Structure Information
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Angela D. Friederici - One of the best experts on this subject based on the ideXlab platform.
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word category and verb argument Structure Information in the dynamics of parsing
Cognition, 2004Co-Authors: Stefan Frisch, Anja Hahne, Angela D. FriedericiAbstract:One of the core issues in psycholinguistic research concerns the relationship between word category Information and verb-argument Structure (e.g. transitivity) Information of verbs in the process of sentence parsing. In two experiments (visual versus auditory presentation) using event-related brain potentials (ERPs), we addressed this question by presenting sentences in which the critical word simultaneously realized both a word category and a transitivity violation. ERPs for sentences with both types of violation clustered with the patterns for sentences with a word category violation only, but were different from the patterns elicited by argument Structure violations in isolation, since only the latter elicited an N400 ERP component. The finding that an argument Structure violation evoked an N400 only if the phrase Structure of the respective sentence was correct suggests that a successful integration of the word category Information of a verb functionally precedes the application of its argument Structure Information.
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Word category and verb–argument Structure Information in the dynamics of parsing
Cognition, 2004Co-Authors: Stefan Frisch, Anja Hahne, Angela D. FriedericiAbstract:One of the core issues in psycholinguistic research concerns the relationship between word category Information and verb-argument Structure (e.g. transitivity) Information of verbs in the process of sentence parsing. In two experiments (visual versus auditory presentation) using event-related brain potentials (ERPs), we addressed this question by presenting sentences in which the critical word simultaneously realized both a word category and a transitivity violation. ERPs for sentences with both types of violation clustered with the patterns for sentences with a word category violation only, but were different from the patterns elicited by argument Structure violations in isolation, since only the latter elicited an N400 ERP component. The finding that an argument Structure violation evoked an N400 only if the phrase Structure of the respective sentence was correct suggests that a successful integration of the word category Information of a verb functionally precedes the application of its argument Structure Information.
G. Deleage - One of the best experts on this subject based on the ideXlab platform.
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Identification of related proteins with weak sequence identity using secondary Structure Information.
Protein Science, 2001Co-Authors: C. Geourjon, C. Combet, C. Blanchet, G. DeleageAbstract:Molecular modeling of proteins is confronted with the problem of finding homologous proteins, especially when few identities remain after the process of molecular evolution. Using even the most recent methods based on sequence identity detection, structural relationships are still difficult to establish with high reliability. As protein Structures are more conserved than sequences, we investigated the possibility of using protein secondary Structure comparison (observed or predicted Structures) to discriminate between related and unrelated proteins sequences in the range of 10%-30% sequence identity. Pairwise comparison of secondary Structures have been measured using the structural overlap (Sov) parameter. In this article, we show that if the secondary Structures likeness is >50%, most of the pairs are structurally related. Taking into account the secondary Structures of proteins that have been detected by BLAST, FASTA, or SSEARCH in the noisy region (with high E: value), we show that distantly related protein sequences (even with
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identification of related proteins with weak sequence identity using secondary Structure Information
Protein Science, 2001Co-Authors: C. Geourjon, C. Combet, C. Blanchet, G. DeleageAbstract:Molecular modeling of proteins is confronted with the problem of finding homologous proteins, especially when few identities remain after the process of molecular evolution. Using even the most recent methods based on sequence identity detection, structural relationships are still difficult to establish with high reliability. As protein Structures are more conserved than sequences, we investigated the possibility of using protein secondary Structure comparison (observed or predicted Structures) to discriminate between related and unrelated proteins sequences in the range of 10%–30% sequence identity. Pairwise comparison of secondary Structures have been measured using the structural overlap (Sov) parameter. In this article, we show that if the secondary Structures likeness is >50%, most of the pairs are structurally related. Taking into account the secondary Structures of proteins that have been detected by BLAST, FASTA, or SSEARCH in the noisy region (with high E value), we show that distantly related protein sequences (even with <20% identity) can be still identified. This strategy can be used to identify three-dimensional templates in homology modeling by finding unexpected related proteins and to select proteins for experimental investigation in a structural genomic approach, as well as for genome annotation.
Bernhard Sick - One of the best experts on this subject based on the ideXlab platform.
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semi supervised active learning for support vector machines a novel approach that exploits Structure Information in data
Information Sciences, 2018Co-Authors: Adrian Calma, Tobias Reitmaier, Bernhard SickAbstract:Abstract In today’s Information society more and more data emerges, e.g., in social networks, technical applications, or business practice. Companies try to commercialize these data using data mining or machine learning methods. For this purpose, the data are often categorized or classified, but many times at high (monetary or temporal) cost. An effective approach to reduce these cost is to apply any kind of active learning (AL) methods, as AL controls the training process of a classifier by specifically querying individual data points (samples), which are then labeled (e.g., provided with class memberships) by a domain expert. However, an analysis of current AL research shows that AL still has some shortcomings. In particular, Structure Information given by the spatial pattern of the (un)labeled data in the input space of a classification (e.g., cluster Information), is used in an insufficient way. To meet this challenge, this article presents a new approach for AL based on support vector machines (SVM) for classification. Structure Information is captured by means of probabilistic models that are iteratively improved at run-time when label Information becomes available. The probabilistic models are then considered in a selection strategy based on distance, density, diversity, and distribution Information for AL (4DS strategy) and in a particular kernel function for SVM (Responsibility Weighted Mahalanobis kernel). With 20 benchmark data sets and with the MNIST data set it is shown that our new solution yields significantly better results than state-of-the-art methods.
