The Experts below are selected from a list of 1947 Experts worldwide ranked by ideXlab platform

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

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

  • BIOINFORMATICS Pair Stochastic Tree Adjoining Grammars for Aligning and Predicting Pseudoknot RNA Structures
    2016
    Co-Authors: Hiroshi Matsui, Kengo Sato, Yasubumi Sakakibara
    Abstract:

    Motivation: Since the whole genome sequences of many species have been determined, computational prediction of RNA secondary structures and computational identification of those non-coding RNA regions by comparative genomics become important. Therefore, more advanced alignment methods are required. Recently, an approach of structural alignment for RNA sequences has been introduced to solve these problems. Pair HMMs on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignment of RNA secondary structu-res, although PHMMTSs are incapable of handling pseudo-knots. On the other hand, tree adjoining Grammars (TAGs), a subclass of Context Sensitive Grammars, are suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. Results: We propose pair stochastic tree adjoining Grammars (PSTAGs) for aligning and predicting RNA secondary structu-res including a simple type of pseudoknots which can repre-sent most of known pseudoknot structures. First, we extend PHMMTSs defined on alignment of “trees ” to PSTAGs defined on alignment of “TAG trees ” which represent derivation pro-cess of TAGs and are functionally equivalent to derived trees of TAGs. Then, we develop an efficient dynamic program-ming algorithm of PSTAGs for obtaining an optimal structural alignment including pseudoknots. We implement the PSTAG algorithm and demonstrate the properties of algorithm by using it to align and predict several small pseudoknot struc-tures. We believe that our implemented program based on PSTAGs is the first grammar-based and practically executa-ble software for comparative analyses of RNA pseudoknot structures, and further non-coding RNAs. Availability: The source code of PSTAG and its web applica-tion are available a

  • pair stochastic tree adjoining Grammars for aligning and predicting pseudoknot rna structures
    Bioinformatics, 2005
    Co-Authors: Hiroshi Matsui, Kengo Sato, Yasubumi Sakakibara
    Abstract:

    Motivation: Since the whole genome sequences of many species have been determined, computational prediction of RNA secondary structures and computational identification of those non-coding RNA regions by comparative genomics become important. Therefore, more advanced alignment methods are required. Recently, an approach of structural alignment for RNA sequences has been introduced to solve these problems. Pair hidden Markov models on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignment of RNA secondary structures, although PHMMTSs are incapable of handling pseudoknots. On the other hand, tree adjoining Grammars (TAGs), a subclass of Context-Sensitive Grammars, are suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. Results: We propose pair stochastic TAGs (PSTAGs) for aligning and predicting RNA secondary structures including a simple type of pseudoknot which can represent most known pseudoknot structures. First, we extend PHMMTSs defined on alignment of 'trees' to PSTAGs defined on alignment of 'TAG trees' which represent derivation processes of TAGs and are functionally equivalent to derived trees of TAGs. Then, we develop an efficient dynamic programming algorithm of PSTAGs for obtaining an optimal structural alignment including pseudoknots. We implement the PSTAG algorithm and demonstrate the properties of the algorithm by using it to align and predict several small pseudoknot structures. We believe that our implemented program based on PSTAGs is the first grammar-based and practically executable software for comparative analyses of RNA pseudoknot structures, and, further, non-coding RNAs. Availability: The source code of PSTAG and its web application are available at http://phmmts.dna.bio.keio.ac.jp/pstag/ Contact: yasu@bio.keio.ac.jp

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

  • modeling incremental language comprehension in the brain with combinatory categorial grammar
    Cellular and Molecular Life Sciences, 2021
    Co-Authors: Milos Stanojevic, Mark Steedman, Shohini Bhattasali, Donald G Dunagan, Luca Campanelli, Jonathan Brennan, John Hale
    Abstract:

    Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model. With a view towards sharpening this picture, we model the time course of hemodynamic activity within the brain during an extended episode of naturalistic language comprehension using Combinatory Categorial Grammar (CCG). CCG has well-defined incremental parsing algorithms, surface compositional semantics, and can explain long-range dependencies as well as complicated cases of coordination. We find that CCG-derived predictors improve a regression model of fMRI time course in six language-relevant brain regions, over and above predictors derived from Context-free phrase structure. Adding a special Revealing operator to CCG parsing, one designed to handle right-adjunction, improves the fit in three of these regions. This evidence for CCG from neuroimaging bolsters the more general case for mildly Context-Sensitive Grammars in the cognitive science of language.

