Feature Extraction
Transformers
Joblib
Safetensors
BulkRNABert
bulk RNA-seq
biology
transcriptomics
custom_code
Instructions to use InstaDeepAI/BulkRNABert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/BulkRNABert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InstaDeepAI/BulkRNABert", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InstaDeepAI/BulkRNABert", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Data preprocessing
#1
by ndhieunguyen - opened
I have a question about the steps needed to process RNA-seq data before applying the pretrained model for feature extraction. From what I understand, I need to utilize Transcripts Per Million (TPM) and log10 to process the RNA-seq data. However, I'm unsure if this approach is correct, as I could only find the processed data in the repository. Could you provide me with some pseudocode or a Python implementation for processing the raw count data?
Thank you very much.