The Experts below are selected from a list of 78393 Experts worldwide ranked by ideXlab platform
Gregory F. Cooper - One of the best experts on this subject based on the ideXlab platform.
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Explicit Representation of protein activity states significantly improves causal discovery of protein phosphorylation networks
BMC Bioinformatics, 2020Co-Authors: Jinling Liu, Gregory F. CooperAbstract:Protein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected to extensive perturbations, the statistical relationships between phosphorylation states of proteins may be disrupted, making it difficult to reconstruct the true protein phosphorylation network. Here, we describe a novel framework to address this challenge. We have developed a causal discovery framework that Explicitly represents the activity state of each protein kinase as an unmeasured variable and developed a novel algorithm called “InferA” to infer the protein activity states, which allows us to incorporate the protein phosphorylation level, pharmacological interventions and prior knowledge. We applied our framework to simulated datasets and to a real-world dataset. The simulation experiments demonstrated that Explicit Representation of activity states of protein kinases allows one to effectively represent the impact of interventions and thus enabled our framework to accurately recover the ground-truth causal network. Results from the real-world dataset showed that the Explicit Representation of protein activity states allowed an effective and data-driven integration of the prior knowledge by InferA, which further leads to the recovery of a phosphorylation network that is more consistent with experiment results. Explicit Representation of the protein activity states by our novel framework significantly enhances causal discovery of protein phosphorylation networks.
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Explicit Representation of protein activity states significantly improves causal discovery of protein phosphorylation networks
BMC Bioinformatics, 2020Co-Authors: Jinling Liu, Gregory F. CooperAbstract:Abstract Background Protein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected to extensive perturbations, the statistical relationships between phosphorylation states of proteins may be disrupted, making it difficult to reconstruct the true protein phosphorylation network. Here, we describe a novel framework to address this challenge. Results We have developed a causal discovery framework that Explicitly represents the activity state of each protein kinase as an unmeasured variable and developed a novel algorithm called “InferA” to infer the protein activity states, which allows us to incorporate the protein phosphorylation level, pharmacological interventions and prior knowledge. We applied our framework to simulated datasets and to a real-world dataset. The simulation experiments demonstrated that Explicit Representation of activity states of protein kinases allows one to effectively represent the impact of interventions and thus enabled our framework to accurately recover the ground-truth causal network. Results from the real-world dataset showed that the Explicit Representation of protein activity states allowed an effective and data-driven integration of the prior knowledge by InferA, which further leads to the recovery of a phosphorylation network that is more consistent with experiment results. Conclusions Explicit Representation of the protein activity states by our novel framework significantly enhances causal discovery of protein phosphorylation networks.
Daniele Nardi - One of the best experts on this subject based on the ideXlab platform.
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Explicit Representation of social norms for social robots
Intelligent Robots and Systems, 2015Co-Authors: Fabio Maria Carlucci, Lorenzo Nardi, Luca Iocchi, Daniele NardiAbstract:As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of Explicit Representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.
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IROS - Explicit Representation of social norms for social robots
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Fabio Maria Carlucci, Lorenzo Nardi, Luca Iocchi, Daniele NardiAbstract:As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of Explicit Representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.
Jinling Liu - One of the best experts on this subject based on the ideXlab platform.
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Explicit Representation of protein activity states significantly improves causal discovery of protein phosphorylation networks
BMC Bioinformatics, 2020Co-Authors: Jinling Liu, Gregory F. CooperAbstract:Protein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected to extensive perturbations, the statistical relationships between phosphorylation states of proteins may be disrupted, making it difficult to reconstruct the true protein phosphorylation network. Here, we describe a novel framework to address this challenge. We have developed a causal discovery framework that Explicitly represents the activity state of each protein kinase as an unmeasured variable and developed a novel algorithm called “InferA” to infer the protein activity states, which allows us to incorporate the protein phosphorylation level, pharmacological interventions and prior knowledge. We applied our framework to simulated datasets and to a real-world dataset. The simulation experiments demonstrated that Explicit Representation of activity states of protein kinases allows one to effectively represent the impact of interventions and thus enabled our framework to accurately recover the ground-truth causal network. Results from the real-world dataset showed that the Explicit Representation of protein activity states allowed an effective and data-driven integration of the prior knowledge by InferA, which further leads to the recovery of a phosphorylation network that is more consistent with experiment results. Explicit Representation of the protein activity states by our novel framework significantly enhances causal discovery of protein phosphorylation networks.
