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@article{Bento2014,
abstract = {ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.},
author = {Bento, A. Patr{\'{i}}cia and Gaulton, Anna and Hersey, Anne and Bellis, Louisa J. and Chambers, Jon and Davies, Mark and Kr{\"{u}}ger, Felix A. and Light, Yvonne and Mak, Lora and McGlinchey, Shaun and Nowotka, Michal and Papadatos, George and Santos, Rita and Overington, John P.},
doi = {10.1093/nar/gkt1031},
file = {:Users/cthoyt/Dropbox/Mendeley/2014/Bento et al. - 2014 - The ChEMBL bioactivity database An update.pdf:pdf},
isbn = {1362-4962 (Electronic)},
issn = {03051048},
journal = {Nucleic Acids Res.},
number = {D1},
pages = {1083--1090},
pmid = {24214965},
title = {{The ChEMBL bioactivity database: An update}},
volume = {42},
year = {2014}
}
@article{Catlett2013,
abstract = {BACKGROUND: Gene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action.$\backslash$n$\backslash$nRESULTS: We present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data. This methodology requires prior knowledge in the form of small networks that causally link a key upstream controller node representing a biological mechanism to downstream measurable quantities. These small directed networks are generated from a knowledge base of literature-curated qualitative biological cause-and-effect relationships expressed as a network. The small mechanism networks are evaluated as hypotheses to explain observed differential measurements. We provide a simple implementation of this methodology, Whistle, specifically geared towards the analysis of gene expression data and using prior knowledge expressed in Biological Expression Language (BEL). We present the Whistle analyses for three transcriptomic data sets using a publically available knowledge base. The mechanisms inferred by Whistle are consistent with the expected biology for each data set.$\backslash$n$\backslash$nCONCLUSIONS: Reverse Causal Reasoning yields mechanistic insights to the interpretation of gene expression profiling data that are distinct from and complementary to the results of analyses using ontology or pathway gene sets. This reverse engineering algorithm provides an evidence-driven approach to the development of models of disease, drug action, and drug toxicity.},
author = {Catlett, Natalie L and Bargnesi, Anthony J and Ungerer, Stephen and Seagaran, Toby and Ladd, William and Elliston, Keith O and Pratt, Dexter},
doi = {10.1186/1471-2105-14-340},
file = {:Users/cthoyt/Dropbox/Mendeley/2013/Catlett et al. - 2013 - Reverse causal reasoning applying qualitative causal knowledge to the interpretation of high-throughput data.pdf:pdf},
isbn = {1471-2105 (Electronic)},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Algorithms,Animals,Breast,Breast: cytology,Endothelium, Vascular,Endothelium, Vascular: cytology,Epithelial Cells,Epithelial Cells: cytology,Gene Expression Profiling,Gene Expression Profiling: methods,Genome, Human,High-Throughput Nucleotide Sequencing,High-Throughput Nucleotide Sequencing: methods,Histone-Lysine N-Methyltransferase,Histone-Lysine N-Methyltransferase: genetics,Humans,Insulin Resistance,Insulin Resistance: genetics,Knowledge Bases,Mice,Microarray Analysis,Molecular Probes,Molecular Probes: genetics,Nuclear Proteins,Nuclear Proteins: genetics},
number = {1},
pages = {340},
pmid = {24266983},
title = {{Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4222496{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {14},
year = {2013}
}
@article{Davis2017,
