Results 231 to 240 of about 2,739,450 (298)

Matrix metalloproteinase‐9 regulates cell adhesion and membrane protrusive activity of ovarian cancer cells

FEBS Open Bio, EarlyView.
Matrix metalloproteinase‐9 (MMP9) drives ovarian cancer progression. Using MMP9‐null cells (M9‐KO) created from ovarian cancer cells, we found MMP9 loss did not block Epidermal Growth Factor (EGF)‐driven E‐cadherin dissolution or EMT but delayed and reduced EGF‐driven membrane protrusions. Transient MMP9 re‐expression drove membrane protrusion.
Claire Strauel, Sam Bennett, Natalie Ehlinger, Nelson Cook, Katelyn Marvin, Caleb L. Lines, Sam Reyes, Brooke Kirby, Shawn M. Ellerbroek   +8 more
wiley   +1 more source

Decoupled dual-granularity rebalanced pyramid network for drug-target interaction prediction. [PDF]

Brief Bioinform
Dong Z, Zhang Y, Shi Y, Zhang C, Han C, Li H, Gao Y, Fu W, Lu Y, Ren P, Liang L, Liu Z.   +11 more
europepmc   +1 more source

Quantitative proteomic analysis reveals different characteristics of bladder cancer cells after exposure to bisphenol A

FEBS Open Bio, EarlyView.
Bisphenol A (BPA), a common chemical in plastics, exerts dual effects on bladder cancer cells: low doses promote growth and migration, while high doses suppress growth and migration. Multi‐omics and bioinformatics reveal BPA acts via MAPK and inflammatory pathways.
Shaomin Niu, Lanlan Li, Wenjuan Jia, Hanzhong Feng, Xiaoquan Yu, Youli Zhao, Fengzhi Yuan, Hui Ding, Junqiang Tian, Yong‐Xing He, Zhiping Wang   +10 more
wiley   +1 more source

The Omp85 family protein, TamA, exhibits characteristics of a suitable drug target against <i>Pseudomonas aeruginosa</i>. [PDF]

Microbiology (Reading)
Duodu R, Boocock DJ, Hoyles L, Leo JC.
europepmc   +1 more source

Digital twins to accelerate target identification and drug development for immune‐mediated disorders

FEBS Open Bio, EarlyView.
Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley   +1 more source

Enhancing Local Functional Structure Features to Improve Drug-Target Interaction Prediction. [PDF]

Int J Mol Sci
Feng B, Du H, Tong HHY, Wang X, Li K.
europepmc   +1 more source

An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions. [PDF]

PLoS One
Roberts-Nuttall J, Jones AM, Castellani M, Pham D.   +3 more
europepmc   +1 more source

Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling. [PDF]

Brief Bioinform
Chen F, Zhao R, Han X, Li H, Tang Z.
europepmc   +1 more source

BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks. [PDF]

Quant Biol
Sartono A, Trilaksono BR, Damayanti S, Nugroho AS, Firdayani F.   +4 more
europepmc   +1 more source

Causal proteomic insights into drug target discovery for tension-type headache. [PDF]

J Headache Pain
Gao R, Wang R, Xiong Z.
europepmc   +1 more source