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Many feature subset selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. In this paper, we propose novel methods to find the relevant feature subset by using biologically-inspired algorithms such as Genetic Algorithm and Particle Swarm Optimization. We also propose a variant of the approach considering the significance of each feature. We verified the performance of the proposed methods by experiments with various real-world datasets. Our feature selection methods based on the biologically-inspired algorithms produced better performance than other methods in terms of the classification accuracy and the feature relevance. In particular, the modified method considering feature significance demonstrated even more improved performance. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient indexing mechanism for similarity search in high-dimensional multimedia database that quickly filter-outs the irrelevant objects using a novel indexing structure, called HBI (Hierarchical Bitmap Index). In this bitmap index, the feature (or attribute) value of object at each dimension is represented with a set of two bits each of which indicates whether it is relatively high ('11'), low ('00'), or neither ('01') compared to the feature values of other objects at a hierarchical organized interval. This approximation helps to reduce the CPU time of filtering process because many irrelevant objects could be simply excluded by just XORing the bitmaps of two objects. Upon experimental results, we find that there is an optimal number of bitmaps that keeps the filtering rate as high as possible while keeping the search time as short as possible. Furthermore, we also find that the similarity search using the proposed indexing mechanism is about 2-3 times faster than VA-File while guaranteeing the exact solutions. [ABSTRACT FROM AUTHOR]

Background: The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field. Results: Here, we suggest two kinds of kernel methods for genic interaction extraction, considering the structural aspects of sentences. First, we improve our prior dependency kernel by modifying the kernel function so that it can involve various substructures in terms of (1) e-walks, (2) partial match, (3) non-contiguous paths, and (4) different significance of substructures. Second, we propose the walk-weighted subsequence kernel to parameterize non-contiguous syntactic structures as well as semantic roles and lexical features, which makes learning structural aspects from a small amount of training data effective. Furthermore, we distinguish the significances of parameters such as syntactic locality, semantic roles, and lexical features by varying their weights. Conclusions: We addressed the genic interaction problem with various dependency kernels and suggested various structural kernel scenarios based on the directed shortest dependency path connecting two entities. Consequently, we obtained promising results over genic interaction data sets with the walk-weighted subsequence kernel. The results are compared using automatically parsed third party protein-protein interaction (PPI) data as well as perfectly syntactic labeled PPI data. [ABSTRACT FROM AUTHOR]

Gallium doped zinc oxide (GZO) thin films have been prepared by a simple sol-gel spin coating technique. XRD results showed the preferential c-axis orientation of the crystallites and the presence of the wurtzite phase of ZnO. A lowest resistivity of 3.3 × 10[?]3 O cm was obtained for the ZnO film doped with 2 at% of Ga after post-annealing at 500 °C for 45 min in a H2 atmosphere. All the films showed more than 80% of transparency in the entire visible region. Blue shifting of the optical band gap was observed with an increase in Ga doping, which can be explained on the basis of the Burstein-Moss effect. OLED devices were fabricated using 2 at% Ga-doped ZnO thin films as anodes. Preliminary results obtained demonstrated that spin-coated GZO films can be used as a promising TCO for optoelectronic device applications. [ABSTRACT FROM AUTHOR]

Motivation: Automatic knowledge discovery and efficient information access such as named entity recognition and relation extraction between entities have recently become critical issues in the biomedical literature. However, the inherent difficulty of the relation extraction task, mainly caused by the diversity of natural language, is further compounded in the biomedical domain because biomedical sentences are commonly long and complex. In addition, relation extraction often involves modeling long range dependencies, discontiguous word patterns and semantic relations for which the pattern-based methodology is not directly applicable. Results: In this article, we shift the focus of biomedical relation extraction from the problem of pattern extraction to the problem of kernel construction. We suggest four kernels: predicate, walk, dependency and hybrid kernels to adequately encapsulate information required for a relation prediction based on the sentential structures involved in two entities. For this purpose, we view the dependency structure of a sentence as a graph, which allows the system to deal with an essential one from the complex syntactic structure by finding the shortest path between entities. The kernels we suggest are augmented gradually from the flat features descriptions to the structural descriptions of the shortest paths. As a result, we obtain a very promising result, a 77.5 F-score with the walk kernel on the Language Learning in Logic (LLL) 05 genic interaction shared task. Availability: The used algorithms are free for use for academic research and are available from our Web site http://mllab.sogang.ac.kr/∼shkim/LLL05.tar.gz. Contact: shkim@lex.yonsei.ac.kr [ABSTRACT FROM AUTHOR]