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Scalable Biomedical Data Solutions: Connecting Discovery to Development
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November 20, 2024
In the day and age of precision medicine and next-generation sequencing, biomedical research faces an unprecedented explosion of data. The growing volume and complexity of biomedical data demand innovative approaches to manage and analyze it effectively, as traditional methods struggle to keep pace.
Modern biomedical research operates at the intersection of genomics, proteomics, and clinical data. With the aid of high-throughput technologies, the volume of data generated often reaches terabytes of information per dataset. For example, a single whole-genome sequencing experiment can generate over 200 gigabytes of raw data. This data is matched by its diversity, ranging from single-cell RNA sequencing and multi-omics datasets to electronic health records (EHRs). Such heterogeneity often requires a specialized approach to integrate, harmonize, and analyze data effectively. [1]
Furthermore, the rising complexity of these databases presents yet another problem. Biomedical data is not only multi-dimensional but also interdependent, requiring sophisticated methods for annotation, metadata tagging, and integration. A lack of standardized file formats and data schemas across repositories exacerbates these challenges. Elucidata is solving this problem for biomedical research and biopharmaceutical companies.
The pathway from discovery to development is rarely linear. Research organizations must navigate through siloed data sources, incompatible platforms, and fragmented workflows. These barriers disrupt the continuity of data across research phases, leading to inefficiencies in the transition from early discovery to preclinical and clinical development.
During our work with Celsius Therapeutics, we faced a challenge of this kind where inconsistent file formats and metadata gaps hindered the integration of public and proprietary single-cell data platforms. How we addressed this is discussed in the case studies section below.
Scalable biomedical data solutions are urgently required to handle exponential data growth while maintaining high-quality standards. Moreover, cloud-native platforms, data standardization protocols, and automation frameworks are needed to meet these demands. Without such systems, research organizations risk falling behind in a competitive landscape where the ability to derive insights quickly can define success.
Scalability is particularly important in environments where collaboration between cross-functional data and teams is required. Connecting data from discovery to development while ensuring reproducibility, speeding up hypothesis testing, and providing actionable insights is what the field needs.
The sophistication and magnitude of biomedical data require a robust architecture that excels at managing diverse datasets. It must include cloud-native strategies, data standardization protocols, and comprehensive security measures to manage data flow throughout research and development cycles.
Cloud-native solutions have become fundamental to managing vast biomedical datasets in modern research. Unlike traditional on-premises infrastructure, cloud platforms provide dynamic scalability, enabling organizations to process data at unparalleled scales without substantial capital investment.
At Elucidata, our platform Polly leverages its cloud infrastructure to offer scalable and secure data processing capabilities. It also facilitates collaboration among geographically dispersed research teams through centralized data repositories with real-time access. This capability proves essential for multi-institutional research initiatives that depend on continuous data exchange and processing. Additionally, shifting to cloud resources eliminates the operational burden of physical infrastructure management, allowing organizations to focus on scientific innovation.
Biomedical data requires precise standardization that includes uniform file formats, clear annotations, and consistent metadata frameworks across the data lifecycle. Neglecting these protocols risks creating data silos, operational inefficiencies, and compromised research integrity.
Here at Elucidata, we follow a step-by-step process for the harmonization of large datasets. Starting with data cleaning and using consistent processing, we convert diverse datasets into common formats and structures and add a layer of metadata annotation followed by quality assurance.
Managing biomedical data carries significant ethical and legal responsibilities, particularly regarding sensitive patient information. To maintain data integrity and privacy while meeting regulatory requirements including HIPAA, GDPR, and ISO standards, robust security protocols are required. Core security measures include advanced encryption protocols, multi-factor authentication systems, and precise access controls to prevent unauthorized access. These safeguards protect sensitive information while building trust among stakeholders, from patients to researchers and regulatory authorities.
As regulations evolve, modern systems must adapt to new compliance requirements. Therefore, at Elucidata, we keep prioritizing security and compliance to protect sensitive biomedical data and adhere to industry standards, such as:
SOC 2 Compliance: We have achieved SOC 2 compliances howing its commitment to security and enabling customers to use its services with confidence and trust.
Data Encryption: We employ AES 256 encryption for data at rest and TLS/SSL protocols for data in transit, ensuring robust protection against unauthorized access.
Regular Audits and Assessments: We conduct biannual vulnerability assessments and penetration tests by independent third parties to identify and solve potential security threats.
Compliance with Regulations: Polly aligns with key regulations such as HIPAA, ISO 27001, and GDPR.
Transition from discovery to development is critical to maximize the value of data, accelerate timelines, and improve outcomes. Effective strategies include optimizing data flow, fostering cross-functional collaboration, and leveraging real-time analytics capabilities.
