update readme citations

README.md

@@ -6,14 +6,15 @@ tags:
 - genomics
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 # Geneformer
-Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
+Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
 
 - See [our manuscript](https://rdcu.be/ddrx0) for details of the original model trained on ~30 million transcriptomes in June 2021 and the initial report of our in silico perturbation and cell and gene classification strategies.
-- See [our manuscript](https://www.biorxiv.org/content/10.1101/2024.08.16.608180v1.full.pdf) for details of the expanded model, now trained on ~104 million transcriptomes, and our continual learning, multitask learning, and quantization strategies.
+- See [our manuscript](https://rdcu.be/famFk) for details of the expanded model, now trained on ~104 million transcriptomes, and our quantization implementation for resource-efficient predictions.
+- See [our preprint](https://www.biorxiv.org/content/10.1101/2024.08.16.608180v1.full.pdf) for details of our continual and multitask learning strategies.
 - See [geneformer.readthedocs.io](https://geneformer.readthedocs.io) for documentation.
 
 # Model Description
-Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes representing a broad range of human tissues. Geneformer V1 was originally pretrained in June 2021 on [Genecorpus-30M](https://huggingface.co/datasets/ctheodoris/Genecorpus-30M), a corpus comprised of ~30 million human single cell transcriptomes. We excluded cells with high mutational burdens (e.g. malignant cells and immortalized cell lines) that could lead to substantial network rewiring without companion genome sequencing to facilitate interpretation. The current updated Geneformer V2 is pretrained on ~104 million human single cell transcriptomes (non-cancer). The cancer continual learning V2 variant was continually pretrained on ~14 million cancer transcriptomes to yield a cancer domain-tuned model.
+Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes representing a broad range of human tissues. Geneformer V1 was originally pretrained in June 2021 on [Genecorpus-30M](https://huggingface.co/datasets/ctheodoris/Genecorpus-30M), a corpus comprised of ~30 million human single cell transcriptomes. We excluded cells with high mutational burdens (e.g. malignant cells and immortalized cell lines) that could lead to substantial network rewiring without companion genome sequencing to facilitate interpretation. The current updated Geneformer V2 is pretrained on ~104 million human single cell transcriptomes (non-cancer) from [Genecorpus-104M](https://huggingface.co/datasets/theodoris-lab/Genecorpus-104M). The cancer continual learning V2 variant was continually pretrained on ~14 million cancer transcriptomes to yield a cancer domain-tuned model.
 
 Each single cell’s transcriptome is presented to the model as a rank value encoding where genes are ranked by their expression in that cell scaled by their expression across the entire Genecorpus (~30M for V1, ~104M for V2). The rank value encoding provides a nonparametric representation of that cell’s transcriptome and takes advantage of the many observations of each gene’s expression across the pretraining corpus to prioritize genes that distinguish cell state. Specifically, this method will deprioritize ubiquitously highly-expressed housekeeping genes by scaling them to a lower rank. Conversely, genes such as transcription factors that may be lowly expressed when they are expressed but highly distinguish cell state will move to a higher rank within the encoding. Furthermore, this rank-based approach may be more robust against technical artifacts that may systematically bias the absolute transcript counts value while the overall relative ranking of genes within each cell remains more stable.
 
@@ -88,4 +89,4 @@ Please note that GPU resources are required for efficient usage of Geneformer. A
 
 # Citations
 - C V Theodoris#, L Xiao, A Chopra, M D Chaffin, Z R Al Sayed, M C Hill, H Mantineo, E Brydon, Z Zeng, X S Liu, P T Ellinor#. Transfer learning enables predictions in network biology. _**Nature**_, 31 May 2023. (#co-corresponding authors)
-- H Chen*, M S Venkatesh*, J Gomez Ortega, S V Mahesh, T Nandi, R Madduri, K Pelka†, C V Theodoris†#. Quantized multi-task learning for context-specific representations of gene network dynamics. _**bioRxiv**_, 19 Aug 2024. (*co-first authors, †co-senior authors, #corresponding author)
+- H Chen*, M S Venkatesh*, J Gomez Ortega, S V Mahesh, T Nandi, R Madduri, K Pelka†, C V Theodoris†#. Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology. _**Nature Computational Science**_, 27 Mar 2026. (*co-first authors, †co-senior authors, #corresponding author)