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.
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.
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.
14157019
Projections of Binary Linear Codes onto Larger Fields
We study certain projections of binary linear codes onto larger fields. These projections include the well-known projection of the extended Golay [24,12,8] code onto the hexacode over $\mbox{GF}(4)$ and the projection of the Reed--Muller code R(2,5) onto the unique self-dual [8,4,4] code over $\mbox{GF}(4)$. We give a characterization of these projections, and we construct several binary linear codes which have best known optimal parameters, for instance, [20,11,5], [40,22,8], [48,21,12], and [72,31,16]. We also relate the automorphism group of a quaternary code to that of the corresponding binary code.
18028546
Divide and conquer martingales and the number of triangles in a random graph
The goal of this paper is to present a novel application of a recent and useful martingale inequality. As an illustration, we prove an essentially sharp bound for the probability that a random graph contains significantly more triangles than expected.
32992816
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.
1231793
An effective data clustering measure for temporal selection and projection queries
Abstract Temporal databases (TDBs) allow users to record and retrieve time-varying data objects. Since TDBs usually manage a huge amount of underlying data objects, efficient disk accesses are essential for fast response time in temporal query processing. Data clustering is one of the most effective techniques that can improve performance of TDB systems. However, clustering measures for conventional data objects are not appropriate to temporal data objects because it is important to exploit temporal properties of underlying data objects and temporal queries as data clustering criteria. In this paper, we propose a data clustering measure called temporal affinity that can be used for effective temporal data clustering. The temporal affinity, which is based on the semantics of temporal operators, reflects the closeness among temporal data objects with respect to temporal query processing. We perform experiments to show the effectiveness of the proposed temporal data clustering measure. The experimental results indicate that a data clustering method based on the temporal affinity works better than other methods.