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Precursor miRNAs (pre-miRNAs) are usually extracted to obtain quite a lot of global and intrinsic folding features that include some redundant and useless features. Therefore,it is essential to select the most representative feature subset,which contributes to improve the classification efficiency.We propose a novel feature selection method based on genetic algorithm.The information gain of feature...
Recent advances in microarray technology allow an unprecedented view of the biochemical mechanisms contained within a cell. Deriving useful information from the data is still proving to be a difficult task. In this paper a novel method based on a multi-objective genetic algorithm that discovers relevant sets of genes and uses a neural network to create rules using the evolved genes is described. This...
A novel algorithm, the k-means clustering algorithm based on immune genetic algorithm (KMCIGA) is put forward. To improve the Genetic operators, the conception of concentration in the immune algorithm and the dynamic chromosome coding are used. Strategies and methods of selecting vaccines and constructing an immune operator are also given. KMCIGA is illustrated to be obviously better than the traditional...
In the past decade, DNA microarray technologies have had a great impact on cancer genome research; this technology has been viewed as a promising approach in predicting cancer classes and prognosis outcomes. In this paper, we proposed two systematic methods which can predict cancer classification. By applying the genetic algorithm gene selection (GAGS) method in order to find an optimal information...
Bandyopadhyay and Pal proposed an improved genetic search strategy, GACD, involving partitioning the chromosomes into two classes, and defining a restricted form of the crossover operator between the two classes. The GACD can be applied to many multi-dimensional pattern recognition problems. However, their GACD suffered from two kinds of problems, i.e. "sampling-without-replacement" and...
Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can...
Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we...
We propose an algorithm for generating diagnostic rules for cardiac diagnoses. Diagnostic rules are presented in decision tree forms that are created by genetic programming. The algorithm was tested by using cardiac single proton emission computed tomography images. In comparisons with other six well-known methods including support vector machine, LogitBoost, logistic regression, linear discriminant...
Fold recognition based on sequence-derived features is a complex classification problem and usually sequence-derived features are exploited using proper machine learning techniques. Here we adress the task of fold recognition on a protein similarity network (PSN) basis. We construct a protein sequence similarity network (PSeSN) using a set of 125 sequence-derived features for an available set of 311...
Classification is a major task in the gene sequence analysis. Based on the general principle of artificial immune system, this paper first constructed a classifier which inducted antibody-antigen identification, immune colonel reproduction, hypermutation, affinity mature and the network suppression, by simulating how the antigens stimulate the immune network and how the immune network responds. Then,...
In order to solve NP-type problems, a lot of methods have been developed by exploiting the properties of systems in the nature. Genetic algorithms, ant colony optimization, artificial neural networks, artificial immune systems, etc. are this-type algorithms. In this study, tree growing up algorithm inspired by growing up of trees was used to estimate the secondary structure of proteins. Proteins 1bbe,...
Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. The gene (feature) selection...
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