Composition Vector Spaces as a New Type of Tri-Operational Algebras

The aim of this paper is to define and study the composition vector spaces as a type of tri-operational algebras. In this regard, by presenting nontrivial examples, it is emphasized that they are a proper generalization of vector spaces and their structure can be characterized by using linear operators. Additionally, some related properties about foundations, composition subspaces and residual elements are investigated. Moreover, it is shown how to endow a vector space with a composition structure by using bijective linear operators. Finally, more properties of the composition vector spaces are presented in connection with linear transformations.

Using Composite Classifier Systems to Predict Protein Locations

Predicting protein location is both an important and challenging topic in molecular and cellular biology. As we all know that the location of proteins sheds light upon the function of a protein whose location was uncertain. But the success of human genome project led to a protein sequence explosion. It is in a great need to develop a computational method for fast and reliably predicting the locations of proteins according to their primary sequences. In this paper, we use composite classifier system that was formed by a set of k-nearest neighbor (K-NN) classifiers, each of which is defined in a different pseudo amino composition vector. The location of a queried protein is determined by the outcome of voting among these constituent individual classifiers. It is show through the outcome that the classifier outperformed single classifier widely used in biological literature.