Challenges and solutions in AI-bioinformatics integration: Focus on data quality and standardization

Introduction

The integration of artificial intelligence (AI) into bioinformatics is transforming the pharmaceutical and biotech industries. AI enables the processing and analysis of vast, complex datasets, unlocking new possibilities in drug discovery, genomics and personalised medicine. However, challenges such as data quality and standardisation continue to hinder seamless adoption. This blog explores these challenges and presents actionable solutions, offering a balanced perspective for industry experts and potential pharmaceutical clients.

The importance of data quality and standardisation

Bioinformatics data serves as the foundation for AI-driven insights, whether in identifying drug targets, analysing genetic variants or understanding disease pathways. The accuracy of these insights depends on the quality and consistency of input data. High-quality, standardised data ensures the reproducibility and reliability of AI models, whereas poor data quality leads to erroneous predictions, wasted resources and increased costs.

Similarly, the lack of standardisation across datasets hinders the integration of information from diverse sources. For example, a clinical dataset using ICD-10 codes may not align with omics data in a different format, creating obstacles to holistic analysis.

DeepVariant in variant calling

AI has significantly enhanced the field of genomics. Tools like DeepVariant, developed by Google, have revolutionised variant calling, which is the process of identifying genetic variations in a DNA sample by comparing it to a reference genome, by leveraging convolutional neural networks. Unlike traditional methods, such as GATK, which rely on predefined heuristics, DeepVariant analyses sequencing data directly, distinguishing true genetic variants from noise. This innovation improves accuracy in population-scale genomic studies, such as the UK Biobank, highlighting the critical role of high-quality data in genomics.

Challenges in AI-bioinformatics integration

Data heterogeneity

Bioinformatics involves diverse data types, including genomic, transcriptomic, proteomic and clinical data. These datasets are stored in various formats:

• Genomic data (e.g., BCL, FASTQ, BAM or VCF formats)
• Transcriptomic data (e.g., FPKM or TPM expression matrices)
• Clinical data (e.g., HL7, FHIR standards)

Integrating such heterogeneous data presents technical hurdles. For instance, in multiomics studies, researchers must combine proteomic and genomic data to identify biomarkers. AI tools like Multi-Omics Factor Analysis Plus (MOFA+) streamline this process by identifying shared patterns across datasets, enabling researchers to extract meaningful biological insights.

High dimensionality and sparsity

Omics datasets, such as single-cell RNA sequencing (scRNA-seq), contain thousands of features (e.g., genes) but relatively few samples. This high-dimensional, sparse data complicates machine learning by increasing the risk of overfitting.

Tools like single-cell variational inference (scVI) address these challenges by using deep generative models. By denoising and imputing missing values, scVI improves cell clustering and functional annotation, enabling researchers to better understand cellular heterogeneity in diseases such as cancer or autoimmune disorders.

Standardised pipelines and workflows

Variability in bioinformatics workflows poses a challenge. For instance:

• Different tools for genome assembly (e.g., BWA, Bowtie2) can yield varying results
• RNA-seq preprocessing may introduce biases based on alignment methods or quantification tools

Platforms like Snakemake and Nextflow have emerged to address these issues. These tools ensure that workflows are reproducible, scalable and standardised, making it easier for researchers to handle large datasets.

Interpretability of AI models

Many AI models, particularly deep learning systems, function as “black boxes,” making it difficult for researchers to interpret results. This lack of transparency can hinder adoption in critical applications like clinical diagnostics.

Regulatory and security concerns

AI systems in bioinformatics must handle sensitive patient data while complying with regulations like GDPR and HIPAA. Privacy-preserving methods, such as federated learning, are emerging as solutions to securely train AI models on distributed datasets without sharing raw data.

Solutions and best practices

Improving data quality through AI

Ensuring high-quality data is crucial. AI-driven tools like DeepVariant have demonstrated how machine learning can correct sequencing errors and improve variant calling. Similarly, tools like Trimmomatic and FastQC help clean raw genomic data, ensuring reliable downstream analysis.

