Conference
Pattern Recognition in Bioinformatics
About: Pattern Recognition in Bioinformatics is an academic conference. The conference publishes majorly in the area(s): Support vector machine & Feature selection. Over the lifetime, 253 publications have been published by the conference receiving 1627 citations.
Papers
2 Nov 2011
TL;DR: This paper presents a method for global network alignment that is fast and robust, and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account, and finds that it outperforms alternative state-of-the-art methods with respect to quality and running time.
Abstract: Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. In this paper we present a method for global network alignment that is fast and robust, and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. It is based on an integer linear programming formulation, generalizing the well-studied quadratic assignment problem. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the software tool NATALIE 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative state-of-the-art methods with respect to quality and running time. An extended version of this paper including proofs and pseudo code is available at http://arxiv.org/pdf/1108.4358v1.
68 citations
22 Sep 2010
TL;DR: Experiments show that GraphGrepSX outperforms the compared systems on a very large collection of molecular data and reduces the size and the time for the construction of large database index and outper performs the most popular systems.
Abstract: Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, scientists require systems that search for all occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed.
This paper presents GraphGrepSX. The system implements efficient graph searching algorithms together with an advanced filtering technique.
GraphGrepSX is compared with SING, GraphFind, CTree and GCoding. Experiments show that GraphGrepSX outperforms the compared systems on a very large collection of molecular data. In particular, it reduces the size and the time for the construction of large database index and outperforms the most popular systems.
53 citations
1 Oct 2007
TL;DR: A method for automatic classification of computed tomography (CT) brain images of different head trauma types is presented and will be useful in building a content-based medical image retrieval system.
Abstract: A method for automatic classification of computed tomography (CT) brain images of different head trauma types is presented in this paper. The method has three major steps:1. The images are first segmented to find potential hemorrhage regions using ellipse fitting, background removal and wavelet decomposition technique; 2. For each region, features (such as area, major axis length, etc.) are extracted; 3. Each extracted feature is classified using machine learning algorithm; the images are then classified based on its component regions' classification. The automatic medical image classification will be useful in building a content-based medical image retrieval system.
52 citations
15 Oct 2008
TL;DR: A thorough performance study by example of RAxML, which is a widely used Bioinformatics application for large-scale phylogenetic inference under the Maximum Likelihood criterion, indicates that the ML function should be parallelized with MPI and Pthreads based on software engineering criteria as well as to enforce data locality.
Abstract: Emerging multi- and many-core computer architectures pose new challenges with respect to efficient exploitation of parallelism. In addition, it is currently not clear which might be the most appropriate parallel programming paradigm to exploit such architectures, both from the efficiency as well as software engineering point of view. Beyond that, the application of high performance computing techniques and the use of supercomputers will be essential to deal with the explosive accumulation of sequence data. We address these issues via a thorough performance study by example of RAxML, which is a widely used Bioinformatics application for large-scale phylogenetic inference under the Maximum Likelihood criterion. We provide an overview over the respective parallelization strategies with MPI, Pthreads, and OpenMP and assess performance for these approaches on a large variety of parallel architectures. Results indicate that there is no universally best-suited paradigm with respect to efficiency and portability of the ML function. Therefore, we suggest that the ML function should be parallelized with MPI and Pthreads based on software engineering criteria as well as to enforce data locality.
44 citations
8 Nov 2012
TL;DR: A principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.
Abstract: In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based ondeep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.
41 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2014 | 10 |
| 2013 | 25 |
| 2012 | 24 |
| 2011 | 31 |
| 2010 | 40 |
| 2009 | 38 |