Welcome to SIDISH Documentation !
SIDISH (Semi-supervised Iterative Deep learning for Identifying Single-cell High-Risk populations) is an advanced deep learning framework designed to revolutionize biomarker discovery and therapeutic target identification. By seamlessly integrating the comprehensive breadth of bulk RNA sequencing (bulk RNA-seq) with the granular depth of single-cell RNA sequencing (scRNA-seq), SIDISH empowers researchers to uncover High-Risk disease-associated cell populations, prognostic biomarkers, and novel therapeutic targets—laying the foundation for transformative advancements in precision medicine. For more information, explore our paper.
What is SIDISH?
At its core, SIDISH is a semi-supervised iterative learning framework that bridges molecular data with clinical outcomes. It leverages cutting-edge machine learning techniques to address one of the most pressing challenges in biomedical research: identifying the cellular and genetic drivers of poor patient outcomes.
SIDISH combines several key components to achieve this goal:
High-Risk Cell Identification: Detects cell populations strongly associated with poor clinical outcomes through iterative refinement.
Prognostic Biomarker Discovery: Uncovers key genetic markers linked to disease progression and patient survival.
In Silico Therapeutic Simulation: Simulates gene perturbations to predict the impact of targeting specific genes, aiding in drug discovery and target validation.
SIDISH Framework Overview
The SIDISH workflow is structured around four key phases:
Extraction of Cellular Heterogeneity: Utilizing a variational autoencoder (VAE) to capture hidden cellular states from scRNA-seq data, preserving the biological complexity of tumor microenvironments.
Survival Prediction with Transfer Learning: Applying deep Cox regression models trained on bulk RNA-seq data to predict patient survival risks, effectively transferring learned representations across data modalities.
Risk Stratification: Identifying High-Risk cells and patient groups based on survival predictions, supported by robust statistical models for accurate risk categorization.
Iterative Learning and Weight Updates: Incorporating feedback loops using feature attribution methods (e.g., SHAP values) to iteratively refine gene and cell-level predictions.
Additionally, SIDISH features an in silico perturbation module, enabling virtual gene knockouts to simulate therapeutic interventions, predict drug responses, and prioritize targets for experimental validation.
SIDISH is run in four phases:
a, Phase 1: Extraction of Cellular Heterogeneity via a Variational Autoencoder (VAE) trained on scRNA-seq data to capture key biological patterns.
b, Phase 2: Survival Prediction Using Transfer Learning through a deep Cox regression model optimized for patient survival risk assessment.
c, Phase 3: Risk Prediction and Stratification using survival scores to identify High-Risk cells and patient groups.
d, Phase 4: Iterative Weight Updates leveraging SHAP-based feature attribution to enhance model performance.
e, In Silico Perturbation simulating gene knockouts to predict the impact of potential therapeutic targets.
f, Core Functionalities of SIDISH encompassing biomarker discovery, High-Risk cell identification, and precision medicine applications.
Key Features
Iterative Learning Framework: Continuously improves model accuracy in identifying High-Risk cell populations through a robust semi-supervised iterative approach.
Deep Survival Analysis: Employs deep Cox regression models for high-precision survival risk prediction, directly linking molecular data to patient outcomes.
In Silico Gene Perturbation: Simulates gene knockouts computationally to identify potential therapeutic targets and predict treatment efficacy.
Multi-Omics Integration: Bridges bulk RNA-seq and single-cell RNA-seq data to provide a holistic view of disease mechanisms at both the population and cellular levels.
Scalable & Versatile: Demonstrated effectiveness across multiple cancer types, including pancreatic ductal adenocarcinoma (PDAC), breast cancer (BRCA), and lung adenocarcinoma (LUAD).
Precision Medicine-Driven Insights: SIDISH is uniquely designed to power precision medicine initiatives, offering tools to tailor therapeutic strategies based on individual patient profiles, cellular dynamics, and genetic vulnerabilities.
Why Choose SIDISH?
SIDISH revolutionizes biomarker discovery by unifying detailed single-cell insights with large-scale clinical data. It outperforms traditional approaches through:
Enhanced Precision: Identifies rare High-Risk subpopulations often missed by conventional methods.
Clinical Relevance: Directly links cellular features to patient survival outcomes, facilitating translational research.
Therapeutic Discovery: Predicts druggable targets through computational perturbation analysis, accelerating therapeutic development.
Personalized Insights: Powers precision medicine initiatives by uncovering patient-specific biomarkers for targeted therapies.
SIDISH is designed for researchers, clinicians, and data scientists dedicated to advancing personalized medicine and improving patient outcomes.
Note
This project is under active development.