  • On natural language processing and plan recognition
    IJCAI International Joint Conference on Artificial Intelligence, 2007
    Co-Authors: Christopher W. Geib, Mark Steedman
    Abstract:

    The research areas of plan recognition and natu- ral language parsing share many common features and even algorithms. However, the dialog between these two disciplines has not been effective. Specif- ically, significant recent results in parsing mildly Context Sensitive Grammars have not been lever- aged in the state of the art plan recognition sys- tems. This paper will outline the relations between natural language processing(NLP) and plan recog- nition(PR), argue that each of them can effectively inform the other, and then focus on key recent re- search results in NLP and argue for their applica- bility to PR.

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

  • inductive inference machines that can refute hypothesis spaces
    Algorithmic Learning Theory, 1993
    Co-Authors: Yasuhito Mukouchi, Setsuo Arikawa
    Abstract:

    This paper intends to give a theoretical foundation of machine discovery from examples. We point out that the essence of a logic of machine discovery is the refutability of the entire spaces of hypotheses. We discuss this issue in the framework of inductive inference of length-bounded elementary formal systems (EFS's, for short), which are a kind of logic programs over strings of characters and correspond to Context-Sensitive Grammars in Chomsky hierarchy.

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

  • BIOINFORMATICS Pair Stochastic Tree Adjoining Grammars for Aligning and Predicting Pseudoknot RNA Structures
    2016
    Co-Authors: Hiroshi Matsui, Kengo Sato, Yasubumi Sakakibara
    Abstract:

    Motivation: Since the whole genome sequences of many species have been determined, computational prediction of RNA secondary structures and computational identification of those non-coding RNA regions by comparative genomics become important. Therefore, more advanced alignment methods are required. Recently, an approach of structural alignment for RNA sequences has been introduced to solve these problems. Pair HMMs on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignment of RNA secondary structu-res, although PHMMTSs are incapable of handling pseudo-knots. On the other hand, tree adjoining Grammars (TAGs), a subclass of Context Sensitive Grammars, are suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. Results: We propose pair stochastic tree adjoining Grammars (PSTAGs) for aligning and predicting RNA secondary structu-res including a simple type of pseudoknots which can repre-sent most of known pseudoknot structures. First, we extend PHMMTSs defined on alignment of “trees ” to PSTAGs defined on alignment of “TAG trees ” which represent derivation pro-cess of TAGs and are functionally equivalent to derived trees of TAGs. Then, we develop an efficient dynamic program-ming algorithm of PSTAGs for obtaining an optimal structural alignment including pseudoknots. We implement the PSTAG algorithm and demonstrate the properties of algorithm by using it to align and predict several small pseudoknot struc-tures. We believe that our implemented program based on PSTAGs is the first grammar-based and practically executa-ble software for comparative analyses of RNA pseudoknot structures, and further non-coding RNAs. Availability: The source code of PSTAG and its web applica-tion are available a

  • pair stochastic tree adjoining Grammars for aligning and predicting pseudoknot rna structures
    Bioinformatics, 2005
    Co-Authors: Hiroshi Matsui, Kengo Sato, Yasubumi Sakakibara
    Abstract:

    Motivation: Since the whole genome sequences of many species have been determined, computational prediction of RNA secondary structures and computational identification of those non-coding RNA regions by comparative genomics become important. Therefore, more advanced alignment methods are required. Recently, an approach of structural alignment for RNA sequences has been introduced to solve these problems. Pair hidden Markov models on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignment of RNA secondary structures, although PHMMTSs are incapable of handling pseudoknots. On the other hand, tree adjoining Grammars (TAGs), a subclass of Context-Sensitive Grammars, are suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. Results: We propose pair stochastic TAGs (PSTAGs) for aligning and predicting RNA secondary structures including a simple type of pseudoknot which can represent most known pseudoknot structures. First, we extend PHMMTSs defined on alignment of 'trees' to PSTAGs defined on alignment of 'TAG trees' which represent derivation processes of TAGs and are functionally equivalent to derived trees of TAGs. Then, we develop an efficient dynamic programming algorithm of PSTAGs for obtaining an optimal structural alignment including pseudoknots. We implement the PSTAG algorithm and demonstrate the properties of the algorithm by using it to align and predict several small pseudoknot structures. We believe that our implemented program based on PSTAGs is the first grammar-based and practically executable software for comparative analyses of RNA pseudoknot structures, and, further, non-coding RNAs. Availability: The source code of PSTAG and its web application are available at http://phmmts.dna.bio.keio.ac.jp/pstag/ Contact: yasu@bio.keio.ac.jp