바이오인포매틱스 분야 AI 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI in bioinformatics market is expected to grow from USD 10.32 billion in 2025 to USD 11.89 billion in 2026 and is forecasted to reach USD 25.21 billion by 2031 at 16.23% CAGR over 2026-2031.

This report is Segmented by Offering (Software, Services), Technology (Machine Learning, Deep Learning, and Others), Application (Drug Discovery and Development, and Others), End-User (Pharmaceutical and Biotechnology Companies, and Others), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, South America). The Market Forecasts are Provided in Terms of Value (USD).

Global AI In Bioinformatics Market Trends and Insights

AI-Led Interpretation of Multi-Omics Data at Scale

The AI in bioinformatics market is being pushed forward by the merging of genomics, transcriptomics, proteomics, and metabolomics into unified AI workflows. Multi-omics analysis was previously limited because models struggled to reconcile different statistical properties and batch effects across data types. A 2026 study in Cell Metabolism showed unified multi-omics modeling across 425,258 individuals, with strong performance in predicting aging trajectories, metabolic health, and intervention response at a scale that single-modality approaches could not reach.This is moving pharmaceutical companies toward AI-integrated multi-omics as a more routine step in target validation and reducing dependence on slower in vitro screening paths. The 2025 Flexynesis toolkit also showed how bulk multi-omics data can support precision oncology stratification, which points to earlier clinical use in cancer indication selection.The remaining challenge has shifted from data generation to semantic harmonization, because vendors now need to align inconsistent clinical phenotyping and metadata standards across large cohort datasets.

Clinical Trial Stratification and Cohort Matching

The AI in bioinformatics market is also gaining support from the need to improve patient subgroup selection in drug development. Conventional epidemiological tools often fail to isolate pharmacogenomically defined responder populations inside heterogeneous trial pools, and that weakness has contributed to Phase II failures. The FDA's April 2026 pilot program on AI in clinical trials directly addressed dose selection, safety monitoring, and early go or no-go decisions, which gives clearer regulatory support for AI bioinformatics tools used in trial design and execution.Commercial platforms are responding by combining genomic, transcriptomic, imaging, and clinical data into cohort definitions that can be used much faster than traditional workflows. This raises demand for explainable software, because AI-defined cohorts that enter regulatory submissions will need fixed model parameters, auditable logic, and clear lineage records.

Data Sovereignty Friction in Cross-Border Genomic Collaboration

The AI in bioinformatics market faces a major constraint from cross-border genomic data rules that were built on different assumptions about privacy, re-identification risk, and secondary data use. The EU's GDPR, China's Personal Information Protection Law and Human Genetic Resources Administration rules, and the U.S. NIH genomic data sharing framework do not align well in practice, which stretches governance timelines for multinational model training and validation programs. China's genetic resources rules are especially restrictive for foreign-involved research because they impose export controls and domestic localization requirements on relevant data flows. As a result, the AI in bioinformatics market is moving toward country-specific data partnership structures rather than simple global licensing arrangements. This raises development costs and extends time to market for vendors that want globally relevant training datasets.

Other drivers and restraints analyzed in the detailed report include:

1. Foundation Models for Biological Sequence and Structure Prediction
2. Federated Learning Across Hospital and Biobank Data Silos
3. Model Validation Burden Across Clinical, Research, and GxP Use Cases

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software held 59.73% of the AI in bioinformatics market share in 2025, supported by SaaS genomic interpretation platforms, cloud-native variant annotation tools, and AI-powered sequencing analysis pipelines that scale efficiently after deployment. Its position is also reinforced by switching costs created by large genomic databases and by proprietary model weights embedded within software platforms. These factors make software the most entrenched offering in the AI in bioinformatics market at present. The value proposition has been strongest where customers need repeatable analysis, faster throughput, and centralized model updates across multiple research programs.

Services are expected to be the fastest-growing sub-segment at 16.58% CAGR from 2026 to 2031, which shows that complex model deployment is pushing buyers toward external support. The AI in bioinformatics industry is moving toward bundled platform and service contracts because model customization, pipeline integration, and managed analysis are harder to standardize than software alone. Revenue is moving away from software-only models and toward recurring service-based engagement structures in the AI in bioinformatics market.

Machine learning held 44.38% share in 2025 and remained the most established technology base across sequencing pipelines, variant classification, biomarker association studies, and phenotype prediction. Supervised models such as gradient-boosted trees and random forests still matter because clinical genomics workflows often favor interpretability and calibration. Deep learning has delivered stronger relative performance in protein structure prediction, whole-slide image analysis, and single-cell tasks, which keeps it important even when not leading overall share. This mix shows that the AI in bioinformatics market still uses multiple technical approaches rather than converging on one dominant model class.

Natural language processing is expected to be the fastest-growing technology segment at 16.82% CAGR through 2031 because biomedical literature, clinical notes, and knowledge graphs are becoming active data layers in research workflows. Computer vision remains a smaller segment, but it is growing alongside AI-powered digital pathology and whole-slide imaging in clinical workflows. The AI in bioinformatics industry is therefore broadening from sequence analysis alone into text, image, and graph reasoning tasks that support more of the research and diagnostic process.

Complete Report Scope:

• By Offering
  • Software
  • Services
• By Technology
  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Other Technologies
• By Application
  • Drug Discovery and Development
  • Clinical Diagnostics and Precision Medicine
  • Biomarker Discovery and Validation
  • Multi-Omics Data Integration and Interpretation
  • Biological Network and Systems Biology Modeling
  • Laboratory Informatics and Workflow Automation
  • Other Applications
• By End-User
  • Pharmaceutical and Biotechnology Companies
  • Academic and Research Institutes
  • Hospitals and Diagnostic Laboratories
  • Other End-Users
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America accounted for 48.55% of the AI in bioinformatics market size in 2025, giving it the largest regional position. The region benefits from dense pharmaceutical R&D activity, deep venture funding, and strong NIH-backed genomics infrastructure. The United States remains the anchor of the regional AI in bioinformatics market, while Canada adds support through Genome Canada and related precision medicine activity. Access to high-end GPU infrastructure at major cloud providers also strengthens North America's cost and speed advantage for large omics workloads, and the EuroHPC MeluXina project showed that GPU-accelerated whole-genome analysis can reduce runtime from 14.6 hours to 4.7 hours with Parabricks on 3 GPU nodes.

