Sciome LLC is an innovative research and technology consulting company providing state of the art informatics solutions for the life sciences industry. We specialize in developing and deploying novel bioinformatics, cheminformatics, text-mining and data science solutions for researchers in government, academic and private sector organizations.
High throughput technologies are routinely used to study genomes, proteomes and metabolomes creating a deluge of scientific data. In addition, large volumes of data are available via electronic health records, clinical study reports, government reports, high content images, and the ever increasing amount of scientific literature in peer reviewed journals and on the World Wide Web. Success of scientific endeavors in such data rich environments greatly depends on the careful exploration and interpretation of this data. This has created a large demand for sophisticated tools and highly skilled expertise to enable data-driven discovery.
Our goal is to provide cutting edge, customized and cost effective informatics solutions for efficient data management, data analyses and interpretation. We develop novel quantitative methodologies, design and deliver intuitive software tools, and deploy efficient computational techniques to enable timely and insightful discoveries.
We use comprehensive and cutting edge quantitative methods paired with efficient informatics techniques to solve complex scientific challenges. We work with our clients at all stages of the life science discovery process, starting with hypotheses generation and experiment design, through data analysis using off-the-shelf and custom designed methodologies and culminating with expert help in interpretation of the results. We also provide detailed support for manuscript preparation, formulation of reports, and aid in the dissemination of information using print or online medium for projects that cover a variety of scientific disciplines. Our consulting team includes PhD level Bioinformatics Scientists, Statisticians, Cheminformaticians, Computational Toxicologists, Health Scientists and Computer Scientists with a wide array of experience in government contracting, academic research and the pharmaceutical industry.
We are a light hearted, hardworking, multi-disciplinary team of scientists who enjoy diverse technical challenges and love to work in a team. We share a passion for varied scientific disciplines and we love to learn, experiment and innovate.
We get hired to solve complex, unique and/or recurring scientific challenges. Our consulting engagements range from multiyear research endeavors in support of environmental or health related topics to short term data mining projects in support of specific scientific pursuits. We continue to serve a variety of US Federal Government agencies, pharmaceutical and biotechnology companies, non-profit research organizations and academic institutes.
SWIFT-Active Screener is a web-based, collaborative systematic review software application. Active Screener was designed to be easy-to-use, incorporating a simple, but powerful, graphical user interface with rich project status updates.
BMDExpress was created for the purpose of providing an easy workflow to transform toxicogenomic data into BenchMark Doses for aggregated collections of genes; KEGG pathways, GO terms, or user defined categories.
SWIFT-Review (SWIFT is an acronym for “Sciome Workbench for Interactive computer-Facilitated Text-mining”) is a freely available interactive workbench which provides numerous tools to assist with problem formulation and literature prioritization.
Comprehensive analysis of TempO-Seq data using novel, validated and robust bioinformatics methods. Upload raw Tempo-Seq data and instantaneously obtain in-depth analysis including cluster plots, principal component plots, differentially expressed gene list, and pathway analysis.
The Integrated Chemical Environment (ICE) provides high-quality, curated data from the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) and its partners as well as other data resources and tools to support development of new approaches for assessing chemical safety.
OrbiTox presents Big Data in an intuitive, immersive, and fluid 3D environment. It is a one-of-a-kind computational translational discovery platform for interactive, 3D visualization of large (~million objects), high-dimensional (~1000 features), multidomain toxicology and drug discovery data.
Sciome employs a highly talented team of software engineers and scientific programmers. This development team works closely with Sciome scientists, mathematicians and statisticians to design and implement interactive software solutions that solve important problems on behalf of our clients.
Schmidt, L., Finnerty Mutlu, A.N., Elmore, R., Olorisade, B.K., Thomas, J. & Higgins, J.P.T. (2025). Data extraction methods for systematic review (semi)automation: Update of a living systematic review [version 3; peer review: 3 approved]. F1000Research 10:401.
Nodzenski, M., Shi, M., Umbach, D.M., Kidd, B., Petty, T. & Weinberg, C.R. (2025) A method for finding epistatic effects of maternal and fetal variants. Frontiers in Genetics 16:1420641.
Mauge-Lewis, K.A., Ramaiahgari, S.C., Auerbach, S.S., Roberts, G.K., Waidyanatha, S., Fenton, S.E., Phadke, D.P., Balik-Meisner, M.R., Tandon, A., Mav, D., Howard, B., Shah, R., Sparrow, B., Gorospe, J. & Ferguson, S.S. (2025). Unraveling Human Hepatocellular Responses to PFAS and Aqueous Film-Forming Foams (AFFFs) for Molecular Hazard Prioritization and In Vivo Translation. Environmental Science & Technology 59(5):2423-2435.
Starnes, H.M., Green, A.J., Reif, D.M. & Belcher, S.M. (2025). An in vitro and machine learning framework for quantifying serum albumin binding of per-and polyfluoroalkyl substances. Toxicological Sciences 203(1):67-78.
Pannala, V.R., Hari, A., AbdulHameed, M.D.M., Balik-Meisner, M.R., Mav, D., Phadke, D.P., Scholl, E.H., Shah, R.R., Auerbach, S.S. & Wallqvist, A. (2025). Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model. Toxicological Sciences 2025, 1-15.
Green, A.J., Truong, L., Thunga, P., Leong, C., Hancock, M., Tanguay, R.L. & Reif, D.M. (2024). Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies. PLOS Computational Biology 20(9): e1012423.
We are excited to be back at the annual meeting of SOT this year (Booth 521), which is being held in Orlando March 16-20, 2025. This year, we will be showcasing our work in the following platform and poster presentations: Title: Advancing Computer Vision in High-Throughput Larvae Zebrafish Developmental Morphological
We are excited to be back at the annual meeting of ASCCT this year, which is being held in Research Triangle Park, NC between October 28-30, 2024. This year, we will be showcasing our work in the following presentations: Title: Nucleus and Cell Quantification of 3D Cell Painting Liver Spheroids
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Research Triangle Park, NC 27709, USA
919-338-2177
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Durham, NC 27713, USA
919-338-2177