Role of AI and ML in Personalized Medicine
Personalized Medicine:
Personalized medicine is a type of medical care in which treatment is customized individually for any perticular patient.
To understand personalized medicine, consider a situation where a person suffering from a chronic disease receives a standard treatment that works well for many other patients. However, in some case, the treatment does not show the expected results. To identify the reason behind this difference, the person's complete genome is sequenced and compared with reference genetic data to find the specific genetic variations that may influence treatment response. AI-based tools can then analyse the patient's genetic information, disease characteristics, and available medical data to simulate and predict the most suitable treatment options. AI can provide different possible therapies along with their predicted effectiveness or success probability. Based on this information, the most appropriate treatment approach can be selected, this is called personalized medicine.AI and ML Models are widely applied for:
Genomic Data Analysis: These tools compare DNA, RNA, or protein sequences to identify similarities, mutations, single nucleotide polymorphisms (SNPs) and genetic variations. They help researchers to find changes in genes that may be linked to diseases and compare genes between different organisms. Proteomics (Protein Structure, Interaction Prediction): These tools predict the three-dimensional (3D) structure of proteins. Understanding protein structure, predicting protein-protein interactions (PPIs), identifying novel protein functions based on sequence and structural data helps researchers to study protein functions, disease mechanisms, and design new drugs. Gene Expression Analysis: These tools analyze RNA sequencing data to measure how active different genes are. They help identify disease-related genes, understand cellular functions, and discover possible targets for new therapies. Molecular Interaction Visualization: These analysis display how different molecules interact with each other using 2D or 3D models. They help scientists to understand biological processes, drug–target interactions, and molecular mechanisms involved in diseases.Key Roles of AI and ML
Artificial Intelligence (AI) and Machine Learning (ML) are closely related, they play different roles in the development of personalized medicine.| Artificial Intelligence (AI) | Machine Learning (ML) |
|---|---|
| AI is the broader field that enables computers to perform tasks requiring human genome analysis and data generation. | ML is a subset of AI that learns patterns from data without being explicitly programmed. |
| Integrates information from multiple sources (genomics, proteomics, EHRs, medical images, wearable devices, lifestyle data) to support clinical decisions. | Analyzes large datasets to identify hidden patterns and make predictions. |
| Assists clinicians by recommending personalized diagnosis and treatment plans. | Predicts disease risk, drug response, and treatment outcomes from patient data. |
Learnings used in Drug Development
Two types of learnings were used to understand the genetic structure which further used to develop personalized medicine. Supervised Learning: Supervised learning is a machine learning method in which the computer learns from labeled data (data generated from known sample). Supervised learning in gene analysis helps the computer to learn from already identified normal and abnormal gene patterns and then classify them into the correct group. Known examples are given to the computer first. Example: If gene expression data of cancer patients and normal individuals are labeled, the model learns the differences between them. In simple terms machine learning model learns to categorize or classify whether a new gene expression of sample belongs to a normal cell group or an abnormal (disease) cell group. This is called the labelled data. The model studies the differences in gene activity between these two groups. Unsupervised Learning: Unsupervised learning is a machine learning method in which the computer learns from unlabeled data, without any predefined answers. Unlabeled data contains gene expression information of samples where the category or disease status is not known. For example, the data only contains gene patterns, but there is no label such as "normal cell" or "abnormal cell." The algorithm analyzes the data and finds hidden patterns, similarities, or groups by itself. In gene analysis, it helps to discover unknown gene patterns.Integration of Multi-Omics Data through AI-ML for Personalized Precision Medicine. :
The integration of artificial intelligence (AI) and machine learning (ML) allows bioinformatics researchers to analyse these datasets, enabling healthcare providers to develop treatments tailored according to individual’s genetic makeup. Multi-omics refers to the combined study and analysis of different types of biological data obtained from an individual, such as DNA, RNA, proteins and metabolites, to understand their overall biological condition. In AI/ML-based personalized medicine, multi-omics data are integrated and analysed using artificial intelligence and machine learning algorithms to understand disease mechanisms, identify biomarkers, predict drug responses and select appropriate treatments for individual patients. The correct sequence is:-
1. Data Generation/Collection: Biological data are obtained from different sources, such as DNA sequencing, RNA sequencing, protein analysis and metabolite analysis.
2. Data Input: These datasets are supplied to AI/ML systems.
3. Data Preprocessing: AI/ML-supported computational methods help clean, normalize and prepare the datasets.
4. Multi-Omics Integration: Different omics datasets are integrated to obtain a comprehensive biological picture.
5. AI/ML Analysis: Machine learning and deep learning algorithms identify patterns, relationships and predictions.
6. Personalized Medicine: Results support individualized treatment decisions.