Computational Biology

Covance provides high-performance scientific computing and analysis support throughout the drug development pipeline, from early stage preclinical lead optimization to late stage clinical development. Covance computational biologists design and apply cutting edge computational techniques and work collaboratively with client research scientists to mine, integrate, analyze and interpret complex biological data from a wide variety of sources, including high-throughput genomic or proteomic platforms, associated clinical data and relevant literature.


  • Computational method design and application for biomarker identification and/or molecular signature discovery
  • Use of orthogonal data for candidate biomarker identification, confirmation and/or prioritization
  • Drug/target/gene expression relationship modeling
  • Marker/phenotype association analysis
  • miRNA and/or gene expression linkage to clinical endpoints

“Omics” data analysis

  • Gene expression analysis
  • miRNA expression analysis
  • Proteomics data analysis
  • Next Generation Sequencing (NGS) data analysis
  • SNP genotyping analysis
  • Array CGH analysis
  • Alternative splicing analysis

Pathway analysis

  • Data mining to identify molecular targets
  • Pathway data management and reporting
  • Biological interpretation and hypothesis generation

Indications discovery

  • Drug/target relationship modeling in current/novel indications

Data mining and visualization

  • Data clustering, classification, regression and rule learning
  • Literature text mining, pattern matching and extraction
  • Statistical approaches to data analysis
  • Heatmaps, networks, histograms, scatterplots, boxplots


  • Data warehousing
  • Data integration
  • Cross-species data mapping

Covance Data Visualization

Heat map
Functional analysis
Interaction network
Heat map
Disease network