Europe is the second-largest regional block in the AI in bioinformatics market, led by Germany, the UK, and France. In the UK, NHS England's blood-test-first cancer program and SOPHiA GENETICS' May 2026 partnership with Synnovis show how public health systems can create direct demand for AI-enabled genomic diagnostics at scale. Germany has taken a leading role in federated genomics through the German Biobank Alliance and the Genomic Data Infrastructure project, which completed a 2026 demonstration of privacy-preserving federated GWAS across multiple national nodes.

Asia-Pacific is projected to be the fastest-growing region at 18.43% CAGR from 2026 to 2031, making it the fastest-rising part of the AI in bioinformatics market. Growth is being driven by government-backed genomics programs in China, Japan, India, and South Korea and by continued investment in national health data infrastructure. A 2025 Nature study on Han Chinese ancestry showed how population-specific polygenic risk scoring can support non-European model development at large scale, which is important for region-specific precision medicine tools. China's large cohort programs and cross-ancestry research are expanding the training base for local models, while South America and the Middle East and Africa are showing earlier-stage demand through hospital partnerships and precision medicine infrastructure investment, including PathAI's 2026 Brazil collaboration.

1. Benevolent AI
2. Congenica Ltd.
3. Deep Genomics Inc.
4. DNAnexus, Inc.
5. Fabric Genomics, Inc.
6. GeneDx, Inc.
7. Genialis, Inc.
8. Genoox Ltd.
9. IBM
10. Illumina
11. Insilico Medicine
12. NVIDIA
13. Owkin, Inc.
14. Oxford Nanopore Technologies plc
15. PathAI, Inc.
16. QIAGEN
17. SOPHiA GENETICS SA
18. Tempus AI, Inc.
19. Thermo Fisher Scientific
20. Velsera, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 AI-Led Interpretation of Multi-Omics Data at Scale
  • 4.2.2 Clinical Trial Stratification and Cohort Matching
  • 4.2.3 Foundation Models for Biological Sequence and Structure Prediction
  • 4.2.4 Federated Learning Across Hospital and Biobank Data Silos
  • 4.2.5 Closed-Loop Wet Lab and In Silico Workflow Automation
  • 4.2.6 Cross-Ancestry Model Expansion for Population-Scale Genomics
• 4.3 Market Restraints
  • 4.3.1 Data Sovereignty Friction in Cross-Border Genomic Collaboration
  • 4.3.2 Model Validation Burden Across Clinical, Research, and GxP Use Cases
  • 4.3.3 GPU and High-Performance Compute Constraints for Large Omics Pipelines
  • 4.3.4 Limited Labeled Biological Datasets for Rare Variant and Rare Disease Use Cases
• 4.4 Supply/Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 Market Size & Growth Forecasts (Value)

• 5.1 By Offering
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Technology
  • 5.2.1 Machine Learning
  • 5.2.2 Deep Learning
  • 5.2.3 Natural Language Processing
  • 5.2.4 Computer Vision
  • 5.2.5 Other Technologies
• 5.3 By Application
  • 5.3.1 Drug Discovery and Development
  • 5.3.2 Clinical Diagnostics and Precision Medicine
  • 5.3.3 Biomarker Discovery and Validation
  • 5.3.4 Multi-Omics Data Integration and Interpretation
  • 5.3.5 Biological Network and Systems Biology Modeling
  • 5.3.6 Laboratory Informatics and Workflow Automation
  • 5.3.7 Other Applications
• 5.4 By End-User
  • 5.4.1 Pharmaceutical and Biotechnology Companies
  • 5.4.2 Academic and Research Institutes
  • 5.4.3 Hospitals and Diagnostic Laboratories
  • 5.4.4 Other End-Users
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Spain
    • 5.5.2.6 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 India
    • 5.5.3.4 Australia
    • 5.5.3.5 South Korea
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 Middle East and Africa
    • 5.5.4.1 GCC
    • 5.5.4.2 South Africa
    • 5.5.4.3 Rest of Middle East and Africa
  • 5.5.5 South America
    • 5.5.5.1 Brazil
    • 5.5.5.2 Argentina
    • 5.5.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles {(includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)}
  • 6.3.1 BenevolentAI
  • 6.3.2 Congenica Ltd.
  • 6.3.3 Deep Genomics Inc.
  • 6.3.4 DNAnexus, Inc.
  • 6.3.5 Fabric Genomics, Inc.
  • 6.3.6 GeneDx, Inc.
  • 6.3.7 Genialis, Inc.
  • 6.3.8 Genoox Ltd.
  • 6.3.9 IBM Corporation
  • 6.3.10 Illumina, Inc.
  • 6.3.11 Insilico Medicine, Inc.
  • 6.3.12 NVIDIA Corporation
  • 6.3.13 Owkin, Inc.
  • 6.3.14 Oxford Nanopore Technologies plc
  • 6.3.15 PathAI, Inc.
  • 6.3.16 QIAGEN N.V.
  • 6.3.17 SOPHiA GENETICS SA
  • 6.3.18 Tempus AI, Inc.
  • 6.3.19 Thermo Fisher Scientific Inc.
  • 6.3.20 Velsera, Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-space & unmet-need assessment