> For the complete documentation index, see [llms.txt](https://svai.gitbook.io/research-to-the-people/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://svai.gitbook.io/research-to-the-people/specialized-biological-domains/svai-research-team-mvps.md).
# SVAI Research Team MVPs
## **AutoNF2 Team**
**Led by Jyotika Varshney, DVM, PhD, used an unsupervised transfer learning approach to identify novel drug targets for NF2, also identifying miR-200a as a potential circulating marker for NF2.**
* **Applying deep learning to identify potential novel therapeutic targets!**
### Pipeline Walk-Through
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## [**DeeperDrugs**](https://docs.google.com/presentation/d/1xOUtJlJruLMoNxX9EIZ7QyhGl22pDwlz9B3bTNJvjuA/edit#slide=id.g3abd7e4cad_20_0)
**Implementation of rigorous variant filtering and target pruning, including a CRISPR/Cas9 repair design, that pipelines into drug discovery with deep learning, which includes training a DeepChem graph convolutional model, searching for optimal hyperparameters, and applying downstream experimental verification.**
### Pipeline Walk-Through
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## [**AIzheng**](http://rarekidneycancer.org/sites/default/files/aizheng_presentation.pdf)
**Modeled TCGA-RCC tumors as a “time series” across stage (Normal, I, II, III, IV) for each subtype of p1RCC: KIRC, KIRP, KICH. This team evaluated ability of a neural network to discriminate across subtype and stage, constructed stage-specific co-expression networks, and finally identified shared gene interaction communities across each tumor stage.**
### Pipeline Walk-Through
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## [**DamTheRiver**](https://docs.google.com/presentation/d/1uTl-QplO-Egoc_4O1T_3dI5O7dP1Zx-CfdYVvCqy6U4/edit#slide=id.p1)
**Identified of neo-antigens present within patent P1RCC sequence data by machine learning major histocompatibility complex affinity tool. This team implemented a very clean, sophisticated pipeline that ultimately identified 25 peptides with high MHC binding affinity.**
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### Pipeline Walk-Through
## [**ExpressForce**](https://docs.google.com/presentation/d/1zcuwuSCY8RJEpJyCZO71cHcGv9kKUxG78hJgxsFbA1s/edit#slide=id.p1)
**“Nexflix For Genes”: Provided candidate biomarkers for p1RCC via a collaborative filtering using probability matrix factorization after obtaining data from COSMIC, a well-known online cancer catalogue. As an overview, this team created a Sample ID vs Gene matrix table, implemented Naive Bayes algorithm, created entity embeddings of categorical variables, and applied dimensional reduction to find candidate biomarkers for p1RCC.**
### Pipeline Walk-Through
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