Standardised pipelines

Reproducible workflows enable seamless collaboration across teams. For example, pharmaceutical companies use Nextflow to integrate multiple bioinformatics tools, such as GATK for variant calling and STAR for RNA-seq alignment. These workflows reduce inconsistencies and save valuable time.

AI for protein structure prediction

Protein structure prediction, a longstanding challenge in bioinformatics, was revolutionised by AlphaFold, developed by DeepMind. Using deep learning, AlphaFold predicts protein structures with near-experimental accuracy. Pharmaceutical companies now use AlphaFold’s predictions to design drugs targeting diseases like Alzheimer’s and cancer. This underscores how standardised, high-quality data can drive breakthroughs in AI applications.

Dimensionality reduction in high-dimensional data

Dimensionality reduction techniques like t-SNE and UMAP are commonly used in single-cell transcriptomics to visualise complex datasets. AI-enhanced methods, such as those employed by scVI, further improve data representation by denoising sparse, high-dimensional data.

Explainable AI for trustworthy insights

Frameworks like SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) are gaining traction in bioinformatics. By explaining how models make predictions, these tools build trust among researchers and clinicians, facilitating the adoption of AI in clinical settings.

Emerging trends in AI-bioinformatics integration

To better understand the evolving landscape of AI in bioinformatics, here are some key emerging trends shaping the future of data-driven biological research:

Multiomics integration

Multiomics integration is revolutionising bioinformatics by combining diverse biological datasets to uncover deeper insights into health and disease. This approach enables precision medicine by aligning genomics, proteomics, and clinical data through advanced AI-driven platforms and tools.

Multiomics integration enables researchers to combine diverse biological data types—such as genomics, transcriptomics, proteomics and metabolomics—to uncover complex disease mechanisms. Tools like DIABLO and MOFA+ help identify meaningful patterns across datasets, improving biomarker discovery and disease classification. Recent benchmarking studies have highlighted the effectiveness of methods like Seurat v4 WNN and totalVI in single-cell multimodal analysis. These approaches address challenges like data heterogeneity and dimensionality, offering a more holistic view of biological systems. In diseases like chronic kidney disease, such integration provides deeper mechanistic insights that support precision medicine and targeted therapeutic development.

Cognizant helped a life sciences client streamline petabyte-scale omics data using Azure’s cloud-native architecture. By assessing existing pipelines and designing a unified lake house, the solution enabled secure, compliant and scalable analytics. This accelerated AI-driven insights and laid the foundation for precision medicine.

Cognizant’s approach to multiomics integration

Cognizant is actively advancing healthcare analytics through multiomics integration, particularly in chronic kidney disease research. By combining electronic health records with omics data using ontology-based mapping and privacy-preserving AI frameworks, Cognizant enables precise disease prediction and patient stratification. Tools like DIABLO and MOFA+ help extract meaningful features and identify disease subtypes, while federated learning ensures secure, regulation-compliant data sharing.

To support these efforts, Cognizant leverages AWS and Azure for scalable, secure infrastructure. AWS facilitates global data migration and AI-driven analytics, while Azure powers cloud modernization for health plans. Cognizant is partnering with Microsoft to offer multiomics data platform assessment Cognizant® Multiomics Data Platform Assessment—Microsoft Azure Marketplace. This will enable collaborative offerings with Microsoft’s Health and Life Sciences (HLS) team, driven by the growing demand they’re seeing across the HLS ecosystem. Additionally, Google Cloud supports Cognizant’s multiomics initiatives by providing scalable AI and cloud infrastructure through its Multiomics Suite and Vertex AI pipelines. These tools enable efficient ingestion, processing and analysis of genomic and clinical data, accelerating precision medicine and drug discovery. Together, these platforms enable enterprise-grade bioinformatics solutions that drive innovation in precision medicine.

Conclusion

Integrating AI into bioinformatics offers transformative opportunities while posing significant challenges. By addressing data quality, standardisation and scalability, the pharmaceutical industry can unlock AI’s full potential. Adopting standardised pipelines, leveraging cutting-edge tools and ensuring regulatory compliance are essential steps toward success.

For industry leaders and clients, navigating these challenges is crucial to harness the power of AI-bioinformatics integration. Together, we can pave the way for a future of improved healthcare and groundbreaking scientific discoveries.