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Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data
arXiv: Machine Learning, 2016Co-Authors: Tobias Reitmaier, Adrian Calma, Bernhard SickAbstract:In our today's Information society more and more data emerges, e.g.~in social networks, technical applications, or business applications. Companies try to commercialize these data using data mining or machine learning methods. For this purpose, the data are categorized or classified, but often at high (monetary or temporal) costs. An effective approach to reduce these costs is to apply any kind of active learning (AL) methods, as AL controls the training process of a classifier by specific querying individual data points (samples), which are then labeled (e.g., provided with class memberships) by a domain expert. However, an analysis of current AL research shows that AL still has some shortcomings. In particular, the Structure Information given by the spatial pattern of the (un)labeled data in the input space of a classification model (e.g.,~cluster Information), is used in an insufficient way. In addition, many existing AL techniques pay too little attention to their practical applicability. To meet these challenges, this article presents several techniques that together build a new approach for combining AL and semi-supervised learning (SSL) for support vector machines (SVM) in classification tasks. Structure Information is captured by means of probabilistic models that are iteratively improved at runtime when label Information becomes available. The probabilistic models are considered in a selection strategy based on distance, density, diversity, and distribution (4DS strategy) Information for AL and in a kernel function (Responsibility Weighted Mahalanobis kernel) for SVM. The approach fuses generative and discriminative modeling techniques. With 20 benchmark data sets and with the MNIST data set it is shown that our new solution yields significantly better results than state-of-the-art methods.
David Baker - One of the best experts on this subject based on the ideXlab platform.
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accurate protein Structure modeling using sparse nmr data and homologous Structure Information
Proceedings of the National Academy of Sciences of the United States of America, 2012Co-Authors: James M Thompson, Nikolaos G Sgourakis, G Liu, Paolo Rossi, Yuefeng Tang, J L Mills, Thomas Szyperski, Gaetano T Montelione, David BakerAbstract:While Information from homologous Structures plays a central role in X-ray Structure determination by molecular replacement, such Information is rarely used in NMR Structure determination because it can be incorrect, both locally and globally, when evolutionary relationships are inferred incorrectly or there has been considerable evolutionary structural divergence. Here we describe a method that allows robust modeling of protein Structures of up to 225 residues by combining , 13C, and 15N backbone and 13Cβ chemical shift data, distance restraints derived from homologous Structures, and a physically realistic all-atom energy function. Accurate models are distinguished from inaccurate models generated using incorrect sequence alignments by requiring that (i) the all-atom energies of models generated using the restraints are lower than models generated in unrestrained calculations and (ii) the low-energy Structures converge to within 2.0 Å backbone rmsd over 75% of the protein. Benchmark calculations on known Structures and blind targets show that the method can accurately model protein Structures, even with very remote homology Information, to a backbone rmsd of 1.2–1.9 Å relative to the conventional determined NMR ensembles and of 0.9–1.6 Å relative to X-ray Structures for well-defined regions of the protein Structures. This approach facilitates the accurate modeling of protein Structures using backbone chemical shift data without need for side-chain resonance assignments and extensive analysis of NOESY cross-peak assignments.
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coupled prediction of protein secondary and tertiary Structure
Proceedings of the National Academy of Sciences of the United States of America, 2003Co-Authors: Jens Meiler, David BakerAbstract:The strong coupling between secondary and tertiary Structure formation in protein folding is neglected in most Structure prediction methods. In this work we investigate the extent to which nonlocal interactions in predicted tertiary Structures can be used to improve secondary Structure prediction. The architecture of a neural network for secondary Structure prediction that utilizes multiple sequence alignments was extended to accept low-resolution nonlocal tertiary Structure Information as an additional input. By using this modified network, together with tertiary Structure Information from native Structures, the Q3-prediction accuracy is increased by 7–10% on average and by up to 35% in individual cases for independent test data. By using tertiary Structure Information from models generated with the rosetta de novo tertiary Structure prediction method, the Q3-prediction accuracy is improved by 4–5% on average for small and medium-sized single-domain proteins. Analysis of proteins with particularly large improvements in secondary Structure prediction using tertiary Structure Information provides insight into the feedback from tertiary to secondary Structure.
Stefan Frisch - One of the best experts on this subject based on the ideXlab platform.
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word category and verb argument Structure Information in the dynamics of parsing
Cognition, 2004Co-Authors: Stefan Frisch, Anja Hahne, Angela D. FriedericiAbstract:One of the core issues in psycholinguistic research concerns the relationship between word category Information and verb-argument Structure (e.g. transitivity) Information of verbs in the process of sentence parsing. In two experiments (visual versus auditory presentation) using event-related brain potentials (ERPs), we addressed this question by presenting sentences in which the critical word simultaneously realized both a word category and a transitivity violation. ERPs for sentences with both types of violation clustered with the patterns for sentences with a word category violation only, but were different from the patterns elicited by argument Structure violations in isolation, since only the latter elicited an N400 ERP component. The finding that an argument Structure violation evoked an N400 only if the phrase Structure of the respective sentence was correct suggests that a successful integration of the word category Information of a verb functionally precedes the application of its argument Structure Information.
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Word category and verb–argument Structure Information in the dynamics of parsing
Cognition, 2004Co-Authors: Stefan Frisch, Anja Hahne, Angela D. FriedericiAbstract:One of the core issues in psycholinguistic research concerns the relationship between word category Information and verb-argument Structure (e.g. transitivity) Information of verbs in the process of sentence parsing. In two experiments (visual versus auditory presentation) using event-related brain potentials (ERPs), we addressed this question by presenting sentences in which the critical word simultaneously realized both a word category and a transitivity violation. ERPs for sentences with both types of violation clustered with the patterns for sentences with a word category violation only, but were different from the patterns elicited by argument Structure violations in isolation, since only the latter elicited an N400 ERP component. The finding that an argument Structure violation evoked an N400 only if the phrase Structure of the respective sentence was correct suggests that a successful integration of the word category Information of a verb functionally precedes the application of its argument Structure Information.