Decoding LARS at the Cellular Level with AI

LARS doesn’t just affect digestion—it fundamentally alters gut epithelial cells, leading to chronic inflammation, unpredictable bowel function, and reduced microbiome diversity. However, until now, understanding these cellular-level changes has been a challenge.

At SANJEEVANI AI, we are bridging this gap by applying AI-driven analysis to bulk microbiome and transcriptomic data, using deconvolution techniques to approximate single-cell insights. This approach helps us track how microbiome shifts correlate with LARS symptoms and develop precision-based recommendations for survivors.

Step 1: Data Collection & Preparation

To accurately model LARS at the cellular level, we require high-quality bulk microbiome and transcriptomic (RNA-seq) datasets from colorectal cancer (CRC) survivors, LARS patients, and gut health studies.

Where We Source Data

• Public Metagenomic Repositories (MGnify, HMP, TCGA-CRC, ENA)

• Stool Microbiome Sequencing Studies (Metagenomic shotgun sequencing)

• 16S rRNA Sequencing Datasets (Bacterial community profiling)

• GEO/SRA Datasets from CRC and LARS-related studies

  
  

Types of Data We Analyze

• Microbiome Composition Profiles (Relative abundance of gut bacteria)

• Metagenomic Functional Analysis (Predict metabolic pathways using HUMAnN2)

• Bulk RNA-seq from Gut Mucosa Samples (To capture host-microbiome interactions)

Step 2: Preprocessing the Data

Before applying AI-powered deconvolution techniques, we standardize and structure the dataset.

🔹 For Metagenomic Data

• Normalize microbial abundance data (RPKM, TPM)✔ Map microbial taxa to reference cell types✔ Convert microbial gene expression data to CIBERSORT-compatible formats

🔹 For Bulk RNA-seq (Host Gene Expression Data)

• Align reads & quantify gene expression (Salmon, STAR, Kallisto)✔ Filter low-expression genes✔ Transform data into log-transformed TPM or FPKM for AI modeling

  
  

Step 3: AI-Powered Deconvolution Techniques

We use single-cell deconvolution methods to estimate immune and epithelial cell proportions from bulk RNA-seq and microbiome data.

🔹 CIBERSORT (for Bulk RNA-seq Analysis)

• Uses a signature gene matrix (LM22) to estimate immune cell composition in LARS patients.

• Output: How immune system responses correlate with LARS severity.

  
  

🔹 CIBERSORTx (Advanced Version)

• Uses single-cell RNA-seq as a reference to infer cell-type-specific gene expression from bulk data.

• Output: A single-cell-like view of gut inflammation and microbiome-host interactions.

  
  

🔹 MuSiC (Multi-Subject Single-Cell Deconvolution)

• Infers cell proportions from bulk RNA-seq using pre-existing single-cell RNA-seq references.

• Output: More precise LARS-related cell-type estimations.

  
  

🔹 SPOTlight (Spatial Mapping of Microbiome-Host Interactions)

• Helps visualize how gut microbiome changes impact colon epithelial cells.

• Output: Spatial mapping of microbiome shifts in LARS patients.

  
  

Step 4: Integrating AI for Predictive Insights

SANJEEVANI AI transforms deconvolved cellular insights into real-time symptom predictions using machine learning.

Step 1: Training AI Models

• Input: Deconvolved cell-type data + microbiome shifts + patient-reported symptom logs

• Target: Predict worsening/improving LARS symptoms

• Model: Random Forest, Gradient Boosting, Transformers

  
  

Step 2: Personalized AI Recommendations

• AI analyzes gut microbiome shifts & immune responses

• AI suggests personalized diet, probiotic interventions, and hydration plans

  
  

Step 3: Continuous Learning

• AI adapts dynamically as users log new symptoms

• AI improves its predictions over time based on patient-reported outcomes

  
  

Step 5: Deploying in SANJEEVANI AI App

• Real-Time Symptom Tracking:Users log symptoms → AI predicts patterns based on gut microbiome shifts

• Personalized AI-Driven Recommendations:✔ Diet suggestions based on microbiome diversity & predicted flare-ups✔ Hydration & fiber intake adjustments for symptom control✔ Mental health correlations using sentiment analysis

• AI-Powered Predictive Alerts:

  • “Your microbiome shifts indicate a possible symptom flare-up. Adjust fiber intake.”

  • “Your gut microbiome is stabilizing! Continue your dietary routine.”

    
    

The Future of AI-Driven LARS Care

By integrating microbiome and transcriptomic insights with AI-driven analysis, SANJEEVANI AI is bridging the data gap in LARS management.

We are not just tracking symptoms—we are understanding them at the cellular level to build precision AI models that provide real, actionable solutions.

📩 Reach out at support@sanjeevaniai.com to collaborate or contribute