The Experts below are selected from a list of 6324 Experts worldwide ranked by ideXlab platform

Nasa - One of the best experts on this subject based on the ideXlab platform.

Clifford A. Shaffer - One of the best experts on this subject based on the ideXlab platform.

  • Efficiently Encoding Complex Biochemical Models with the Multistate Model Builder (MSMB).
    Modeling Biomolecular Site Dynamics, 2019
    Co-Authors: Alida Palmisano, Stefan Hoops, Layne T. Watson, Thomas C. Jones, John J. Tyson, Clifford A. Shaffer
    Abstract:

    Biologists seek to create increasingly complex molecular regulatory network models. Writing such a model is a creative effort that requires flexible analysis tools and better modeling languages than offered by many of today's biochemical model editors. Our Multistate Model Builder (MSMB) supports multistate models created using different modeling styles that suit the modeler rather than the software. MSMB defines a simple but powerful syntax to describe multistate species. Our syntax reduces the number of reactions needed to encode the model, thereby reducing the cognitive load involved with model creation. MSMB gives extensive feedback during all stages of model creation. Users can activate error notifications, and use these notifications as a guide toward a consistent, syntactically correct model. Any consistent model can be exported to SBML or COPASI formats. We show the effectiveness of MSMB's multistate syntax through realistic models of cell cycle regulation and mRNA transcription. MSMB is an open-source project implemented in Java and it uses the COPASI API. Complete information and the Installation Package can be found at http://copasi.org/Projects/ .

  • multistate model builder msmb a flexible editor for compact biochemical models
    BMC Systems Biology, 2014
    Co-Authors: Alida Palmisano, Stefan Hoops, Layne T. Watson, Thomas C. Jones, John J. Tyson, Clifford A. Shaffer
    Abstract:

    Background: Building models of molecular regulatory networks is challenging not just because of the intrinsic difficulty of describing complex biological processes. Writing a model is a creative effort that calls for more flexibility and interactive support than offered by many of today’s biochemical model editors. Our model editor MSMB — Multistate Model Builder — supports multistate models created using different modeling styles. Results: MSMB provides two separate advances on existing network model editors. (1) A simple but powerful syntax is used to describe multistate species. This reduces the number of reactions needed to represent certain molecular systems, thereby reducing the complexity of model creation. (2) Extensive feedback is given during all stages of the model creation process on the existing state of the model. Users may activate error notifications of varying stringency on the fly, and use these messages as a guide toward a consistent, syntactically correct model. MSMB default values and behavior during model manipulation (e.g., when renaming or deleting an element) can be adapted to suit the modeler, thus supporting creativity rather than interfering with it. MSMB’s internal model representation allows saving a model with errors and inconsistencies (e.g., an undefined function argument; a syntactically malformed reaction). A consistent model can be exported to SBML or COPASI formats. We show the effectiveness of MSMB’s multistate syntax through models of the cell cycle and mRNA transcription. Conclusions: Using multistate reactions reduces the number of reactions need to encode many biochemical network models. This reduces the cognitive load for a given model, thereby making it easier for modelers to build more complex models. The many interactive editing support features provided by MSMB make it easier for modelers to create syntactically valid models, thus speeding model creation. Complete information and the Installation Package can be found at http://www.copasi.org/SoftwareProjects. MSMB is based on Java and the COPASI API.

Eija I Korpelainen - One of the best experts on this subject based on the ideXlab platform.

  • Chipster: user-friendly analysis software for microarray and other high-throughput data
    BMC Genomics, 2011
    Co-Authors: M Aleksi Kallio, Jarno T Tuimala, Taavi Hupponen, Petri Klemelä, Massimiliano Gentile, Ilari Scheinin, Mikko Koski, Janne Käki, Eija I Korpelainen
    Abstract:

    Background The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Results Chipster ( http://chipster.csc.fi/ ) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Conclusions Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server Installation Package is freely available.

  • Chipster: user-friendly analysis software for microarray and other high-throughput data
    BMC Genomics, 2011
    Co-Authors: M Aleksi Kallio, Jarno T Tuimala, Taavi Hupponen, Petri Klemelä, Massimiliano Gentile, Ilari Scheinin, Mikko Koski, Janne Käki, Eija I Korpelainen
    Abstract:

    The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Chipster ( http://chipster.csc.fi/ ) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server Installation Package is freely available.