The multitudes of data types generated at different stages of research require systematic approaches for management. The first step in establishing this flow is to have centralized data storage and automated data processing. This is essential to maintain data integrity and significantly reduce manual intervention and potential errors.
At Elucidata, Polly automates the ingestion, transformation, and curation of data at scale. Polly also has a cloud-native infrastructure that supports high-throughput data processing, allowing for the efficient handling of large datasets. This scalability ensures that data flows smoothly, even as the volume and complexity of data increases.
The roles of discovery and development teams are at either end of the development pipeline. While discovery teams focus on hypothesis generation and testing, development teams concentrate on validation and practical application.
To make this system more robust, the integration of diverse expertise like computational biology, clinical research, data science, and engineering is a crucial step.[2]
In addition to clear communication channels between teams, well-documented workflows can help teams track progress and address challenges promptly. We at Elucidata can design such workflows to integrate fragmented data and enable collaboration across various departments.
The decision-making in any research is guided by analytical insights. However, delayed actions can lead to longer timelines and operational difficulties. Real-time analysis can help teams respond rapidly to new data and emerging patterns.
Machine learning algorithms speed up and improve the quality of analysis by identifying patterns and predicting outcomes. This computational support helps teams process large datasets and generate insights more efficiently. To monitor these insights and project metrics in real time, interactive visualization and detailed data examination are required.
Elucidata’s approach to managing data is focused on scalable solutions and measurable success for its clients. With our proprietary platform, Polly, we have significantly improved how research groups handle complex data challenges.
Our core solution is Polly, a cloud-native, data-centric platform designed for high-throughput biomedical research. It integrates various functionalities to ensure smooth data processing, including:
Polly’s cloud-native infrastructure offers scalability, allowing research groups to adapt to growing data volumes. The key features are:
These scalability solutions allow our clients to optimize resource utilization, maintain operational flexibility, and achieve faster turnaround times.
Our innovative solutions have delivered transformative results for its clients. They have reported significant improvements in data accessibility, enabling teams to focus more on scientific discovery rather than data management.
We partnered with a women’s health startup to optimize RNA-seq pipelines, reducing the sample-to-report time by 2x and cutting processing costs by 50%. This collaboration also delivered annual cost savings of $1.6M.
In another project, we helped curate and process ~5.3 million cells across 1,900 single-cell datasets, enabling the discovery of precision medicines 4x faster than traditional methods. The challenge was sourcing high-quality single-cell datasets, integrating inconsistent public data, limited visualization tool functionality, and managing large-scale data. Using our unique ML-based, human-in-loop approach, we curated ~5.3M cells and 1,900 datasets, enriched with critical metadata via Polly. It was set in harmonized formats to ensure compatibility with the client’s pipelines. In addition, a visualization tool was built on a Python app with advanced analytics.
Elucidata stands out in the biomedical data landscape due to its unique value propositions:
The integration of scalable solutions offers a significant return on investment (ROI) for biopharma and research organizations. Since 2015, we have been driving innovation in how these organizations handle their data infrastructure, and delivering ROI and impact. Our collaborations with leading biopharma companies have demonstrated that effective data integration can significantly reduce analysis time while improving the accuracy of research outcomes.
What sets us apart in this space is our thorough understanding of both the technical challenges and biological complexities. We've witnessed how proper data architecture smoothens operations and gives scientists time and space for innovative thinking.
Our results go beyond just operational efficiency. Teams report faster hypothesis generation, more reliable reproducibility of results, and significantly reduced time-to-insight. These improvements translate directly to accelerated research timelines and more efficient use of research budgets.
The return on investment becomes particularly evident in large-scale research initiatives, where integrated data architectures have helped our partners significantly reduce data preparation time and in some projects cut computational costs by nearly half. But perhaps most importantly, these systems have enabled discoveries that might otherwise have been missed in traditional, siloed research environments.
Our goal is to do- everything that we do currently- better and faster. This includes integrating advanced AI into Polly to enhance its capabilities, enabling predictive analytics and automated insights for specific research objectives. The platform’s data harmonization tools will be expanded to support emerging data types and complex multi-modal analyses. To address the rising security concerns, we will continuously update our compliance workflows and integrate advanced encryption measures to meet global data protection standards. By leveraging modular, cloud-native architectures, we aim to ensure sustainable scalability, enabling the platform to grow alongside increasing data demands without compromising efficiency.
Visit www.elucidata.io or reach out to us at info@elucidata.io to discuss potential collaboration and research in this ever-evolving and dynamic field.
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