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README.md

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+# Project PhenoSeq: Protein Network Analysis for Phenotypic Outcomes
+
+While demonstrating promising results in basic prediction tasks, the project identified key areas for improvement in protein-phenotype relationship modeling. The findings provide a foundation for future work in protein network analysis and phenotype prediction.
+
+*This project represents a significant step forward in understanding protein-phenotype relationships, while highlighting important areas for future research and development in computational biology.*
+
+## Project Overview
+PhenoSeq is an innovative project focused on understanding how protein networks contribute to organism-scale phenotypes, particularly in cancer growth and organism longevity. The project leverages protein embeddings from ESM (Evolutionary Scale Modeling) combined with graph neural networks to predict phenotypic outcomes through protein-protein interactions (PPIs).
+
+## Core Objectives
+1. Develop predictive models for understanding biological drivers of complex diseases
+2. Create frameworks for inferring oncogenic potential of genetic mutations
+3. Analyze clinical significance of protein modifications using sequence embeddings
+4. Establish connections between protein networks and phenotypic outcomes
+
+## Data Sources
+The project utilized three major public databases:
+- DepMap: CRISPR-based experimental data measuring protein deletion effects on cancer cell proliferation
+- TCGA: The Cancer Genome Atlas data
+- Longevity Database: Species longevity information
+
+## Methodological Approach
+
+### Model Development
+The team developed three distinct models:
+
+1. **Baseline Model**
+   - Fully connected network predicting CRISPR scores from embeddings
+   - Achieved correlation of 0.55 with ground truth
+   - Outperformed K-nearest neighbors baseline
+   - Performance correlated with training set proximity
+
+2. **Cell Line-Specific Model**
+   - Incorporated cell line identity through one-hot embedding
+   - Included mutation status (wild type vs mutated)
+   - Achieved 0.44 correlation with ground truth
+   - Limited success in predicting cell line-specific differences
+
+3. **PPI-Informed Model**
+   - Integrated protein-protein interaction data
+   - Results comparable to cell line-specific model
+   - Limited additional performance gain from PPI integration
+
+### Additional Analyses
+- Species Longevity Analysis
+  - Challenges in cross-phylogenetic prediction
+  - Limited success across different orders of the phylogenetic tree
+
+- TCGA Patient Survival Analysis
+  - Achieved significant correlations
+  - Performance below initial expectations
+
+## Key Findings
+1. ESM3 embeddings contain valuable functional information
+2. Simple models can outperform basic baselines
+3. Current approach limitations in capturing subtle effects
+4. Challenges in predicting mutation-specific impacts
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/gDGJH2ErnqGcHoF9DWuBc.png)
+
+## Future Directions
+1. Integration of additional data types:
+   - Copy number variation
+   - Transcriptomic information
+2. Exploration of amino acid level embeddings
+3. Enhanced signal processing methods
+4. Improved model architectures
+
+## Technical Achievements
+- Successful implementation of protein embedding analysis
+- Development of multiple predictive models
+- Integration of complex biological datasets
+- Novel approaches to phenotype prediction
+
+## Limitations and Challenges
+1. Limited success in cell line-specific predictions
+2. Challenges in cross-phylogenetic predictions
+3. Subtle effect detection limitations
+4. Data integration complexities
+
+## Impact and Applications
+- Enhanced understanding of disease mechanisms
+- Improved drug target identification
+- Better prediction of genetic mutation effects
+- Advanced protein function analysis
+
+# PhenoSeq Longevity Analysis Component
+
+This analysis revealed both the potential and limitations of using protein sequence data for predicting species longevity, highlighting the importance of taxonomic relationships in such predictions.
+
+## Overview
+The longevity analysis component of PhenoSeq investigated the relationship between protein sequences and species lifespan across different taxonomic orders, with a particular focus on Primates, Chiroptera (bats), and Cetacea (whales).
