33321332
Learning Information Extraction Rules for Protein Annotation from Unannotated Corpora
As the number of published papers on proteins increases rapidly, manual protein annotation for biological sequence databases faces the problem of catching up with the speed of publication. Automated information extraction for protein annotation offers a solution to this problem. Generally, information extraction tasks have relied on the availability of pre-defined templates as well as annotated corpora. However, in many real world applications, it is difficult to fulfill this requirement; only relevant sentences for target domains can be easily collected. At the same time, other resources can be harnessed to compensate for this difficulty: natural language processing provides reliable tools for syntactic text analysis, and in bio-medical domains, there is a large amount of background knowledge available, e.g., in the form of ontologies. In this paper, we present a method for learning information extraction rules without pre-defined templates or labor-intensive pre-annotation by exploiting various types of background knowledge in an inductive logic programming framework.
6458402
METIS: multiple extraction techniques for informative sentences
SUMMARY
METIS is a web-based integrated annotation tool. From single query sequences, the PRECIS component allows users to generate structured protein family reports from sets of related Swiss-Prot entries. These reports may then be augmented with pertinent sentences extracted from online biomedical literature via support vector machine and rule-based sentence classification systems.


AVAILABILITY
http://umber.sbs.man.ac.uk/dbbrowser/metis/
18235969
Learning Relations and Information Extraction Rules for Protein Annotation
Protein annotation is a task that describes protein X in terms of topic Y Until now, most of protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever rapidly, manual annotation becomes difficult, and there is increasing need to automate the protein annotation process. Recently, Information Extraction (IE) has been used to solve this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real world domains such as the biomedical domain. In this paper, we describe an IE system which requires only sentences labeled relevant or not to a given topic by domain experts.
2306091
Classifying Protein Fingerprints
Protein fingerprints are groups of conserved motifs which can be used as diagnostic signatures to identify and characterize collections of protein sequences. These fingerprints are stored in the prints database after time-consuming annotation by domain experts who must first of all determine the fingerprint type, i.e., whether a fingerprint depicts a protein family, superfamily or domain. To alleviate the annotation bottleneck, a system called PRECIS has been developed which automatically generates prints records, provisionally stored in a supplement called preprints. One limitation of PRECIS is that its classification heuristics, handcoded by proteomics experts, often misclassify fingerprint type; their error rate has been estimated at 40%. This paper reports on an attempt to build more accurate classifiers based on information drawn from the fingerprints themselves and from the SWISS-PROT database. Extensive experimentation using 10-fold cross-validation led to the selection of a model combiing the ReliefF feature selector with an SVM-RBF learner. The final models error rate was estimated at 14.1% on a blind test set, representing a 26% accuracy gain over PRECIS handcrafted rules.
16087620
A Corpus-Based Learning Method of Compound Noun Indexing Rules for Korean
In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.
2891290
Sequence-driven features for prediction of subcellular localization of proteins
Prediction of the cellular location of a protein plays an important role in inferring the function of the protein. Feature extraction is a critical part in prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present a method for extracting useful features from protein sequence data. The method employs local and global pairwise sequence alignment scores as well as composition-based features. Five different features are used for training support vector machines (SVMs) separately and a weighted majority voting makes a final decision. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. Comparing the prediction accuracy of various feature extraction methods, provides a biological insight into the location of targeting information. Our experimental results confirm that our feature extraction methods are very useful for predicting subcellular localization of proteins.
488943
A Combinatorial Toolbox for Protein Sequence Design and Landscape Analysis in the Grand Canonical Model
In modern biology, one of the most important research problems is to understand how protein sequences fold into their native 3D structures. To investigate this problem at a high level, one wishes to analyze the protein landscapes, i.e., the structures of the space of all protein sequences and their native 3D structures. Perhaps the most basic computational problem at this level is to take a target 3D structure as input and design a fittest protein sequence with respect to one or more fitness functions of the target 3D structure. We develop a toolbox of combinatorial techniques for protein landscape analysis in the Grand Canonical model of Sun, Brem, Chan, and Dill. The toolbox is based on linear programming, network flow, and a linear-size representation of all minimum cuts of a network. It not only substantially expands the network flow technique for protein sequence design in Kleinberg's seminal work but also is applicable to a considerably broader collection of computational problems than those considered by Kleinberg. We have used this toolbox to obtain a number of efficient algorithms and hardness results. We have further used the algorithms to analyze 3D structures drawn from the Protein Data Bank and have discovered some novel relationships between such native 3D structures and the Grand Canonical model.
2642560
Forward-secure commercial key escrow systems
Key escrow system has been studied widely in recent years to reduce the gap between the needs for individual privacy and the needs for public security. In this paper, we survey three popular key escrow products of Netscape's, VeriSign's and Entrust's. And we also propose forward-secure KESs (Key Escrow Systems) and analyze their security.
13639231
Extracting contrastive information from negation patterns in biomedical literature
Expressions of negation in the biomedical literature often encode information of contrast as a means for explaining significant differences between the objects that are so contrasted. We show that such information gives additional insights into the nature of the structures and/or biological functions of these objects, leading to valuable knowledge for subcategorization of protein families by the properties that the involved proteins do not have in common. Based on the observation that the expressions of negation employ mostly predictable syntactic structures that can be characterized by subclausal coordination and by clause-level parallelism, we present a system that extracts such contrastive information by identifying those syntactic structures with natural language processing techniques and with additional linguistic resources for semantics. The implemented system shows the performance of 85.7% precision and 61.5% recall, including 7.7% partial recall, or an F score of 76.6. We apply the system to the biological interactions as extracted by our biomedical information-extraction system in order to enrich proteome databases with contrastive information.
250579
Live neural network formations on electronic chips
Abstract Live neuron behavior on an electronic chip was recorded by Nippon Telephone & Telegraph (NTT) with a time-lapsed video under the microscope. The dissociated chick embryonic brain neurons (telencephalic neurons) were cultured, and individual neurons were placed on silicon glass plates deposited with metal oxide strips about 10 μm width for possible electronegativity neurite guidance. We have attempted to map biological neural networks (BNN) to artificial neural networks (ANN) to determine whether the connectivity patterns dictate the information processing efficiency, or vice versa. By an image processing technique, we discovered the smallest size of intelligent BNN from those NTT video tapes that happened to fail their neurite guidance experiments. The neurite growth connecting other neurons was accomplished intentionally through selective paring in time rather than mechanically following the external guidance. Such a dynamic interconnection has inspired a model of energy landscaping, of which a general convergence theorem that has unified Hopfield-Cohen-Grossberg and Werbos-Rumelhart theorems is given, and a nonlinear Hebbian learning is derived capable for self-determination of ANN architectures.