The paper has data generated by NIH and the EPA coauthors provided input into the preparation of the manuscript. This dataset is not publicly accessible because: Data was not collected in EPA labs or paid for by EPA. It can be accessed through the following means: Data generated by NIH. Format: N/A. This dataset is associated with the following publication: Lynch, C., S. Sakamuru, R. Huang, D.A. Stavea, L. Varticovski, G.L. Hagar, R.S. Judson, K.A. Houck, N.C. Kleinstreuer, W. Casey, R.S. Paules, A. Simeonov, and M. Xia. (Toxicology) Identifying Environmental Chemicals as Agonists of the Androgen Receptor by Applying a Quantitative High-throughput Screening Platform. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 385: 48-58, (2017).

Background Methods for extracting useful information from the datasets produced by microarray experiments are at present of much interest. Here we present new methods for finding gene sets that are well suited for distinguishing experiment classes, such as healthy versus diseased tissues. Our methods are based on evaluating genes in pairs and evaluating how well a pair in combination distinguishes two experiment classes. We tested the ability of our pair-based methods to select gene sets that generalize the differences between experiment classes and compared the performance relative to two standard methods. To assess the ability to generalize class differences, we studied how well the gene sets we select are suited for learning a classifier. Results We show that the gene sets selected by our methods outperform the standard methods, in some cases by a large margin, in terms of cross-validation prediction accuracy of the learned classifier. We show that on two public datasets, accurate diagnoses can be made using only 15-30 genes. Our results have implications for how to select marker genes and how many gene measurements are needed for diagnostic purposes. Conclusion When looking for differential expression between experiment classes, it may not be sufficient to look at each gene in a separate universe. Evaluating combinations of genes reveals interesting information that will not be discovered otherwise. Our results show that class prediction can be improved by taking advantage of this extra information.

We present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. This dataset is associated with the following publication: Watford, S., R. Grashow, V. De La Rosa, R. Rudel, K. Paul-Friedman, and M. Martin. Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: Use case in breast carcinogenesis. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 46-57, (2018).

This file contains the data on apoptosis, neurite outgrowth, synaptogenesis and proliferation. This dataset is associated with the following publication: Harrill, J., T. Freudenrich, K. Wallace, K. Ball, T. Shafer, and W. Mundy. Testing for developmental neurotoxicity using a battery of in vitro assays for key cellular events in neurodevelopment. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 354(1): 24-39, (2018).

Background: We propose that a computerized, internet-based graphical description language for systems biology will be essential for describing, archiving and analyzing complex problems of biological function in health and disease. Results: We outline here a conceptual basis for designing such a language and describe BioD, a prototype language that we have used to explore the utility and feasibility of this approach to functional biology. Using example models, we demonstrate that a rather limited lexicon of icons and arrows suffices to describe complex cell-biological systems as discrete models that can be posted and linked on the internet. Conclusions: Given available computer and internet technology, BioD may be implemented as an extensible, multidisciplinary language that can be used to archive functional systems knowledge and be extended to support both qualitative and quantitative functional analysis.

국립낙동강생물자원관은 생물다양성 연구 성과의 확산과 과학 기반 정책 수립 지원을 위해 자체 수행한 연구 결과를 연구보고서로 발간하고 있으며, 이에 따라 관련 연구보고서 정보를 공공데이터로 개방하고 있습니다.개방 데이터는 연도, 과제명, 저자, 발간등록번호 등으로 구성되어 있습니다.해당 데이터는 학계·연구기관에서 생물다양성 분야의 연구 동향 분석, 주제별 연구 축적 현황 파악, 협력 연구 기획 등에 활용될 수 있으며, 정책기관이나 교육기관은 주요 연구성과를 바탕으로 정책 개발, 교육 콘텐츠 구성, 관련 연구자 매칭 등에 활용할 수 있습니다.또한, 민간 활용자는 연구보고서 주제 및 발간 현황 분석을 통해 생물자원 관련 시장 동향 및 기술 트렌드 파악 등 다양한 분석 기반 자료로 활용할 수 있습니다.

Thiacloprid (THI) is a neonicotinoid insecticide of interest to the USEPA due to its low absorption by crops, greater distribution into the surrounding areas, and potential for adverse effect to terrestrial and aquatic organisms. Prior to this report, there was very limited information addressing the ex vivo metabolism of THI by fish species and the metabolic pathways regulating its potential adverse effects. The in vitro and ex vivo biotransformation pathway of THI is defined by the formation of three primary metabolites (TM1, TM2 and TM3) via separate paths differentiated by reductive decyanation, reductive dechlorination with hydration and dealkylation processes, respectively. Kinetic rates were calculated for the rat microsomal decyanation of THI into TM1 (Km=299.2 µM and Vmax=5.3 pmol/min/mg), and for the dealkylation of THI into TM3 (Km=368.9 µM µM and Vmax=3.95 pmol/min/mg). Formation confirmation and identity inference of THI metabolites in absence of standards was achieved by LC-UV and High Resolution-MS strategies. It was concluded that the in vitro and ex vivo metabolic products of THI are conserved both across species (rat and RBT) and levels of biological organization (microsomes and liver slices), as previously reported for the neonicotinoid insecticides Imidacloprid and Acetamiprid. This dataset is associated with the following publication: Serrano, J., R. Kolanczyk, B. Blackwell, B. Sheedy, and M. Tapper. In vitro metabolism assessment of thiacloprid in rainbow trout and rat by LC-UV and high resolution-mass spectrometry. XENOBIOTICA. Taylor & Francis, Inc., Philadelphia, PA, USA, 51(5): 536-548, (2021).