biomarkers able to discern men with a higher risk of developing ...
Carmen Ferrara, Rosalia Battaglia, Angela Caponnetto, Anna Fazzio, Michele Stella, Cristina Barbagallo, Nicolò Musso, Federica Lunelio, Maria Elena Vento, Placido Borzì, Paolo Scollo, Davide Barbagallo, Marco Ragusa, Salvatore Pernagallo, Cinzia Di Pietro   +14 more
wiley   +1 more source

Immunoinformatics method to design universal multi-epitope nanoparticle vaccine for TGEV S protein. [PDF]

Sci Rep
Li S, Yu J, Xiao C, Li Y.
europepmc   +1 more source

Detection rate for ESR1 mutations is higher in circulating‐tumor‐cell‐derived genomic DNA than in paired plasma cell‐free DNA samples as revealed by ddPCR

Molecular Oncology, EarlyView.
Analysis of ESR1 mutations in plasma cell‐free DNA (cfDNA) is highly important for the selection of treatment in patients with breast cancer. Using multiplex‐ddPCR and identical blood draws, we investigated whether circulating tumor cells (CTCs) and cfDNA provide similar or complementary information for ESR1 mutations.
Stavroula Smilkou, Aliki Ntzifa, Victoria Tserpeli, Ioanna Balgkouranidou, Alkistis Papatheodoridi, Evangelia Razis, Helena Linardou, Christos Papadimitriou, Amanda Psyrri, Flora Zagouri, Stylianos Kakolyris, Evi Lianidou   +11 more
wiley   +1 more source

Group verifiable secure aggregate federated learning based on secret sharing. [PDF]

Sci Rep
Zhou S, Wang L, Chen L, Wang Y, Yuan K.
europepmc   +1 more source

Maize’s origin to be revisited

Plant Signaling & Behavior
Khaled Moustafa
doaj   +1 more source

Multi‐omics profiling reveals key factors involved in Ewing sarcoma metastasis

Molecular Oncology, Volume 19, Issue 4, Page 1002-1028, April 2025.
Using a mouse model that recapitulates the spread of tumor cells within the body, we have profiled paired tumors and metastasis samples using a multiomics approach combining different layers of biological information. We identified key genes that drive metastasis in Ewing sarcoma.
Mariona Chicón‐Bosch, Sara Sánchez‐Serra, Marta Rosàs‐Lapeña, Nicolás Costa‐Fraga, Judit Besalú‐Velázquez, Janet Illa‐Bernadí, Silvia Mateo‐Lozano, Florencia Cidre‐Aranaz, Thomas G.P. Grünewald, Ángel Díaz‐Lagares, Roser Lopez‐Alemany, Òscar M. Tirado   +11 more
wiley   +1 more source

Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile edge systems. [PDF]

Sci Rep
Nishad DK, Verma VR, Rajput P, Gupta S, Dwivedi A, Shah DR.   +5 more
europepmc   +1 more source

Robust acute myeloid leukemia engraftment in humanized scaffolds using injectable biomaterials and intravenous xenotransplantation

Molecular Oncology, Volume 19, Issue 5, Page 1371-1385, May 2025.
Patient‐derived xenografts (PDXs) can be improved by implantation of a humanized niche. We tested different biomaterials and approaches, and demonstrate that the combination of an injectable biomaterial for scaffold creation plus an intravenous route for acute myeloid leukemia (AML) xenotransplantation provide the most convenient and robust approach to
Daniel Busa, Zdenka Herudkova, Jan Hyl, Jakub Vlazny, Filip Sokol, Kvetoslava Matulova, Adam Folta, Jakub Hynst, Lucy Vojtova, Leos Kren, Martin Repko, Zdenek Racil, Jiri Mayer, Martin Culen   +13 more
wiley   +1 more source

Research on memory failure prediction based on ensemble learning. [PDF]

PLoS One
Zhang P, Zhang J, Li Y.
europepmc   +1 more source