Results 211 to 220 of about 1,289,019 (331)
Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis
This study delivers the first systematic, task‐level evaluation of single‐cell foundation models across eight core analytical tasks. By benchmarking 10 leading models with the scEval framework, it reveals where foundation models truly add value, where task‐specific methods still dominate, and provides concrete, reproducible guidelines to steer the next
Tianyu Liu +4 more
wiley +1 more source
Bioinformatics Tools for the Prediction of T-Cell Epitopes
Massimo Andreatta, Morten Nielsen
openalex +2 more sources
Zinc Exposure Causes Disulfidptosis to Induce Miscarriage by Up‐Regulating GATA1/METTL1/SLC7A11 Axis
Zn exposure up‐regulates GATA1, promoting GATA1‐mediated METTL1 and SLC7A11 transcription. It also enhances METTL1‐mediated m7G modification on SLC7A11 mRNA, increasing SLC7A11 mRNA stability. Ultimately, Zn exposure up‐regulates SLC7A11 at both transcriptional and post‐transcriptional levels, causing disulfidptosis. Knockdown of murine Slc7a11, Gata1,
Wenxin Huang +16 more
wiley +1 more source
This study reveals that m6A regulators cooperatively upregulate BGN in melanoma, promoting malignancy. Within the tumor microenvironment, CAFs show highest BGN expression. The BGN/MDK axis mediates cancer‐stroma crosstalk, driving normal fibroblast (NF) activation and enhancing the pro‐tumor effect of CAFs, highlighting a promising therapeutic target ...
Hao‐ze Shi +16 more
wiley +1 more source
scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data. [PDF]
Tornisiello R, Kretzmer H.
europepmc +1 more source
This study discovered a new pathway that tells fruit flies when to stop eating. It found that rising blood sugar (fructose) is detected by a sensor called GR43a. This triggers a chain reaction involving the satiety signal sulfakinin and its receptor, ultimately activating a final satiety signal, ILP5.
Hong‐Fei Li +7 more
wiley +1 more source
Pretraining improves prediction of genomic datasets across species. [PDF]
Huang F, Wang Y, Cutkosky A, Song JHT.
europepmc +1 more source

