Verified recordHigher Education

James Stevens

Adjunct Associate Professor at Leiden University

Based in Apex, United States

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Seniority

Staff

Department

Education

Location

Apex

Industry

Higher Education

Company size

8.3K

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j•••••••@••••••.com

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Background

About James Stevens

LEADERSHIP EXPERIENCE: Over 35 years of leadership experience in safety science, research and technology development in government, academia and biopharmaceutical companies. Specific areas of expertise include: Drug discovery and development: Science driven safety decisions including target safety, hit to lead scaffold selection and lead optimization. Drug discovery portfolio management from target entry to candidate selection Science and technology strategy: Safety strategy and implementation, workflow integration for novel technologies, organizational change management. Experienced with implementation of cell-based screens, systems biology and genomics applications and mechanisms of nonclinical pathogenesis. Investigative and predictive toxicology: Implementation of cell and computational safety models, biomarkers, and genomic applications. Mechanisms of adverse drug reactions and molecular stress responses. Public Private Partnerships: Technology transfer and small company start-ups. Managing cooperative academic and industrial technology partnerships RESEARCH INTERESTS: Systems biology and drug safety research. My primary research interest is in quantitative systems approaches to drug target and drug molecule safety assessment. Although systems approaches have the potential to revolutionize drug safety assessment, realizing that potential requires integration of high content data, e.g. cheminformatic, genomic, proteomic and metabonomic data, while reducing complexity (dimensionality) with the right analytics to reveal meaningful biological information. Unsupervised network-based approaches, such as weighted gene co-expression network analysis (WGCNA), reduce the dimensionality of complex gene expression datasets and organize expression data into statistically robust networks of genes that translates vertically (in vitro to in vivo) and horizontally (across species). Thus, unsupervised network-based approaches help reduce complex and reveal relevant and interpretable mechanisms of pathogenesis.

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