Background: Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from pro...

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Method...

We consider the problem of classification in noisy, high-dimensional, and class-imbalanced protein datasets. In order to design a complete classification system, we use a three-stage machine learning framework consisting of a feature selection stage, a method addressing noise and class-imbalance, and a method for combining biologically related tasks through a prior-knowledge based clustering. I...

MOTIVATION Building an accurate protein classification system depends critically upon choosing a good representation of the input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data--examples with known 3D structures, organized into structural classes--w...

Szeged 2008 " In theory, there is no difference between theory and practice. But, in practice, there is. " Jan L.A. van de Snepscheut

This chapter reviews analytical tools currently in use for protein classification, and gives an overview of the C. elegans proteome. Computational analysis of proteins relies heavily on hidden Markov models of protein families. Proteins can also be classified by predicted secondary or tertiary structures, hydrophobic profiles, compositional biases, or size ranges. Strictly orthologous protein f...

Motivation: Proteins are comprised of one or several domains. Such domains can be classified into families according to their biological function. Whereas sequencing technologies have advanced immensely in recent years, there are no matching computational tools for large-scale determination of protein domains and their boundaries. The present paper addresses the challenge of developing computat...

The sheer volume of data today and its expected growth over the next years are some of the key challenges in data mining and knowledge discovery applications. Besides the huge number of data samples that are collected and processed, the high dimensional nature of data arising in many applications causes the need to develop effective and efficient techniques that are able to deal with this massi...

1 MOTIVATION Many human neurological and cognitive disorders are caused in part by aberrancies in the composition and volume densities of synapses in the brain [1]. The ability to analyze the underlying causes of such diseases would be greatly enhanced by detailed knowledge about the ratio and quantities of different types of synapses that are present in specific regions of a patient’s brain. I...