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Cytoscape Lightning Talk

Sophie Liu, Alex Pico, Dexter Pratt
2020 Zenodo  
Lightning talk about Cytoscape for EOSS Kickoff Meeting 2019
doi:10.5281/zenodo.3833191 fatcat:2t7mprww3bbbfgokjnllzxvvlm

Centaur: a mobile dexterous humanoid for surface operations

Fredrik Rehnmark, Robert O. Ambrose, S. Michael Goza, Lucien Junkin, Peter D. Neuhaus, Jerry E. Pratt, Grant R. Gerhart, Charles M. Shoemaker, Douglas W. Gage
2005 Unmanned Ground Vehicle Technology VII  
A mobile, highly dexterous Extra-Vehicular Robotic (EVR) system called Centaur is proposed to cost-effectively augment human astronauts on surface excursions.  ...  Biologically inspired in its design, Robonaut shares only partial human form, with efficient dual arm dexterity as the real driver in its design.  ...  CAP ABILITmS FOR PLANETARY SURFACE EXPLORATION 3.1 Upper Body NASA has developed one of the most sophisticated dexterous robots ever built, called Robonaut.  ... 
doi:10.1117/12.601525 fatcat:jrz5y4lomrc35obhnygn2ejdnq

Decoding of persistent multiscale structures in complex biological networks [article]

Fan Zheng, She Zhang, Christopher Churas, Dexter Pratt, Ivet Bahar, Trey Ideker
2020 biorxiv/medrxiv  
Networks of genes, proteins, and cells exhibit significant multiscale organization, with distinct communities appearing at different spatial resolutions. Here, we apply the concept of 'persistent homology' to identify network communities that persist within defined scale ranges, yielding a hierarchy of robust structures in data. Application to mouse single-cell transcriptomes significantly expands the catalog of cell types identified by current tools, while analysis of SARS-COV-2 networks suggests pro-viral hijacking of WNT.
doi:10.1101/2020.06.16.151555 pmid:32587977 pmcid:PMC7310637 fatcat:d5j7vpfykvgonanqysl2qmrxeu

HiDeF: identifying persistent structures in multiscale 'omics data

Fan Zheng, She Zhang, Christopher Churas, Dexter Pratt, Ivet Bahar, Trey Ideker
2021 Genome Biology  
AbstractIn any 'omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse
more » ... cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.
doi:10.1186/s13059-020-02228-4 pmid:33413539 fatcat:tjv4hevzyvhtpgltuir7cqrl7a

NDEx: A Community Resource for Sharing and Publishing of Biological Networks [chapter]

Rudolf T. Pillich, Jing Chen, Vladimir Rynkov, David Welker, Dexter Pratt
2017 Msphere  
Networks are a powerful and flexible paradigm that facilitate communication and computation about interactions of any type, whether social, economic, or biological. NDEx, the Network Data Exchange, is an online commons to enable new modes of collaboration and publication using biological networks. NDEx creates an access point and interface to a broad range of networks, whether they express molecular interactions, curated relationships from literature, or the outputs of systematic analysis of
more » ... data. Research 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 can also facilitate the integration of networks as data in electronic publications, thus making a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow seamlessly between scientists. researchers can be stored, shared, discussed, reviewed, and used. This chapter describes NDEx, the Network Data Exchange, an online commons where scientists can store, share and publicly distribute biological networks as dynamic actionable data, and develop applications using them [8] . One of the goals of the NDEx Project is to create a home for network models that are currently available only as figures, tables, or supplementary information, such as networks produced via systematic mining and integration of largescale molecular data. In doing this, the NDEx project does not compete with existing pathway and interaction databases, such as KEGG or Reactome; instead, NDEx provides a novel, common distribution channel for these efforts, preserving their identity and attribution rather than subsuming them. By providing a flexible computable medium for biological knowledge, networks are also becoming a critical element for new models of scientific publication, in which data and its derivatives are as important as text [9] . The NDEx platform is intended to enable experimentation with novel forms of scientific review and discourse. NDEx networks are assigned stable, globally unique URIs and can be referenced by publications, by other networks, and by analytic applications. NDEx aims to become the main hub for the development of new, lightweight applications and scripts capable of accessing and manipulating networks via the NDEx API, making it easy for scientists to develop novel network-based analyses. An example is CyNDEx, the NDEx Cytoscape App: CyNDEx enables users to access an NDEx server directly from Cytoscape and engage its full range of tools to analyze and transform any networks stored on NDEx. The NDEx project fosters the creation of new utilities and analytic tools that use NDEx via code examples, client libraries in multiple languages, developer documentation, and a strong social media outreach campaign to drive community awareness and engagement.
doi:10.1007/978-1-4939-6783-4_13 pmid:28150243 fatcat:g7o7npnizjgingotcpqmsgkpc4

Network propagation in the cytoscape cyberinfrastructure

Daniel E. Carlin, Barry Demchak, Dexter Pratt, Eric Sage, Trey Ideker, Xianghong Jasmine Zhou
2017 PLoS Computational Biology  
Supporting information Carlin DE, Demchak B, Pratt D, Sage E, Ideker T (2017) Network propagation in the cytoscape cyberinfrastructure.  ... 
doi:10.1371/journal.pcbi.1005598 pmid:29023449 pmcid:PMC5638226 fatcat:w5f3ftbiqvcebptyimwkg3tbmy