abstract = {The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between environmental chemicals and gene products and their relationships to diseases. Chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature are integrated to generate expanded networks and predict many novel associations between different data types. CTD now contains over 15 million toxicogenomic relationships. To navigate this sea of data, we added several new features, including DiseaseComps (which finds comparable diseases that share toxicogenomic profiles), statistical scoring for inferred gene-disease and pathway-chemical relationships, filtering options for several tools to refine user analysis and our new Gene Set Enricher (which provides biological annotations that are enriched for gene sets). To improve data visualization, we added a Cytoscape Web view to our ChemComps feature, included color-coded interactions and created a 'slim list' for our MEDIC disease vocabulary (allowing diseases to be grouped for meta-analysis, visualization and better data management). CTD continues to promote interoperability with external databases by providing content and cross-links to their sites. Together, this wealth of expanded chemical-gene-disease data, combined with novel ways to analyze and view content, continues to help users generate testable hypotheses about the molecular mechanisms of environmental diseases.},
author = {Davis, Allan Peter and Grondin, Cynthia J. and Johnson, Robin J. and Sciaky, Daniela and King, Benjamin L. and McMorran, Roy and Wiegers, Jolene and Wiegers, Thomas C. and Mattingly, Carolyn J.},
doi = {10.1093/nar/gkw838},
file = {:Users/cthoyt/Dropbox/Mendeley/2017/Davis et al. - 2017 - The Comparative Toxicogenomics Database Update 2017.pdf:pdf},
isbn = {1362-4962 (Electronic)$\backslash$r0305-1048 (Linking)},
issn = {13624962},
journal = {Nucleic Acids Res.},
number = {D1},
pages = {D972--D978},
pmid = {23093600},
title = {{The Comparative Toxicogenomics Database: Update 2017}},
volume = {45},
year = {2017}
}
@article{Demir2010,
abstract = {Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.},
author = {Demir, Emek and Cary, Michael P and Paley, Suzanne and Fukuda, Ken and Lemer, Christian and Vastrik, Imre and Wu, Guanming and D'Eustachio, Peter and Schaefer, Carl and Luciano, Joanne and Schacherer, Frank and Martinez-Flores, Irma and Hu, Zhenjun and Jimenez-Jacinto, Veronica and Joshi-Tope, Geeta and Kandasamy, Kumaran and Lopez-Fuentes, Alejandra C and Mi, Huaiyu and Pichler, Elgar and Rodchenkov, Igor and Splendiani, Andrea and Tkachev, Sasha and Zucker, Jeremy and Gopinath, Gopal and Rajasimha, Harsha and Ramakrishnan, Ranjani and Shah, Imran and Syed, Mustafa and Anwar, Nadia and Babur, {\"{O}}zg{\"{u}}n and Blinov, Michael and Brauner, Erik and Corwin, Dan and Donaldson, Sylva and Gibbons, Frank and Goldberg, Robert and Hornbeck, Peter and Luna, Augustin and Murray-Rust, Peter and Neumann, Eric and Reubenacker, Oliver and Samwald, Matthias and van Iersel, Martijn and Wimalaratne, Sarala and Allen, Keith and Braun, Burk and Whirl-Carrillo, Michelle and Cheung, Kei-Hoi and Dahlquist, Kam and Finney, Andrew and Gillespie, Marc and Glass, Elizabeth and Gong, Li and Haw, Robin and Honig, Michael and Hubaut, Olivier and Kane, David and Krupa, Shiva and Kutmon, Martina and Leonard, Julie and Marks, Debbie and Merberg, David and Petri, Victoria and Pico, Alex and Ravenscroft, Dean and Ren, Liya and Shah, Nigam and Sunshine, Margot and Tang, Rebecca and Whaley, Ryan and Letovksy, Stan and Buetow, Kenneth H and Rzhetsky, Andrey and Schachter, Vincent and Sobral, Bruno S and Dogrusoz, Ugur and McWeeney, Shannon and Aladjem, Mirit and Birney, Ewan and Collado-Vides, Julio and Goto, Susumu and Hucka, Michael and Nov{\`{e}}re, Nicolas Le and Maltsev, Natalia and Pandey, Akhilesh and Thomas, Paul and Wingender, Edgar and Karp, Peter D and Sander, Chris and Bader, Gary D},
doi = {10.1038/nbt1210-1308c},
file = {:Users/cthoyt/Dropbox/Mendeley/2010/Demir et al. - 2010 - The BioPAX community standard for pathway data sharing.pdf:pdf},
isbn = {1546-1696 (Electronic)$\backslash$r1087-0156 (Linking)},
issn = {1087-0156},
journal = {Nat. Biotechnol.},
number = {12},
pages = {1308--1308},
pmid = {20829833},
title = {{The BioPAX community standard for pathway data sharing}},
url = {http://www.nature.com/doifinder/10.1038/nbt1210-1308c},
volume = {28},
year = {2010}
}
@article{Domingo-Fernandez2017,
author = {Domingo-Fern{\'{a}}ndez, Daniel and Kodamullil, Alpha Tom and Iyappan, Anandhi and Naz, Mufassra and Emon, Mohammad Asif and Raschka, Tamara and Karki, Reagon and Springstubbe, Stephan and Ebeling, Christian and Hofmann-Apitius, Martin},