+
+## Key Findings
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/vS8Fe-q1lY5Oiro4FPVEP.png)
+
+### 1. Taxonomic Order Analysis
+- The study examined lifespan distributions across multiple orders including:
+  - Rodentia
+  - Artiodactyla
+  - Carnivora
+  - Primates
+  - Chiroptera
+  - Cetacea
+  - Diprotodontia
+  - Perissodactyla
+
+### 2. Prediction Performance
+- Mean predictions across orders were relatively successful
+- However, predictions within individual orders showed limited accuracy
+- High-performing proteins were not well conserved between different orders
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/V9r5W8k5K9BgbuJfkf1XQ.png)
+
+### 3. Model Architecture Insights
+- Later layers in the neural network did not provide significant additional information
+- Training curves showed convergence but with limitations in prediction accuracy
+
+### 4. Protein Embedding Analysis
+- Analysis of protein ALDOB showed that:
+  - Nearest neighbor species in embedding space typically belonged to the same Order/Family
+  - Strong taxonomic clustering was observed in the embedding space
+
+### 5. Hierarchical Prediction Accuracy
+Correlation strength increased with taxonomic specificity:
+- Order level: r = 0.8 (271 species across 12 orders)
+- Family level: r = 0.92 (191 species across 27 families)
+- Genus level: r = 0.97 (47 species across 15 genera)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/qHsUpGuLTIo3Nw3CHJDVM.png)
+
+## Technical Limitations
+- Limited success in cross-order predictions
+- Difficulty in generalizing predictions across distant phylogenetic relationships
+- Need for order/family-specific modeling approaches
+
+## Key Insights
+- Strong within-taxon predictions
+- Decreasing accuracy with increasing phylogenetic distance
+- Need for taxonomic stratification in prediction models
+- High predictive power at genus level suggests strong genetic influence on longevity within closely related species
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/hzumPD8BXOEAnyLzrCE5T.png)
+
+# PhenoSeq DepMap Analysis Component
+
+This analysis demonstrated both the potential and current limitations of using protein sequence data to predict cancer-relevant protein functions, highlighting areas for future improvement in protein-phenotype prediction models.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/_AJXp_IwAx9uzHjlXMLVT.png)
+
+## Overview
+The DepMap component investigated protein function in cancer through CRISPR-based knockout experiments, analyzing 9,353 proteins across 1,150 different cell lines to understand their effects on cancer cell growth.
+
+## Three  Models :
+
+1. **Baseline Model**
+- Input: Average protein embedding across all cell lines
+- Output: Average CrisprScore across all cell lines
+- Architecture: Simple feedforward network using ESM3-open-small embeddings
+- Performance: Achieved Pearson correlation of 0.55
+- Outperformed KNN baseline across all K values
+
+2. **Cell-line-specific Model**
+- Predicted CrisprScore effects for each protein-cell line combination
+- Performance: Achieved Pearson correlation of 0.44
+- Limited success in predicting protein-specific differences between cell lines
+- Poor correlation (r=0.01) for individual proteins like MYC across cancer types
+
+3. **PPI-informed Model**
+- Incorporated protein-protein interaction networks
+- Aimed to predict CrisprScore effects by propagating signals through PPI networks
+- Results similar to cell-line-specific model
+
+## Key Findings
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/T-B8Wm66A-oepjyA562zv.png)
+
+### Model Performance
+- Baseline model showed strong general prediction capability
+- Distance to nearest neighbors in training set affected performance
+- Larger networks didn't necessarily improve performance
+- Model demonstrated true learning rather than memorization
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/wllLuMZmsRpjmZRr1EJ0S.png)
+
+### Technical Insights
+- Hyperparameter sweeps showed similar training patterns across:
+  - Different numbers of layers
+  - Various hidden dimensions
+- Model struggled with fine-grained predictions of mutation effects
+
+### Limitations
+- Poor performance in predicting effects of small sequence differences
+- Limited ability to distinguish between mutations of the same protein
+- Challenges in cell-line-specific predictions
+
+## Technical Details
+- CrisprScore distribution showed varied effects of protein deletion
+- Different proteins showed distinct patterns of effect across cell lines
+- Model performance was consistent across different architectural choices
+
+## Future Implications
+- Need for improved mutation-specific prediction capabilities
+- Potential for enhanced protein function understanding
+- Opportunity for better cancer-specific protein effect prediction
+