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Computational Biology Service

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Drug discovery is a complex process with many steps. It begins by finding the right targets and identifying groups of patients who will respond well to treatments. Each step has its own challenges and requires specific skills and solutions. At Excelra, our Computational Biology (CB) team blends technical skills with a solid understanding of biology. This allows us to offer clear and helpful insights at every stage of your research. We assist with finding targets, growing portfolios, data analysis, and developing on the semantic web.

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Machine Learning Applications

We use AI to give important insights. It begins with looking for genomic biomarkers. We also transform unstructured data using Large Language Models (LLMs).

  • Explainable AI (XAI): Our clear methods help you see how AI makes predictions. This builds trust and confidence.

Molecular Modeling and Simulations

Our computational chemists use clever techniques. They do virtual screening, scaffold hopping, and molecular dynamics simulations. These methods help us find good candidates, improve compounds, and cut down on mistakes. By using GPU-powered tools, we provide fast and reliable results. This supports your discovery programs.

ETL Pipelines for Efficient Data Processing

Our team makes strong data pipelines. We use them to clean, combine, and change biological data from various file types and platforms. Our skills in cloud solutions help us create a smooth process for adding and mixing data.

Custom Data Analysis Pipelines

We concentrate on making full workflows for NGS data, DEL selection, and other tasks. Our pipelines are flexible and can grow with your needs. They meet industry standards and are built for great performance on platforms such as Google Cloud, Microsoft Azure, and AWS.

Interactive Dashboards and Visualizations

Data-driven decisions require clear information. Our dashboard engineers design easy-to-use and adaptable visualizations with tools such as Power BI, Tableau, and R-Shiny. These tools allow you to spot trends, recognize patterns, and make decisions faster and with confidence.

Semantic Web and Ontology Management

  • Knowledge Graphs: We create custom knowledge graphs. These graphs connect different biomedical data. This helps us find new insights.
  • Ontology Support: Our team develops and manages specific domain ontologies. This keeps everything consistent and makes it easy to integrate across platforms.
 

1. Omics Data Analysis and Biomarker Identification

Comprehensive Omics Data Services

  • Creating and Using Data Assets: We use the newest NGS technologies to gather, join, and change different omics data. This includes transcriptomics and proteomics. It helps us give important insights for drug discovery. Our team makes sure your data is ready for key decisions, from finding targets to developing models to adjust doses.
  • Finding Biomarkers: Our team looks at big public and private datasets to find biomarkers linked to diseases and drugs. We use smart data analysis. We explore pathways and build computer models to help you see how things work. This helps create ideas that lead to success.
 

Data Management and FAIRification

We make sure your data is correct, secure, and simple to reach. We follow the FAIR guidelines. This means your data is Findable, Accessible, Interoperable, and Reusable..

Key Features:

  • Standardizing and managing data
  • Managing metadata