doi = {10.1093/bioinformatics/btx399},
journal = {Bioinformatics},
title = {{Multimodal Mechanistic Signatures for Neurodegenerative Diseases (NeuroMMSig): a web server for mechanism enrichmentle}},
volume = {btx399},
year = {2017}
}
@article{Emon2017,
abstract = {Neurodegenerative diseases including Alzheimer's disease are complex to tackle because of the complexity of the brain, both in structure and function. Such complexity is reflected by the involvement of various brain regions and multiple pathways in the etiology of neurodegenerative diseases that render single drug target approaches ineffective. Particularly in the area of neurodegeneration, attention has been drawn to repurposing existing drugs with proven efficacy and safety profiles. However, there is a lack of systematic analysis of the brain chemical space to predict the feasibility of repurposing strategies. Using a mechanism-based, drug-target interaction modeling approach, we have identified promising drug candidates for repositioning. Mechanistic cause-and-effect models consolidate relevant prior knowledge on drugs, targets, and pathways from the scientific literature and integrate insights derived from experimental data. We demonstrate the power of this approach by predicting two repositioning candidates for Alzheimer's disease and one for amyotrophic lateral sclerosis.},
author = {Emon, Mohammad Asif Emran Khan and Kodamullil, Alpha Tom and Karki, Reagon and Younesi, Erfan and Hofmann-Apitius, Martin},
doi = {10.3233/JAD-160222},
file = {:Users/cthoyt/Dropbox/Mendeley/2017/Emon et al. - 2017 - Using Drugs as Molecular Probes A Computational Chemical Biology Approach in Neurodegenerative Diseases.pdf:pdf},
issn = {13872877},
journal = {J. Alzheimer's Dis.},
keywords = {alzheimer disease,amyotrophic lateral sclerosis,biological expression language,disease-drug modeling,drug repositioning,neurodegenerative diseases},
number = {2},
pages = {677--686},
pmid = {28035920},
title = {{Using Drugs as Molecular Probes: AComputational Chemical Biology Approach in Neurodegenerative Diseases}},
url = {http://www.medra.org/servlet/aliasResolver?alias=iospress{\&}doi=10.3233/JAD-160222},
volume = {56},
year = {2017}
}
@article{Franz2015,
abstract = {UNLABELLED Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js. AVAILABILITY AND IMPLEMENTATION Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org. CONTACT [email protected].},
author = {Franz, Max and Lopes, Christian T. and Huck, Gerardo and Dong, Yue and Sumer, Onur and Bader, Gary D.},
doi = {10.1093/bioinformatics/btv557},
file = {:Users/cthoyt/Dropbox/Mendeley/2015/Franz et al. - 2015 - Cytoscape.js A graph theory library for visualisation and analysis.pdf:pdf},
isbn = {13674811 (Electronic)},
issn = {14602059},
journal = {Bioinformatics},
number = {2},
pages = {309--311},
pmid = {26415722},
title = {{Cytoscape.js: A graph theory library for visualisation and analysis}},
volume = {32},
year = {2015}
}
@article{Hagberg2008,
abstract = {NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for easy exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdos-Renyi, Small World, and Barabasi-Albert models. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.},
author = {Hagberg, Aric A. and Schult, Daniel A. and Swart, Pieter J.},
file = {:Users/cthoyt/Dropbox/Mendeley/2008/Hagberg, Schult, Swart - 2008 - Exploring network structure, dynamics, and function using NetworkX.pdf:pdf},
isbn = {3333333333},
issn = {1540-9295},
journal = {Proc. 7th Python Sci. Conf. (SciPy 2008)},
number = {SciPy},
pages = {11--15},
title = {{Exploring network structure, dynamics, and function using NetworkX}},
year = {2008}
}
@article{Hucka2003,
abstract = {MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/},
