Results 181 to 190 of about 563,010 (309)

DGHNN: a deep graph and hypergraph neural network for pan-cancer related gene prediction. [PDF]

Bioinformatics
Li B, Xiao X, Zhang C, Xiao M, Zhang L.
europepmc   +1 more source

Gene prediction of aging-related diseases based on DNN and Mashup. [PDF]

BMC Bioinformatics, 2021
Ye J, Wang S, Yang X, Tang X.
europepmc   +1 more source

Therapeutic strategies for MMAE‐resistant bladder cancer through DPP4 inhibition

Molecular Oncology, EarlyView.
We established monomethyl auristatin E (MMAE)‐resistant bladder cancer (BC) cell lines by exposure to progressively increasing concentrations of MMAE in vitro. RNA sequencing showed DPP4 expression was increased in MMAE‐resistant BC cells. Both si‐DPP4 and the DPP4 inhibitor sitagliptin suppressed the viability of MMAE‐resistant BC cells.
Gang Li, Shuichi Tatarano, Hirofumi Yoshino, Saeki Saito, Mitsuhiko Tominaga, Junya Arima, Ikumi Fukuda, Takashi Sakaguchi, Ryosuke Matsushita, Yasutoshi Yamada, Hideki Enokida   +10 more
wiley   +1 more source

Altered expression spectrum and target gene prediction of tRNA-derived small RNAs in clear cell renal cell carcinoma. [PDF]

BMC Cancer
Liu S, Cao H, Chen B, Jiang L, Nueraihemaiti Y, Gao Y, Zhan X, Chen Y, Zhou H, Liu D, Chen H, Xu X, Xie T, Mei W, Dong Y, Xu Y.   +15 more
europepmc   +1 more source

Disease gene prediction with privileged information and heteroscedastic dropout. [PDF]

Bioinformatics, 2021
Shu J, Li Y, Wang S, Xi B, Ma J.
europepmc   +1 more source

Predictive genes in bladder cancer [PDF]

Nature Reviews Clinical Oncology, 2010
openaire   +3 more sources

Recurrent cancer‐associated ERBB4 mutations are transforming and confer resistance to targeted therapies

Molecular Oncology, EarlyView.
We show that the majority of the 18 analyzed recurrent cancer‐associated ERBB4 mutations are transforming. The most potent mutations are activating, co‐operate with other ERBB receptors, and are sensitive to pan‐ERBB inhibitors. Activating ERBB4 mutations also promote therapy resistance in EGFR‐mutant lung cancer.
Veera K. Ojala, Sini Ahonen, Sara Peltola, Aura Tuohisto‐Kokko, Olaya Esparta, Peppi Suominen, Anne Jokilammi, Iman Farahani, Deepankar Chakroborty, Nikol Dibus, Steffen Boettcher, Tomi T. Airenne, Mark S. Johnson, Lisa D. Eli, Klaus Elenius, Kari J. Kurppa   +15 more
wiley   +1 more source

Genomic language models (gLMs) decode bacterial genomes for improved gene prediction and translation initiation site identification. [PDF]

Brief Bioinform
Akotenou G, El Allali A.
europepmc   +1 more source

QTL analysis and candidate gene prediction for seed density per silique by QTL-seq and RNA-seq in spring Brassica napus L. [PDF]

PLoS One, 2023
Xing X, Liu H, Ye J, Yao Y, Li K, Li Y, Du D.   +6 more
europepmc   +1 more source

Leveraging Transcriptomics-Based Approaches to Enhance Genomic Prediction: Integrating SNP weights and gene-networks for Cotton Fibre Quality Improvement [PDF]

Nima Khalili Samani, Zitong Li, Filomena Pettolino, Philippe Moncuquet, Antônio Reverter, Colleen P. MacMillan   +5 more
openalex   +1 more source