The Experts below are selected from a list of 24039 Experts worldwide ranked by ideXlab platform

Steven P Gygi - One of the best experts on this subject based on the ideXlab platform.

  • proteome wide quantitative multiplexed profiling of protein expression carbon Source Dependency in saccharomyces cerevisiae
    Molecular Biology of the Cell, 2015
    Co-Authors: Joao A Paulo, Jeremy D Oconnell, Aleksandr Gaun, Steven P Gygi
    Abstract:

    The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon Sources can be comprehensively examined using mass spectrometry-based multiplexing strategies. In this study, we investigate changes in the S. cerevisiae proteome resulting from cultures grown in minimal media using galactose, glucose, or raffinose as the carbon Source. We used a tandem mass tag 9-plex strategy to determine alterations in relative protein abundance due to a particular carbon Source, in triplicate, thereby permitting subsequent statistical analyses. We quantified more than 4700 proteins across all nine samples; 1003 proteins demonstrated statistically significant differences in abundance in at least one condition. The majority of altered proteins were classified as functioning in metabolic processes and as having cellular origins of plasma membrane and mitochondria. In contrast, proteins remaining relatively unchanged in abundance included those having nucleic acid-related processes, such as transcription and RNA processing. In addition, the comprehensiveness of the data set enabled the analysis of subsets of functionally related proteins, such as phosphatases, kinases, and transcription factors. As a reSource, these data can be mined further in efforts to understand better the roles of carbon Source fermentation in yeast metabolic pathways and the alterations observed therein, potentially for industrial applications, such as biofuel feedstock production.

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

  • A Novel Dependency-to-String Model for Statistical Machine Translation
    Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), 2011
    Co-Authors: Jun Xie, Haitao Mi, Qun Liu
    Abstract:

    Dependency structure, as a first step towards semantics, is believed to be helpful to improve translation quality. However, previous works on Dependency structure based models typically resort to insertion operations to complete translations, which make it difficult to specify ordering information in translation rules. In our model of this paper, we handle this problem by directly specifying the ordering information in head-dependents rules which represent the Source side as head-dependents relations and the target side as strings. The head-dependents rules require only substitution operation, thus our model requires no heuristics or separate ordering models of the previous works to control the word order of translations. Large-scale experiments show that our model performs well on long distance reordering, and outperforms the state-of-the-art constituency-to-string model (+1.47 BLEU on average) and hierarchical phrase- based model (+0.46 BLEU on average) on two Chinese-English NIST test sets without resort to phrases or parse forest. For the first time, a Source Dependency structure based model catches up with and surpasses the state-of-the-art translation models.

Joao A Paulo - One of the best experts on this subject based on the ideXlab platform.

  • proteome wide quantitative multiplexed profiling of protein expression carbon Source Dependency in saccharomyces cerevisiae
    Molecular Biology of the Cell, 2015
    Co-Authors: Joao A Paulo, Jeremy D Oconnell, Aleksandr Gaun, Steven P Gygi
    Abstract:

    The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon Sources can be comprehensively examined using mass spectrometry-based multiplexing strategies. In this study, we investigate changes in the S. cerevisiae proteome resulting from cultures grown in minimal media using galactose, glucose, or raffinose as the carbon Source. We used a tandem mass tag 9-plex strategy to determine alterations in relative protein abundance due to a particular carbon Source, in triplicate, thereby permitting subsequent statistical analyses. We quantified more than 4700 proteins across all nine samples; 1003 proteins demonstrated statistically significant differences in abundance in at least one condition. The majority of altered proteins were classified as functioning in metabolic processes and as having cellular origins of plasma membrane and mitochondria. In contrast, proteins remaining relatively unchanged in abundance included those having nucleic acid-related processes, such as transcription and RNA processing. In addition, the comprehensiveness of the data set enabled the analysis of subsets of functionally related proteins, such as phosphatases, kinases, and transcription factors. As a reSource, these data can be mined further in efforts to understand better the roles of carbon Source fermentation in yeast metabolic pathways and the alterations observed therein, potentially for industrial applications, such as biofuel feedstock production.

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

  • A Novel Dependency-to-String Model for Statistical Machine Translation
    Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), 2011
    Co-Authors: Jun Xie, Haitao Mi, Qun Liu
    Abstract:

    Dependency structure, as a first step towards semantics, is believed to be helpful to improve translation quality. However, previous works on Dependency structure based models typically resort to insertion operations to complete translations, which make it difficult to specify ordering information in translation rules. In our model of this paper, we handle this problem by directly specifying the ordering information in head-dependents rules which represent the Source side as head-dependents relations and the target side as strings. The head-dependents rules require only substitution operation, thus our model requires no heuristics or separate ordering models of the previous works to control the word order of translations. Large-scale experiments show that our model performs well on long distance reordering, and outperforms the state-of-the-art constituency-to-string model (+1.47 BLEU on average) and hierarchical phrase- based model (+0.46 BLEU on average) on two Chinese-English NIST test sets without resort to phrases or parse forest. For the first time, a Source Dependency structure based model catches up with and surpasses the state-of-the-art translation models.

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

  • neural machine translation with Source Dependency representation
    Empirical Methods in Natural Language Processing, 2017
    Co-Authors: Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita, Tiejun Zhao
    Abstract:

    Source Dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of Source word and its Dependency label together. In this paper, we propose a novel NMT with Source Dependency representation to improve translation performance of NMT, especially long sentences. Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.