Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training ...

The gene expression data obtained from microarrays have shown useful in cancer classification. DNA microarray data have extremely high dimensionality compared to the small number of available samples. In this paper, we propose a novel system for selecting a set of genes for cancer classification. This system is based on a linear support vector machine and a genetic algorithm. To overcome the pr...

In this paper, we address the problem of extracting technical terms automatically from an unannotated corpus. We introduce a technology term tagger , that is based on Liblinear Support Vector Machines and employs linguistic features including Part of Speech tags and Dependency Structures, in addition to user feedback to perform the task of identification of technology related terms. Our experim...

We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages compared to Neural Networks and Support Vector Machines. The technique optimizes a set of human-readable classifiers with respect to some user-defined performance measure. We calculate the Vapnik-Chervonenkis dimension of this cla...

Microarrays are a new technology that allows biologists to better understand the interactions between diverse pathologic state at the gene level. However, the amount of data generated by these tools becomes problematic, even though data are supposed to be automatically analyzed (e.g., for diagnostic purposes). The issue becomes more complex when the expression data involve multiple states. We p...

Genome-wide association studies (GWAS) are observational of a large set genetic variants in an individual’s sample order to find if any these linked particular trait. In the last two decades, GWAS have contributed several new discoveries field genetics. This research presents novel methodology which can be applied to. It is mainly based on machine learning methodologies, algorithms and support ...