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To date, there are no reliable markers for making an early diagnosis of schizophrenia before clinical diagnostic criteria are fully met. Neuroimaging and pattern classification techniques are promising tools towards predicting transition to schizophrenia. Here, we investigated the diagnostic performance of a combination of neuroanatomical and clinical data in predicting transition to schizophrenia...
Selection of reliable genes from micro array gene expression data is essential to carry out a diagnostic test and successful treatment. In this regard, a rough set based gene selection algorithm is developed recently to select genes from micro array data. In this paper, a fuzzy discretization method is proposed for rough set based gene selection algorithm to compute relevance and significance of continuous...
There are numerous application of DNA microar-rays. The most frequently used application is the one used to research gene expression. The aim is, among others, to detect symptoms of illnesses in tissues, to predict the predisposition for some illnesses and for personal identification. The huge amount of information obtained from DNA microarrays in range of tens of thousands of genes causes many difficulties...
To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) sub-types classification, we propose a novel integrative scheme utilizing PPI network, functional gene sets information, and mRNA profiling. The workflow of the proposed scheme includes three major steps: First, by combining protein-protein interaction network structure and gene co-expression relationship into...
We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments...
In this paper we present a new approach for classification of microarray data. Our methodology consists of two steps: an attribute selection, which aims at selection of the most informative genes, and a classification of expression profiles, which is carried out by weighted voting, a novel instance-based classifier based on Rough Set Theory. Attribute selection consists of two stages - initial selection,...
In this paper, we use the extreme Learning Machine (ELM) for cancer classification. We propose a two step method. In our two step feature selection method, we first use a gene importance ranking and then, finding the minimum gene subset form the top-ranked genes based on the first step. We tested our two step method in cancer datasets like Lymphoma data set and SRBCT data set. The results in the Lymphoma...
This paper studies the suitability of Extreme Learning Machines (ELM) for resolving bioinformatic and biomedical classification problems. In order to test their overall performance, an experimental study is presented based on five gene microarray datasets found in bioinformatic and biomedical domains. The Fast Correlation-Based Filter (FCBF) was applied in order to identify salient expression genes...
This paper deals with the advanced and developed methodology know for cancer multi classification using an Extreme Learning Machine (ELM) for microarray gene expression cancer diagnosis, this used for directing multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima; improper learning rate and over fitting commonly faced by iterative learning methods...
This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems...
Evolutionary algorithms have been actively applied to knowledge discovery, data mining and machine learning under the name of genetics-based machine learning (GBML). The main advantage of using evolutionary algorithms in those application areas is their flexibility: Various knowledge extraction criteria such as accuracy and complexity can be easily utilized as fitness functions. On the other hand,...
Data mining is a very active and rapidly growing research area in the field of computer science. Its goal is to obtain useful knowledge for users from a database. Association rule mining from a database is one of the most well-known data mining techniques. In general, a large number of if-then rules are extracted by specifying minimum support and confidence levels. They are, however, too complicated...
In data mining, the classification algorithms usually pursue more highly accuracy. It is based on the assumption that all misclassifications have the same cost. Obvious, the assumption is not suitable. By improving the encode/decode methods and taking different misclassification cost into account, this paper concerns a new cost-sensitive algorithm called CS-GE based on Gene Expression. The experimental...
This paper aims to challenge the problem of finding accurate and relevant rules for the task of classification. The scope is to improve the accuracy, or at least to provide a comparable accuracy measure, for classification algorithms implemented so far. Because the task of classification must be as accurate as possible, the paper proposes a method based on genetic algorithms to enhance the speed and...
This paper addresses the problem of distinguishing retroviruses from non-coding DNA sequences. Retroviruses have a distinctive reading frame structure that includes multiple reading frames that often overlap. This paper uses reading frame information generated from Fourier spectral analysis as input for Side Effect Machines (SEMs) that are evolved to create clusterings which separate the two types...
The functions of proteins are closely related to their subcellular locations. In the post-proteomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means. This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by using the information provided...
Gene selection is a challenging task in microarray data mining because a typical microarray dataset has only a small number of records while having thousands of attributes. This kind of dataset creates a high likelihood of finding false predictions that are due to chance. Finding the most relevant genes is often the key phase in building an accurate classification model. Irrelevant and redundant attributes...
This paper presents a study on the performance of attribute selection methods to be used with Ant-Miner algorithm for web text categorization. The new generated data set by each attribute selection method was classified with Ant-Miner to see the performance in terms of predictive accuracy and the number of rules generated. The results of classification were also compared to C4.5 algorithm.
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasized the requirement to multiple kernel learning, in order to boost the fitting accuracy...
The learning of Fuzzy Rule-Based Classification Systems for High-Dimensional problems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. In this work, we propose a fuzzy association rule-based classification method with genetic rule selection for high-dimensional problems to obtain an accurate and compact fuzzy rule-based classifier...
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