author = {Hucka, M and Finney, A and Sauro, H M and Bolouri, H and Doyle, J C and Kitano, H and Arkin, A P and Bornstein, B J and Bray, D and Cornish-Bowden, A and Cuellar, A A and Dronov, S and Gilles, E D and Ginkel, M and Gor, V and Goryanin, I I and Hedley, W J and Hodgman, T C and Hofmeyr, J-H and Hunter, P J and Juty, N S and Kasberger, J L and Kremling, A and Kummer, U and {Le Novere}, N and Loew, L M and Lucio, D and Mendes, P and Minch, E and Mjolsness, E D and Nakayama, Y and Nelson, M R and Nielsen, P F and Sakurada, T and Schaff, J C and Shapiro, B E and Shimizu, T S and Spence, H D and Stelling, J and Takahashi, K and Tomita, M and Wagner, J and Wang, J},
institution = {SBML Forum},
issn = {1367-4803 (Print)},
journal = {Bioinformatics},
keywords = {Database Management Systems,Databases, Factual,Documentation,Gene Expression Regulation,Hypermedia,Information Storage and Retrieval,Metabolism,Models, Biological,Models, Chemical,Programming Languages,Software,Software Design,Terminology as Topic,Vocabulary, Controlled,methods,physiology},
language = {eng},
month = {mar},
number = {4},
pages = {524--531},
pmid = {12611808},
title = {{The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.}},
volume = {19},
year = {2003}
}
@article{Irin2015,
abstract = {Neurodegenerative as well as autoimmune diseases have unclear aetiologies, but an increasing number of evidences report for a combination of genetic and epigenetic alterations that predispose for the development of disease. This review examines the major milestones in epigenetics research in the context of diseases and various computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases. In this work, we demonstrate how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables us to integrate epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. As an example, we depict the integration of epigenetic and genetic factors in a functional context specific to Parkinson's disease (PD) and Multiple Sclerosis (MS).},
author = {Irin, Afroza Khanam and Kodamullil, Alpha Tom and G{\"{u}}ndel, Michaela and Hofmann-Apitius, Martin},
doi = {10.1155/2015/737168},
file = {:Users/cthoyt/Dropbox/Mendeley/2015/Irin et al. - 2015 - Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mec.pdf:pdf},
issn = {23147156},
journal = {J. Immunol. Res.},
pmid = {26636108},
title = {{Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mechanistic Analysis}},
volume = {2015},
year = {2015}
}
@article{Kluyver2016,
abstract = {It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.},
author = {Kluyver, Thomas and Ragan-kelley, Benjamin and P{\'{e}}rez, Fernando and Granger, Brian and Bussonnier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica and Grout, Jason and Corlay, Sylvain and Ivanov, Paul and Avila, Dami{\'{a}}n and Abdalla, Safia and Willing, Carol},
doi = {10.3233/978-1-61499-649-1-87},
file = {:Users/cthoyt/Dropbox/Mendeley/2016/Kluyver et al. - 2016 - Jupyter Notebooks—a publishing format for reproducible computational workflows.pdf:pdf},
isbn = {9781614996491},
journal = {Position. Power Acad. Publ. Play. Agents Agendas},
keywords = {notebook,reproducibility,research code},
pages = {87--90},
title = {{Jupyter Notebooks—a publishing format for reproducible computational workflows}},
year = {2016}
}
@article{Martin2012,
abstract = {BACKGROUND: High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.$\backslash$n$\backslash$nRESULTS: Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNF$\alpha$, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNF$\alpha$-induced perturbation for each network model when compared against NF-$\kappa$B nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.$\backslash$n$\backslash$nCONCLUSIONS: The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.},
author = {Martin, Florian and Thomson, Ty M and Sewer, Alain and Drubin, David a and Mathis, Carole and Weisensee, Dirk and Pratt, Dexter and Hoeng, Julia and Peitsch, Manuel C},
doi = {10.1186/1752-0509-6-54},
file = {:Users/cthoyt/Dropbox/Mendeley/2012/Martin et al. - 2012 - Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks(2).pdf:pdf},
issn = {1752-0509},
journal = {BMC Syst. Biol.},
keywords = {Cell Cycle,Humans,Models, Biological,NF-kappa B,NF-kappa B: metabolism,Signal Transduction,Systems Biology,Systems Biology: methods,Tumor Necrosis Factor-alpha,Tumor Necrosis Factor-alpha: metabolism},
