This application seeks funds to develop LIFE - a novel information system to seamlessly integrate diverse data set produced by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) and other screening programs. The LINCS program aims to generate an extensive reference set of cellular response signatures to a variety of small molecule and genetic perturbations. The goal is to create a sustainable and widely accessible knowledge resource to advance our understanding of the highly orchestrated interplay of molecular biological components in maintaining healthy development and how their perturbation causes disease. Data produced at LINCS span a variety of assay formats and technologies, including biochemical and single cell phenotypic responses, and genome-wide transcriptional profiling. The success of this initiative critically relies on an effective informatics solution to integrate the various (current and future) data types into coherent data sets and to make them accessible, interpretable, and actionable for scientists of different backgrounds and with different objectives. We propose to develop LIFE - a novel knowledge-based information system that will solve this challenge. Tremendous progress has been made during the last decade developing Semantic Web technologies with the goals of formalizing knowledge, linking information across different domains, and integrating heterogeneous data from diverse sources. LIFE will leverage these technologies and extend them further. LIFE will incorporate biomedical domain-level ontologies, including our recently developed BioAssay Ontology, to associate related data types and to provide a knowledge context of the LINCS assays and their outcomes. LIFE will be scalable with respect to information volume and complexity. A key novel feature of LIFE will be the potential to derive novel implicit knowledge by various inference mechanisms;similar to how humans obtain insights by (mentally) connecting different pieces of information. The overarching goal of the LIFE system is to help scientists to use data and results produced in the LINCS and other NIH screening programs in their own research and to support their translation towards the development of novel therapeutics.

Public Health Relevance

Public and private organizations are generating huge data sets as they attempt to develop new drugs for human diseases. One reason drug development is slow lies in the difficulty of generating new knowledge from the collection of all the available drug development data. We propose to develop a novel information system that will help scientists access and use information generated by the NIH LINCS project and other public data sources

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-H (55))
Program Officer
Larkin, Jennie E
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Miami School of Medicine
Schools of Medicine
Coral Gables
United States
Zip Code
Allen, Bryce K; Stathias, Vasileios; Maloof, Marie E et al. (2015) Epigenetic pathways and glioblastoma treatment: insights from signaling cascades. J Cell Biochem 116:351-63
Zander Balderud, Linda; Murray, David; Larsson, Niklas et al. (2015) Using the BioAssay Ontology for analyzing high-throughput screening data. J Biomol Screen 20:402-15
Lemmon, Vance P; Ferguson, Adam R; Popovich, Phillip G et al. (2014) Minimum information about a spinal cord injury experiment: a proposed reporting standard for spinal cord injury experiments. J Neurotrauma 31:1354-61
Vempati, Uma D; Chung, Caty; Mader, Chris et al. (2014) Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS). J Biomol Screen 19:803-16
Schurer, Stephan C; Muskal, Steven M (2013) Kinome-wide activity modeling from diverse public high-quality data sets. J Chem Inf Model 53:27-38
Vempati, Uma D; Przydzial, Magdalena J; Chung, Caty et al. (2012) Formalization, annotation and analysis of diverse drug and probe screening assay datasets using the BioAssay Ontology (BAO). PLoS One 7:e49198