👋 Welcome to BioF3's Daily Trends! Today's edition features 3 GitHub projects and 1 research papers from bioRxiv, arXiv, and PubMed.
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1. tAge
🔧 GitHub Project | Language:
Jupyter Notebook| ⭐0| 🍴0
Transcriptomic Age Analysis Package
AI Technical Review (深度解读)
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Description: Transcriptomic Age Analysis Package Topics:
README:
tAge
R package for transcriptomic age prediction from gene expression data.
Installation
```r devtools::install_github("Gladyshev-Lab/tAge") ...
2. Transcriptomics_INP
🔧 GitHub Project | Language:
Jupyter Notebook| ⭐1| 🍴0
Proyectos transcriptómicos del IPN.
AI Technical Review (深度解读)
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Description: Proyectos transcriptómicos del IPN. Topics:
README:
Transcriptomics INP
Informacíon y material relacionado con los proyectos transcriptómicos del IPN.
...
3. spatial-transcriptomics-analysis
🔧 GitHub Project | Language:
R| ⭐0| 🍴0
Spatial transcriptomics analysis of mouse brain data using Seurat and 10X Visium datasets.
AI Technical Review (深度解读)
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Description: Spatial transcriptomics analysis of mouse brain data using Seurat and 10X Visium datasets. Topics:
README:
Spatial Transcriptomics Analysis
This repository contains analysis performed for the Single Cell Bioinformatics course at Saarland University.
Tools Used
- R
- Seurat
- Ce...
4. Adversarial Domain Adaptation Enables Knowledge Transfer Across Heterogeneous RNA-Seq Datasets
📄 arXiv Paper | Date:
2026-03-09| Category:q-bio.GN
Authors: Kevin Dradjat, Massinissa Hamidi, Blaise Hanczar
AI Research Digest (科研解读)
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Accurate phenotype prediction from RNA sequencing (RNA-seq) data is essential for diagnosis, biomarker discovery, and personalized medicine. Deep learning models have demonstrated strong potential to outperform classical machine learning approaches, but their performance relies on large, well-annota...
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