Alida Palmisano - One of the best experts on this subject based on the ideXlab platform.

  • Efficiently Encoding Complex Biochemical Models with the Multistate Model Builder (MSMB).
    Modeling Biomolecular Site Dynamics, 2019
    Co-Authors: Alida Palmisano, Stefan Hoops, Layne T. Watson, Thomas C. Jones, John J. Tyson, Clifford A. Shaffer
    Abstract:

    Biologists seek to create increasingly complex molecular regulatory network models. Writing such a model is a creative effort that requires flexible analysis tools and better modeling languages than offered by many of today's biochemical model editors. Our Multistate Model Builder (MSMB) supports multistate models created using different modeling styles that suit the modeler rather than the software. MSMB defines a simple but powerful syntax to describe multistate species. Our syntax reduces the number of reactions needed to encode the model, thereby reducing the cognitive load involved with model creation. MSMB gives extensive feedback during all stages of model creation. Users can activate error notifications, and use these notifications as a guide toward a consistent, syntactically correct model. Any consistent model can be exported to SBML or COPASI formats. We show the effectiveness of MSMB's multistate syntax through realistic models of cell cycle regulation and mRNA transcription. MSMB is an open-source project implemented in Java and it uses the COPASI API. Complete information and the Installation Package can be found at http://copasi.org/Projects/ .

  • multistate model builder msmb a flexible editor for compact biochemical models
    BMC Systems Biology, 2014
    Co-Authors: Alida Palmisano, Stefan Hoops, Layne T. Watson, Thomas C. Jones, John J. Tyson, Clifford A. Shaffer
    Abstract:

    Background: Building models of molecular regulatory networks is challenging not just because of the intrinsic difficulty of describing complex biological processes. Writing a model is a creative effort that calls for more flexibility and interactive support than offered by many of today’s biochemical model editors. Our model editor MSMB — Multistate Model Builder — supports multistate models created using different modeling styles. Results: MSMB provides two separate advances on existing network model editors. (1) A simple but powerful syntax is used to describe multistate species. This reduces the number of reactions needed to represent certain molecular systems, thereby reducing the complexity of model creation. (2) Extensive feedback is given during all stages of the model creation process on the existing state of the model. Users may activate error notifications of varying stringency on the fly, and use these messages as a guide toward a consistent, syntactically correct model. MSMB default values and behavior during model manipulation (e.g., when renaming or deleting an element) can be adapted to suit the modeler, thus supporting creativity rather than interfering with it. MSMB’s internal model representation allows saving a model with errors and inconsistencies (e.g., an undefined function argument; a syntactically malformed reaction). A consistent model can be exported to SBML or COPASI formats. We show the effectiveness of MSMB’s multistate syntax through models of the cell cycle and mRNA transcription. Conclusions: Using multistate reactions reduces the number of reactions need to encode many biochemical network models. This reduces the cognitive load for a given model, thereby making it easier for modelers to build more complex models. The many interactive editing support features provided by MSMB make it easier for modelers to create syntactically valid models, thus speeding model creation. Complete information and the Installation Package can be found at http://www.copasi.org/SoftwareProjects. MSMB is based on Java and the COPASI API.

M Aleksi Kallio - One of the best experts on this subject based on the ideXlab platform.

  • Chipster: user-friendly analysis software for microarray and other high-throughput data
    BMC Genomics, 2011
    Co-Authors: M Aleksi Kallio, Jarno T Tuimala, Taavi Hupponen, Petri Klemelä, Massimiliano Gentile, Ilari Scheinin, Mikko Koski, Janne Käki, Eija I Korpelainen
    Abstract:

    Background The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Results Chipster ( http://chipster.csc.fi/ ) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Conclusions Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server Installation Package is freely available.

  • Chipster: user-friendly analysis software for microarray and other high-throughput data
    BMC Genomics, 2011
    Co-Authors: M Aleksi Kallio, Jarno T Tuimala, Taavi Hupponen, Petri Klemelä, Massimiliano Gentile, Ilari Scheinin, Mikko Koski, Janne Käki, Eija I Korpelainen
    Abstract:

    The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Chipster ( http://chipster.csc.fi/ ) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server Installation Package is freely available.