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Explicit Representation of protein activity states significantly improves causal discovery of protein phosphorylation networks
BMC Bioinformatics, 2020Co-Authors: Jinling Liu, Gregory F. CooperAbstract:Abstract Background Protein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected to extensive perturbations, the statistical relationships between phosphorylation states of proteins may be disrupted, making it difficult to reconstruct the true protein phosphorylation network. Here, we describe a novel framework to address this challenge. Results We have developed a causal discovery framework that Explicitly represents the activity state of each protein kinase as an unmeasured variable and developed a novel algorithm called “InferA” to infer the protein activity states, which allows us to incorporate the protein phosphorylation level, pharmacological interventions and prior knowledge. We applied our framework to simulated datasets and to a real-world dataset. The simulation experiments demonstrated that Explicit Representation of activity states of protein kinases allows one to effectively represent the impact of interventions and thus enabled our framework to accurately recover the ground-truth causal network. Results from the real-world dataset showed that the Explicit Representation of protein activity states allowed an effective and data-driven integration of the prior knowledge by InferA, which further leads to the recovery of a phosphorylation network that is more consistent with experiment results. Conclusions Explicit Representation of the protein activity states by our novel framework significantly enhances causal discovery of protein phosphorylation networks.
Fabio Maria Carlucci - One of the best experts on this subject based on the ideXlab platform.
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Explicit Representation of social norms for social robots
Intelligent Robots and Systems, 2015Co-Authors: Fabio Maria Carlucci, Lorenzo Nardi, Luca Iocchi, Daniele NardiAbstract:As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of Explicit Representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.
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IROS - Explicit Representation of social norms for social robots
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Fabio Maria Carlucci, Lorenzo Nardi, Luca Iocchi, Daniele NardiAbstract:As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of Explicit Representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.
Lonce Wyse - One of the best experts on this subject based on the ideXlab platform.
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the temporal window Explicit Representation of future actions in improvisational performances
Creativity and Cognition, 2017Co-Authors: Alex Mitchell, Prashanth Thattai, Lonce WyseAbstract:Improvisation often requires performers to anticipate each other's upcoming actions to coordinate their performance. Traditionally, improvisation does not involve Explicit Representation of upcoming actions, relying instead on implicit cues and overt gestures. Providing a concrete Representation of these actions may impact the nature of improvisation. To explore how performers use such a Representation, we introduce the "temporal window", a shared workspace for collaborators to indicate upcoming actions shortly before those actions are carried out. We describe versions for music and storytelling, and present results of an observational study of use of these systems by 11 pairs of participants. Participants used the temporal window in various ways, including using knowledge about upcoming actions when performing live, and adjusting upcoming actions based on knowledge of both the live performance and upcoming actions. These observations can inform design to support improvisation as well as other systems that support anticipating upcoming actions.
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Creativity & Cognition - The Temporal Window: Explicit Representation of Future Actions in Improvisational Performances
Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition, 2017Co-Authors: Alex Mitchell, Prashanth Thattai, Lonce WyseAbstract:Improvisation often requires performers to anticipate each other's upcoming actions to coordinate their performance. Traditionally, improvisation does not involve Explicit Representation of upcoming actions, relying instead on implicit cues and overt gestures. Providing a concrete Representation of these actions may impact the nature of improvisation. To explore how performers use such a Representation, we introduce the "temporal window", a shared workspace for collaborators to indicate upcoming actions shortly before those actions are carried out. We describe versions for music and storytelling, and present results of an observational study of use of these systems by 11 pairs of participants. Participants used the temporal window in various ways, including using knowledge about upcoming actions when performing live, and adjusting upcoming actions based on knowledge of both the live performance and upcoming actions. These observations can inform design to support improvisation as well as other systems that support anticipating upcoming actions.
