Results 201 to 210 of about 11,616,863 (296)

Transfer learning for honey bee toxicity prediction: MolFormer versus classical QSAR representations. [PDF]

Ecotoxicology
de Souza Pinho AV, de Alencar Filho EB, de Oliveira Neto RF.   +2 more
europepmc   +1 more source

In vitro and in silico modelling of ROS1‐positive non‐small cell lung cancer reveals fusion‐dependent tyrosine kinase inhibitor responses

Molecular Oncology, EarlyView.
Drug resistance limits treatment success in a subset of lung cancers driven by ROS1 gene alterations. Using patient‐derived cells and computer simulations, we studied three key mutations and how they affect five targeted drugs. The mutations reduced drug effectiveness in different ways by altering protein structure and behavior.
Farhan Ul Haq, Marc Terrones, Felicia Rodrigues Fortes, Anne Schepers, Alessia Denis, Christophe Deben, Ken Op de Beeck, Guy Van Camp, Geert Vandeweyer   +8 more
wiley   +1 more source

Interpretable Deep Learning of Molecule-Solvent Interactions Using a Solvatochromic Subgroup Contribution Framework. [PDF]

Adv Sci (Weinh)
Park J, Han M, Lee K, Kim S, Park S.
europepmc   +1 more source

Circulating microRNA signatures of cachexia and cancer in Canis familiaris as a comparative oncology model for human disease

Molecular Oncology, EarlyView.
Circulating microRNAs as biomarkers of cachexia and sex‐specific cancer in senior dogs. In 25 client‐owned dogs, four circulating miRNAs (miR‐15a, miR‐15b, miR‐16, miR‐140) were downregulated in cachexia, with miR‐16 the strongest individual biomarker (AUC = 0.899).
Soon‐Seok Park, Kyongman An, Gyeonghwa Kim, Soo‐Nyun Choi, Hong‐Ki Lee, Keun Hur, Kyu‐Shik Jeong   +6 more
wiley   +1 more source

Retraction notice to “Increased levels of Th17 cells are associated with non-neuronal acetylcholine in COPD patients” [Immunobiology 219(5) (2014) 392–401]

Immunobiology
Mirella Profita, Giusy Daniela Albano, Loredana Riccobono, Caterina Di Sano, Angela Marina Montalbano, Rosalia Gagliardo, Giulia Anzalone, Anna Bonanno, Michael Paul Pieper, Mark Gjomarkaj   +9 more
doaj   +1 more source

Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction. [PDF]

Genome Biol
Danner M, Islam T, Begemann M, Kraft F, Elbracht M, Kurth I, Krause J.   +6 more
europepmc   +1 more source

Preoperative circulating tumor cells integrated with imaging analysis for prognostic evaluation in head and neck squamous cell carcinoma

Molecular Oncology, EarlyView.
Detecting circulating tumor cells (CTCs) in blood before surgery may help predict outcomes in patients with head and neck squamous cell carcinoma (HNSCC). Here, we show when combined with tumor size and lymph node involvement from routine imaging, CTC status identifies high‐risk patients with poorer survival—offering a simple, minimally invasive tool ...
Susanne Flach, Gizem Abaci, Tom Huberty, Axel Lechner, Lukas Käsmann, Jens Ricke, Sophia Stöcklein, Martin Canis, Philipp Baumeister, Marianna Alunni‐Fabbroni   +9 more
wiley   +1 more source

Cell Population Dynamics Informed by Cell-Cycle Regulation: A Deterministic Modeling Toolkit. [PDF]

Comput Struct Biotechnol J
Ferro E, Laus A, Atzeni R, Valentini M, Pieroni E, Pisu M.   +5 more
europepmc   +1 more source

Epigenetic silencing of the liver‐specific lncRNA LUNAR promotes liver cancer progression via NOTCH activation

Molecular Oncology, EarlyView.
LUNAR is a liver‐specific long noncoding RNA (lncRNA) that is highly expressed in normal liver but becomes epigenetically silenced in hepatocellular carcinoma through promoter hypermethylation. Loss of LUNAR is associated with NOTCH activation, epithelial–mesenchymal transition, and metastasis, whereas restoring LUNAR restrains metastatic progression ...
Se Ha Jang, Hyung Seok Kim, Geum Ok Baek, Moon Gyeong Yoon, Su In Lee, Jee‐Yeong Jeong, Ji Eun Han, Soon Sun Kim, Jae Youn Cheong, Jung Woo Eun   +9 more
wiley   +1 more source

A Comparative Evaluation Framework Integrating Machine Learning and Deep Learning Models with ADME-Based Pharmacokinetic Assessment for HIV-Related Compounds. [PDF]

Pharmaceuticals (Basel)
Das B, Uslu H, Bostancioglu A, Goktas B, Santur Y, Yilmaz S, Türkoğlu İ.   +6 more
europepmc   +1 more source