Results 231 to 240 of about 490,947 (358)
Integrating -Omic Technologies across Modality, Space, and Time to Decipher Remodeling in Cardiac Disease. [PDF]
Mulvey JF+3 more
europepmc +1 more source
Temporal proteome profiling of Botrytis cinerea reveals proteins involved in plant invasion and survival. [PDF]
Singh S, Hegde M, Kaur I, Adlakha N.
europepmc +1 more source
Proteomic Analysis of Arabidopsis Seed Germination and Priming [PDF]
Karine Gallardo+6 more
openalex +1 more source
Icaritin demonstrates broad antitumor effects by inhibiting colorectal cancer (CRC) cell proliferation, migration, invasion, and causing cell cycle arrest. It induces apoptosis by repressing autophagic flux through disrupting HSP90‐TXNDC9 interactions.
Dan He+6 more
wiley +1 more source
Proteomic Approach to Identify Novel Mitochondrial Proteins in Arabidopsis [PDF]
Volker Kruft+4 more
openalex +1 more source
Peptidyl arginine deiminase 2 (PAD2) plays a key role in regulating macrophage function in Pseudomonas aeruginosa‐induced acute lung injury (ALI). Using single‐cell RNA sequencing and proteomics, a new PAD2‐catalyzed citrullination site on NF‐κB p65 (171 Arginine), modulating macrophage polarization is identified.
Xin Yu+11 more
wiley +1 more source
The hepatocellular model of fatty liver disease: from current imaging diagnostics to innovative proteomics technologies. [PDF]
Hernandez R+10 more
europepmc +1 more source
Unwrapping the Ciliary Coat: High‐Resolution Structure and Function of the Ciliary Glycocalyx
The ciliary membrane is decorated in glycosylated proteins that define the interaction of the cilium with its environment. The main component of the ciliary coat of the green alga Chlamydomonas reinhardtii, FMG1, is characterized by cryo‐electron microscopy, proteomics, and live cell microscopy with flow‐based adhesion assays.
Lara M. Hoepfner+8 more
wiley +1 more source
A Proteomic View on Genome-Based Signal Peptide Predictions [PDF]
Haike Antelmann+6 more
openalex +1 more source
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
Unexpected toxicity accounts for 30% of drug development failures. This review highlights ML innovations in predicting drug‐induced toxicity, emphasizing comparative analyses, interpretable algorithms, and multi‐source data integration. It categorizes toxicity types, summarizes ML models, and organizes key databases, offering strategies to address ...
Changsen Bai+5 more
wiley +1 more source