knowledge_base/publications_detailed.md

# Detailed Publications and Research Contributions 

## BioFusionNet (2024)
**Full Title**: "BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion"

**Journal**: IEEE Journal of Biomedical and Health Informatics

**Key Contributions**:
- Novel multimodal fusion architecture combining histopathology, genomics, and clinical data
- Attention-based feature selection for interpretability
- Superior performance compared to existing methods
- Clinical validation on large patient cohorts

**Technical Details**:
- Uses ResNet-based feature extraction for histopathology images
- Implements cross-attention mechanisms for data fusion
- Employs survival analysis with Cox proportional hazards
- Achieves C-index of 0.78 on validation datasets

**Impact**: This work provides clinicians with a comprehensive tool for patient risk assessment, enabling personalized treatment planning.
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**GitHub**: [raktim-mondol/BioFusionNet](https://github.com/raktim-mondol/BioFusionNet)

## hist2RNA (2023)
**Full Title**: "hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images"

**Journal**: Cancers

**Key Contributions**:
- Direct prediction of gene expression from tissue images
- Efficient architecture suitable for clinical deployment
- Identification of morphology-gene expression relationships
- Validation across multiple cancer datasets

**Technical Details**:
- Custom CNN architecture optimized for gene expression prediction
- Multi-task learning framework
- Attention mechanisms for spatial feature importance
- Correlation analysis with known biological pathways

**Impact**: Enables gene expression profiling without expensive molecular assays, making personalized medicine more accessible.
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**GitHub**: [raktim-mondol/hist2RNA](https://github.com/raktim-mondol/hist2RNA)

## AFExNet (2021)
**Full Title**: "AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-types and Extracting Biologically Relevant Genes"

**Journal**: IEEE/ACM Transactions on Computational Biology and Bioinformatics

**Key Contributions**:
- Adversarial training for robust feature learning
- Automatic biomarker discovery
- Cancer subtype classification
- Biologically interpretable features

**Technical Details**:
- Adversarial autoencoder architecture
- Gene selection based on reconstruction importance
- Validation on TCGA datasets
- Pathway enrichment analysis

**Impact**: Provides insights into cancer biology while achieving high classification accuracy.
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**GitHub**: [raktim-mondol/breast-cancer-sub-types](https://github.com/raktim-mondol/breast-cancer-sub-types)

## Ongoing Research

### Multimodal Foundation Models
- Developing foundation models for medical imaging
- Pre-training on large-scale medical datasets
- Transfer learning for rare diseases

### Ongoing Research
- Large Language Models (LLMs)  
- Retrieval-Augmented Generation (RAG)  
- Fine-tuning and domain adaptation  


### AI Ethics in Healthcare
- Bias detection and mitigation
- Fairness in medical AI
- Regulatory compliance frameworks