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Updated: Aug 6, 2026
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
AI-Driven Multi-Omics Integrated Applications Using Diverse Neural Networks for Breast Cancer Diagnostic Screening
Vandana Kumari1, Harshita Tiwari1, Swati Singh1
1Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Medicinal Research Reviews
|July 23, 2026
Summary
Artificial intelligence (AI) and multi-omics data integration are revolutionizing breast cancer (BC) diagnosis and treatment. These advanced approaches enhance biomarker discovery, patient stratification, and therapeutic response prediction for improved precision medicine.
Area of Science:
- Oncology
- Bioinformatics
- Artificial Intelligence
Background:
- Breast cancer (BC) is a complex, heterogeneous malignancy and the leading cancer in women globally.
- Current diagnostic and therapeutic strategies for BC face challenges, limiting treatment efficacy.
- Omics technologies (genomics, transcriptomics, proteomics, metabolomics) offer molecular profiling for BC diagnosis.
Purpose of the Study:
- To review recent advancements in AI-driven multi-omics approaches for breast cancer biomarker discovery.
- To highlight the integration of omics data with AI techniques (ML, DL) for cancer research.
- To discuss AI's role in BC screening, diagnosis, patient stratification, and treatment response prediction.
Main Methods:
- Integration of multi-omics data (metabolomics, proteomics, transcriptomics, genomics) with AI.
- Application of machine learning (ML) and deep learning (DL) for molecular profiling and diagnostic imaging.
- Analysis of AI models for tumor grading, classification, and prognostic prediction.
Main Results:
- AI and multi-omics integration enable multidimensional approaches for personalized BC diagnosis and treatment.
- AI facilitates accurate and early BC diagnosis through multimodal data integration.
- AI models are effective in patient stratification, biomarker discovery, and predicting therapeutic response.
Conclusions:
- AI-driven multi-omics approaches are crucial for advancing breast cancer theranostics.
- Addressing challenges like data heterogeneity and model interpretability is key for future progress.
- The integration of AI and multi-omics holds promise for revolutionizing precision medicine and improving patient outcomes in BC.
