in-silico-perturbation-oracle
Virtual gene knockout simulation using foundation models to predict transcriptional changes
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aipoch-ai/in-silico-perturbation-oracle-1In Silico Perturbation Oracle
ID: 207
Category: Bioinformatics / Genomics / AI-Driven Drug Discovery
Status: ✅ Production Ready
Version: 1.0.0
⚠️ Note: This tool provides a framework for in silico perturbation analysis. Actual predictions require integration with biological foundation models (Geneformer, scGPT, etc.) and wet lab validation data.
Overview
In Silico Perturbation Oracle is a computational biology tool based on biological foundation models (Geneformer, scGPT, etc.) for performing "virtual gene knockout (Virtual KO)" in silico to predict changes in cellular transcriptome states after specific gene deletions.
This tool provides AI-driven decision support for target screening before wet lab experiments, significantly reducing drug development time and costs.
Features
| Function Module | Description | Status |
|---|---|---|
| 🧬 Gene Knockout Simulation | In silico KO prediction based on pre-trained models | ✅ |
| 📊 Differential Expression Analysis | Predict DEGs (Differentially Expressed Genes) after knockout | ✅ |
| 🔄 Pathway Enrichment Analysis | GO/KEGG pathway change prediction | ✅ |
| 🎯 Target Scoring | Multi-dimensional target scoring and ranking | ✅ |
| 📈 Visualization Report | Generate interpretable charts and reports | ✅ |
| 🔗 Wet Lab Interface | Export wet lab validation recommendations | ✅ |
Supported Models
| Model | Description | Applicable Scenarios |
|---|---|---|
| Geneformer | Transformer-based gene expression foundation model | General gene regulatory network inference |
| scGPT | Single-cell multi-omics foundation model | Single-cell level perturbation prediction |
| scFoundation | Large-scale single-cell foundation model | Cross-cell type generalization prediction |
| Custom | User-defined models | Specific disease/tissue customization |
Installation
# Basic dependencies
pip install torch transformers scanpy scvi-tools
# Bioinformatics tools
pip install gseapy enrichrpy
# Model-specific dependencies
pip install geneformer scgpt
Usage
Quick Start
# Single gene knockout prediction
python scripts/main.py \
--model geneformer \
--genes TP53,BRCA1,EGFR \
--cell-type "lung_adenocarcinoma" \
--output ./results/
# Batch target screening
python scripts/main.py \
--model scgpt \
--genes-file ./target_genes.txt \
--cell-type "hepatocyte" \
--top-k 20 \
--pathways KEGG,GO_BP \
--output ./results/
Python API
from in_silico_perturbation_oracle import PerturbationOracle
# Initialize Oracle
oracle = PerturbationOracle(
model_name="geneformer",
cell_type="cardiomyocyte"
)
# Execute virtual knockout
results = oracle.predict_knockout(
genes=["MYC", "KRAS", "BCL2"],
perturbation_type="complete_ko", # Complete knockout
n_permutations=100
)
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-aipoch-ai-in-silico-perturbation-oracle-1": {
"enabled": true,
"auto_update": true
}
}
}Related Skills
mechanism-flowchart
Generates Mermaid flowchart code and visual diagrams for pathophysiological.
reference-style-sync
One-click synchronization and standardization of reference formats in literature management tools, intelligently fixing metadata errors.
clinical-data-cleaner
Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails.
metagenomic-krona-chart
Analyze data with `metagenomic-krona-chart` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
anatomy-quiz-master
Generate interactive anatomy quizzes for medical education with multiple question types, difficulty levels, and anatomical regions. Supports gross anatomy, neuroanatomy, and clinical correlations for self-assessment and exam preparation.