pages = {54},
pmid = {22651900},
title = {{Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3433335{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Pratt2015,
abstract = {Networks are a powerful and flexible methodology for expressing biological knowledge for computation and communication. Network-encoded information can include systematic screens for molecular interactions, biological relationships curated from literature, and outputs from analyses of Big Data. NDEx, the Network Data Exchange (www.ndexbio.org), is an online commons where scientists can upload, share, and publicly distribute networks. Networks in NDEx receive globally unique accession IDs and can be stored for private use, shared in pre-publication collaboration, or released for public access. Standard and novel data formats are accommodated in a flexible storage model. Organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx helps expand the role of networks in scientific discourse and facilitates the integration of networks as data in publications. It is a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow easily between scientists.},
author = {Pratt, Dexter and Chen, Jing and Welker, David and Rivas, Ricardo and Pillich, Rudolf and Rynkov, Vladimir and Ono, Keiichiro and Miello, Carol and Hicks, Lyndon and Szalma, Sandor and Stojmirovic, Aleksandar and Dobrin, Radu and Braxenthaler, Michael and Kuentzer, Jan and Demchak, Barry and Ideker, Trey},
doi = {10.1016/j.cels.2015.10.001},
file = {:Users/cthoyt/Dropbox/Mendeley/2015/Pratt et al. - 2015 - NDEx, the Network Data Exchange.pdf:pdf},
isbn = {24054712 (Linking)},
issn = {24054712},
journal = {Cell Syst.},
number = {4},
pages = {302--305},
pmid = {26594663},
publisher = {Elsevier Inc.},
title = {{NDEx, the Network Data Exchange}},
url = {http://dx.doi.org/10.1016/j.cels.2015.10.001},
volume = {1},
year = {2015}
}
@article{Sherry2001,
abstract = {In response to a need for a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, the National Center for Biotechnology Information (NCBI) has established the dbSNP database [S.T.Sherry, M.Ward and K. Sirotkin (1999) Genome Res., 9, 677-679]. Submissions to dbSNP will be integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data. The complete contents of dbSNP are available to the public at website: http://www.ncbi.nlm.nih.gov/SNP. The complete contents of dbSNP can also be downloaded in multiple formats via anonymous FTP at ftp://ncbi.nlm.nih.gov/snp/.},
archivePrefix = {arXiv},
arxivId = {Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., {\&} Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic acids research, 29(1), 308-11. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fc},
author = {Sherry, S. T.},
doi = {10.1093/nar/29.1.308},
eprint = {/www.pubmedcentral.nih.gov/articlerender.fc},
file = {:Users/cthoyt/Dropbox/Mendeley/2001/Sherry - 2001 - dbSNP the NCBI database of genetic variation.pdf:pdf},
isbn = {1362-4962 (Electronic)$\backslash$r0305-1048 (Linking)},
issn = {13624962},
journal = {Nucleic Acids Res.},
number = {1},
pages = {308--311},
pmid = {11125122},
primaryClass = {Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., {\&} Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic acids research, 29(1), 308-11. Retrieved from http:},
title = {{dbSNP: the NCBI database of genetic variation}},
url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/29.1.308},
volume = {29},
year = {2001}
}
@article{Slater2014,
author = {Slater, Ted},
doi = {10.1016/j.drudis.2013.12.011},
file = {:Users/cthoyt/Dropbox/Mendeley/2014/Slater - 2014 - Recent advances in modeling languages for pathway maps and computable biological networks.pdf:pdf},
issn = {13596446},
journal = {Drug Discov. Today},
month = {feb},
number = {2},
pages = {193--198},
title = {{Recent advances in modeling languages for pathway maps and computable biological networks}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1359644614000063},
volume = {19},
year = {2014}
}