Cyc: toward programs with common sense

Douglas B. Lenat, R. V. Guha, Karen Pittman, Dexter Pratt, Mary Shepherd
1990 Communications of the ACM  
Cyc, a massive project to create a knowledge base spanning all human consensus knowledge, is discussed. The project will require the development of a new logic language for expressing knowledge before sets of procedures can be created and the knowledge base itself built. Cyc programmers developed CycL, a unique representation language and inference engine. Inferencing in Cyc at the heuristic level involves a variety of 'logically superfluous' mechanisms that make procedures more efficient, as
more » ... ll as highly specialized inference rules. The dependency analysis procedure used in the knowledge base is also described. Objects and properties can be either 'spatially' or 'temporally' intrinsic. Several application programs making use of Cyc are currently under development. © COPYRIGHT Association for Computing Machinery 1990 CYC: TOWARD PROGRAMS WITH COMMON SENSE Motivation: The Brittleness Bottleneck For three decades, Artificial Intelligence researchers have grappled with issues like control of search in problem solving, organization of memory, logic for the representation of knowledge, perception, and so on, driven by tasks ranging from infants exploring their "worlds" through experts performing some intricate reasoning task. Despite many types of successes, today's AI software--and in many ways non-AI software as well--has reached a kind of bottleneck which is limiting its competence and usability. This article begins with a discussion of the nature of that bottleneck, and then describes the Cyc project: a serious attempt, begun in late 1984, to overcome this limitation.
doi:10.1145/79173.79176 fatcat:2qucchqcyrbfdkfzwmmprbkoau

Multiscale community detection in Cytoscape

Akshat Singhal, Song Cao, Christopher Churas, Dexter Pratt, Santo Fortunato, Fan Zheng, Trey Ideker, Teresa M. Przytycka
2020 PLoS Computational Biology  
Detection of community structure has become a fundamental step in the analysis of biological networks with application to protein function annotation, disease gene prediction, and drug discovery. This recent impact creates a need to make these techniques and their accompanying visualization schemes available to a broad range of biologists. Here we present a service-oriented, end-to-end software framework, CDAPS (Community Detection APplication and Service), that integrates the identification,
more » ... notation, visualization, and interrogation of multiscale network communities, accessible within the popular Cytoscape network analysis platform. With novel design principles, CDAPS addresses unmet new challenges, such as identifying hierarchical community structures, comparison of outputs generated from diverse network resources, and easy deployment of new algorithms, to facilitate community-sourced science. We demonstrate that the CDAPS framework can be applied to high-throughput protein-protein interaction networks to gain novel insights, such as the identification of putative new members of known protein complexes.
doi:10.1371/journal.pcbi.1008239 pmid:33095781 fatcat:32bp26eebza6hnirt5noqxqr7y

Deep functional synthesis: a machine learning approach to gene functional enrichment [article]

Sheng Wang, Jianzhu Ma, Samson Fong, Stefano Rensi, Jiawei Han, Jian Peng, Dexter Pratt, Russ Altman, Trey Ideker
2019 bioRxiv   pre-print
Gene functional enrichment is a mainstay of genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of the biological context. Here we present an alternative machine learning approach, Deep Functional Synthesis (DeepSyn), which moves beyond gene function databases to dynamically infer the functions of a gene set from its associated network of literature and data, conditioned on the disease and drug context of the current experiment. Using a
more » ... ge graph with 3,048,803 associations between genes, diseases, drugs, and functions, DeepSyn obtained accurate performance (range 0.74 AUC to 0.96 AUC) on a variety of biological applications including drug target identification, gene set functional enrichment, and disease gene prediction.
doi:10.1101/824086 fatcat:yq2rx6qx2racbkv4fpvv3mavoe

Modeling of RAS complexes supports roles in cancer for less studied partners

H. Billur Engin, Daniel Carlin, Dexter Pratt, Hannah Carter
2017 BMC Biophysics  
RAS protein interactions have predominantly been studied in the context of the RAF and PI3kinase oncogenic pathways. Structural modeling and X-ray crystallography have demonstrated that RAS isoforms bind to canonical downstream effector proteins in these pathways using the highly conserved switch I and II regions. Other non-canonical RAS protein interactions have been experimentally identified, however it is not clear whether these proteins also interact with RAS via the switch regions.
more » ... To address this question we constructed a RAS isoform-specific protein-protein interaction network and predicted 3D complexes involving RAS isoforms and interaction partners to identify the most probable interaction interfaces. The resulting models correctly captured the binding interfaces for well-studied effectors, and additionally implicated residues in the allosteric and hyper-variable regions of RAS proteins as the predominant binding site for non-canonical effectors. Several partners binding to this new interface (SRC, LGALS1, RABGEF1, CALM and RARRES3) have been implicated as important regulators of oncogenic RAS signaling. We further used these models to investigate competitive binding and multi-protein complexes compatible with RAS surface occupancy and the putative effects of somatic mutations on RAS protein interactions. Conclusions: We discuss our findings in the context of RAS localization to the plasma membrane versus within the cytoplasm and provide a list of RAS protein interactions with possible cancer-related consequences, which could help guide future therapeutic strategies to target RAS proteins.
doi:10.1186/s13628-017-0037-6 pmid:28815022 pmcid:PMC5558186 fatcat:trv4kzowqzhp3a65kuezirjozu

