The Experts below are selected from a list of 109365 Experts worldwide ranked by ideXlab platform

Anne-claude Gingras - One of the best experts on this subject based on the ideXlab platform.

  • a web tool for visualizing quantitative protein protein interaction data
    Proteomics, 2015
    Co-Authors: James D.r. Knight, Guomin Liu, Jian Ping Zhang, Adrian Pasculescu, Hyungwon Choi, Anne-claude Gingras
    Abstract:

    Quantitative interaction proteomics data can be a challenge to efficiently analyze and subsequently present to an audience in a simple and easy to understand format that still conveys sufficient levels of information. Here we present freely accessible and open-source web tools for displaying multiple parameters from quantitative protein-protein interaction data sets in a visually intuitive format. Given a set of "bait" proteins with detected "prey" interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and Confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated Confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, Confidence Score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data, and present this information to others in an intuitive way.

  • A web‐tool for visualizing quantitative protein–protein interaction data
    PROTEOMICS, 2015
    Co-Authors: James D.r. Knight, Guomin Liu, Jian Ping Zhang, Adrian Pasculescu, Hyungwon Choi, Anne-claude Gingras
    Abstract:

    Quantitative interaction proteomics data can be a challenge to efficiently analyze and subsequently present to an audience in a simple and easy to understand format that still conveys sufficient levels of information. Here we present freely accessible and open-source web tools for displaying multiple parameters from quantitative protein-protein interaction data sets in a visually intuitive format. Given a set of "bait" proteins with detected "prey" interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and Confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated Confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, Confidence Score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data, and present this information to others in an intuitive way.

James D.r. Knight - One of the best experts on this subject based on the ideXlab platform.

  • a web tool for visualizing quantitative protein protein interaction data
    Proteomics, 2015
    Co-Authors: James D.r. Knight, Guomin Liu, Jian Ping Zhang, Adrian Pasculescu, Hyungwon Choi, Anne-claude Gingras
    Abstract:

    Quantitative interaction proteomics data can be a challenge to efficiently analyze and subsequently present to an audience in a simple and easy to understand format that still conveys sufficient levels of information. Here we present freely accessible and open-source web tools for displaying multiple parameters from quantitative protein-protein interaction data sets in a visually intuitive format. Given a set of "bait" proteins with detected "prey" interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and Confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated Confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, Confidence Score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data, and present this information to others in an intuitive way.

  • A web‐tool for visualizing quantitative protein–protein interaction data
    PROTEOMICS, 2015
    Co-Authors: James D.r. Knight, Guomin Liu, Jian Ping Zhang, Adrian Pasculescu, Hyungwon Choi, Anne-claude Gingras
    Abstract:

    Quantitative interaction proteomics data can be a challenge to efficiently analyze and subsequently present to an audience in a simple and easy to understand format that still conveys sufficient levels of information. Here we present freely accessible and open-source web tools for displaying multiple parameters from quantitative protein-protein interaction data sets in a visually intuitive format. Given a set of "bait" proteins with detected "prey" interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and Confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated Confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, Confidence Score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data, and present this information to others in an intuitive way.

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

  • Computing Confidence Score of any input phrases for a spoken dialog system
    2010 IEEE Workshop on Spoken Language Technology SLT 2010 - Proceedings, 2010
    Co-Authors: Feng Lin, Fuliang Weng
    Abstract:

    One of the main challenges in the development of robust dialog systems is to deal with noisy input due to imperfect results from any speech recognition module. A key step in addressing this noisy input is the computation of Confidence for the portions so that the subsequent dialog modules can make use of the Confidence Scores to design corresponding dialog strategies. While past work in computing Confidence Scores have been focusing on recognized words, semantic slots, or utterances, this paper is extending the investigation on computing Confidence Scores for all phrases of a sentence in a dialog system setting. We demonstrated that using a Conditional Maximum Entropy (CME) classifier in combination with features in acoustic, syntactic, and semantic categories, we are able to obtain a high performance for the dialog system application in a restaurant finding domain, specifically, an annotation error rate of 5.1% is reached, which is a very good result for practical user.

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

  • gpcr i tasser a hybrid approach to g protein coupled receptor structure modeling and the application to the human genome
    Structure, 2015
    Co-Authors: Jian Zhang, Jianyi Yang, Richard Jang, Yang Zhang
    Abstract:

    Experimental structure determination remains difficult for G protein-coupled receptors (GPCRs). We propose a new hybrid protocol to construct GPCR structure models that integrates experimental mutagenesis data with ab initio transmembrane (TM) helix assembly simulations. The method was tested on 24 known GPCRs where the ab initio TM-helix assembly procedure constructed the correct fold for 20 cases. When combined with weak homology and sparse mutagenesis restraints, the method generated correct folds for all the tested cases with an average Cα root-mean-square deviation 2.4 A in the TM regions. The new hybrid protocol was applied to model all 1,026 GPCRs in the human genome, where 923 have a high Confidence Score and are expected to have correct folds; these contain many pharmaceutically important families with no previously solved structures, including Trace amine, Prostanoids, Releasing hormones, Melanocortins, Vasopressin, and Neuropeptide Y receptors. The results demonstrate new progress on genome-wide structure modeling of TM proteins.

  • I-TASSER: A unified platform for automated protein structure and function prediction
    Nature Protocols, 2010
    Co-Authors: Ambrish Roy, Alper Kucukural, Yang Zhang
    Abstract:

    The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional (3D) atomic models from multiple threading alignments and iterative structural assembly simulations. The function of the protein is then inferred by structurally matching the 3D models with other known proteins. The output from a typical server run contains full-length secondary and tertiary structure predictions, and functional annotations on ligand-binding sites, Enzyme Commission numbers and Gene Ontology terms. An estimate of accuracy of the predictions is provided based on the Confidence Score of the modeling. This protocol provides new insights and guidelines for designing of online server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhanglab.ccmb.med.umich.edu/I-TASSER.

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

  • Confidence scoring using whitebox meta models with linear classifier probes
    International Conference on Artificial Intelligence and Statistics, 2019
    Co-Authors: Tongfei Chen, Jiri Navratil, Vijay S Iyengar, Karthikeyan Shanmugam
    Abstract:

    We propose a novel Confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The Confidence Score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probes inserted between the various layers of the base model. Our experiments demonstrate that this approach outperforms multiple baselines in a filtering task, i.e., task of rejecting samples with low Confidence. Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with and without added noise. We discuss the importance of Confidence scoring to bridge the gap between experimental and real-world applications.

  • Confidence scoring using whitebox meta models with linear classifier probes
    arXiv: Learning, 2018
    Co-Authors: Tongfei Chen, Jiri Navratil, Vijay S Iyengar, Karthikeyan Shanmugam
    Abstract:

    We propose a novel Confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The Confidence Score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probes inserted between the various layers of the base model. Our experiments demonstrate that this approach outperforms various baselines in a filtering task, i.e., task of rejecting samples with low Confidence. Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with and without added noise. We discuss the importance of Confidence scoring to bridge the gap between experimental and real-world applications.