The Comparative Toxicogenomics Database: update 2017

Davis, Allan Peter; Grondin, Cynthia; Johnson, Robin J.; Sciaky, Daniela; King, Benjamin L.; McMorran, Roy; Wiegers, Jolene; Wiegers, Thomas C.; Mattingly, Carolyn J.

doi:10.1093/nar/gkw838

Cited by 539 publications

(460 citation statements)

References 37 publications

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“…It is another rich resource for BioNER, additional to the labeled training data. We retrieve three biomedical entity dictionaries, i.e., genes/proteins, chemicals and diseases, from the comparative toxicogenomics database (CTD) [Davis et al, 2017]. Then we incorporate the dictionary information into the neural network models in two ways: (1) dictionary post-processing to match the 'O'-labeled entities with the dictionary to reduce the false negative rate, (2) dictionary pre-processing to add extra dimensions as part of the input into the wordlevel BiLSTM.…”

Section: Integration Of Biomedical Entity Dictionariesmentioning

confidence: 99%

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Wang

Ren

Zhang

et al. 2018

Preprint

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Motivation: Biomedical named entity recognition (BioNER) is the most fundamental task in biomedical text mining. State-of-the-art BioNER systems often require handcrafted features specifically designed for each type of biomedical entities. This feature generation process requires intensive labors from biomedical and linguistic experts, and makes it difficult to adapt these systems to new biomedical entity types. Although recent studies explored using neural network models for BioNER to free experts from manual feature generation, these models still require substantial human efforts to annotate massive training data. Results: We propose a multi-task learning framework for BioNER that is based on neural network models to save human efforts. We build a global model by collectively training multiple models that share parameters, each model capturing the characteristics of a different biomedical entity type. In experiments on five BioNER benchmark datasets covering four major biomedical entity types, our model outperforms state-ofthe-art systems and other neural network models by a large margin, even when only limited training data are available. Further analysis shows that the large performance gains come from sharing character-and word-level information between different biomedical entities. The approach creates new opportunities for text-mining approaches to help biomedical scientists better exploit knowledge in biomedical literature. Availability: The source code for our models is available at https://github.com/yuzhimanhua/lm-lstm-crf, and the corpora are available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016.

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Section: Integration Of Biomedical Entity Dictionariesmentioning

confidence: 99%

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Wang

Ren

Zhang

et al. 2018

Preprint

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“…• Search Tool for Interacting Chemicals (STICH, version 5.0), a database of known and predicted interactions between chemicals and proteins [12] • Comparative Toxicogenomics Database (CTD), which provides manually curated information concerning chemical-gene/protein interactions, chemical-disease as well as gene-disease relationships was used [13].…”

Section: Methodsmentioning

confidence: 99%

Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

Ahammad

2018

Preprint

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L-arginine is involved in a number of biological processes in our bodies. Metabolism of Larginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. Nhydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins-SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.

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“…We used this list to probe the chemical-gene interaction data maintained by the Comparative Toxicogenomics Database (CTD; [15]). CTD uses a manually curated literature pool of approximately 50,000 articles with information related to chemical-gene/protein interactions in vertebrates and invertebrates.…”

Section: Genes Associated With Mammary Carcinogens and Mammary Glandmentioning

confidence: 99%

BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening

Grashow

Rosa

Watford

et al. 2018

Computational Toxicology

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A B S T R A C TTargeted gene lists have been used in clinical settings to specify breast tumor type, and to predict breast cancer prognosis and response to treatment. Separately, panels have been curated to predict systemic toxicity and xenoestrogen activity as a part of chemical screening strategies. However, currently available panels do not specifically target biological processes relevant to breast development and carcinogenesis. We have developed a gene panel called the Breast Carcinogen Screen (BCScreen) as a tool to identify potential breast carcinogens and characterize mechanisms of toxicity. First, we used four seminal reviews to identify 14 key characteristics of breast carcinogenesis, such as apoptosis, immunomodulation, and genotoxicity. Then, using a hybrid data and knowledge-driven framework, we systematically combined information from whole transcriptome data from genomic databases, biomedical literature, the CTD chemical-gene interaction database, and primary literature review to generate a panel of 500 genes relevant to breast carcinogenesis. We used normalized pointwise mutual information (NPMI) to rank genes that frequently co-occurred with key characteristics in biomedical literature. We found that many genes identified for BCScreen were not included in prognostic breast cancer or systemic toxicity panels. For example, more than half of BCScreen genes were not included in the Tox21 S1500+ general toxicity gene list. Of the 230 that did overlap between the two panels, representation varied across characteristics of carcinogenesis ranging from 21% for genes associated with epigenetics to 82% for genes associated with xenobiotic metabolism. Enrichment analysis of BCScreen identified pathways and processes including response to steroid hormones, cancer, cell cycle, apoptosis, DNA damage and breast cancer. The biologically-based systematic approach to gene prioritization demonstrated here provides a flexible framework for creating diseasefocused gene panels to support discovery related to etiology. With validation, BCScreen may also be useful for toxicological screening relevant to breast carcinogenesis.

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The Comparative Toxicogenomics Database: update 2017

Cited by 539 publications

References 37 publications

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening

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