NDEx 2.0: A Clearinghouse for Research on Cancer Pathways

Dexter Pratt, Jing Chen, Rudolf Pillich, Vladimir Rynkov, Aaron Gary, Barry Demchak, Trey Ideker
2017 Cancer Research  
We present NDEx 2.0, the latest release of the Network Data Exchange (NDEx) online data commons (www.ndexbio.org) and the ways in which it can be used to (i) improve the quality and abundance of biological networks relevant to the cancer research community; (ii) provide a medium for collaboration involving networks; and (iii) facilitate the review and dissemination of networks. We describe innovations addressing the challenges of an online data commons: scalability, data integration, data
more » ... rdization, control of content and format by authors, and decentralized mechanisms for review. The practical use of NDEx is presented in the context of a novel strategy to foster networkoriented communities of interest in cancer research by adapting methods from academic publishing and social media. Cancer Res; 77(21); e58-61. Ó2017 AACR.
doi:10.1158/0008-5472.can-17-0606 pmid:29092941 fatcat:uco2scrwing4pdiik3b4zkoy4a

NDEx: Accessing Network Models and Streamlining Network Biology Workflows

Rudolf T. Pillich, Jing Chen, Christopher Churas, Sophie Liu, Keiichiro Ono, David Otasek, Dexter Pratt
2021 Current Protocols  
NDEx, The Network Data Exchange (Pillich, Chen, Rynkov, Welker, & Pratt, 2017; Pratt et al., 2017; Pratt et al., 2015) is an online commons providing infrastructure and data resources to facilitate the  ...  Pratt: funding acquisition, project administration, supervision, writing review and editing Conflict of Interest The authors declare no competing interest.  ...  Chris Churas: resources, software, validation; Sophie Liu: resources, software, validation; Keichiro Ono: resources, software, validation, visual-ization; David Otasek: resources, software, validation; Dexter  ... 
doi:10.1002/cpz1.258 pmid:34570431 pmcid:PMC8544027 fatcat:7qdknr54kreb5ooikcdvx2xdmu

NeXO Web: the NeXO ontology database and visualization platform

Janusz Dutkowski, Keiichiro Ono, Michael Kramer, Michael Yu, Dexter Pratt, Barry Demchak, Trey Ideker
2013 Nucleic Acids Research  
The Network-extracted Ontology (NeXO) is a gene ontology inferred directly from large-scale molecular networks. While most ontologies are constructed through manual expert curation, NeXO uses a principled computational approach which integrates evidence from hundreds of thousands of individual gene and protein interactions to construct a global hierarchy of cellular components and processes. Here, we describe the development of the NeXO Web platform (http://www.nexontology.org)-an online
more » ... e and graphical user interface for visualizing, browsing and performing term enrichment analysis using NeXO and the gene ontology. The platform applies state-of-the-art web technology and visualization techniques to provide an intuitive framework for investigating biological machinery captured by both data-driven and manually curated ontologies.
doi:10.1093/nar/gkt1192 pmid:24271398 pmcid:PMC3965056 fatcat:yhpm4u4dkjbxla4vinw2tsnenm

NDEx, the Network Data Exchange

Dexter Pratt, Jing Chen, David Welker, Ricardo Rivas, Rudolf Pillich, Vladimir Rynkov, Keiichiro Ono, Carol Miello, Lyndon Hicks, Sandor Szalma, Aleksandar Stojmirovic, Radu Dobrin (+4 others)
2015 Cell Systems  
Graphical Abstract Highlights d NDEx (www.ndexbio.org) is an online commons for biological networks d Users can upload, share, and distribute networks of many types, sizes, and formats d Developers can access NDEx via a web-based programming interface d NDEx promotes the publication of networks as dynamic, actionable data In Brief NDEx (www.ndexbio.org) is an online commons where scientists can upload, share, and publicly distribute biological networks of many types, sizes, and formats. It
more » ... tes the publication of networks as dynamic, actionable data and the development of applications using networks.
doi:10.1016/j.cels.2015.10.001 pmid:26594663 pmcid:PMC4649937 fatcat:3fjzu55i7rde7mjvgiuwdqvnla

Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data

Natalie L Catlett, Anthony J Bargnesi, Stephen Ungerer, Toby Seagaran, William Ladd, Keith O Elliston, Dexter Pratt
2013 BMC Bioinformatics  
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. Results: We present a detailed description of
more » ... e 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. Conclusions: 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.
doi:10.1186/1471-2105-14-340 pmid:24266983 pmcid:PMC4222496 fatcat:2om5axzmnjbvzoja6gc2626ski
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