Knowledge discovery of heterogeneous data is an active topic in knowledge engineering.
Feature selection for heterogeneous data is an important part of effective data analysis …

Protein function prediction is one of the most challenging problems in the post-genomic era.
The number of newly identified proteins has been exponentially increasing with the …

In the postgenomic age, rapid growth in the number of sequence-known proteins has been
accompanied by much slower growth in the number of structure-known proteins (as a result …

Electrocardiogram (ECG) signal is critical to the classification of cardiac arrhythmia using
some machine learning methods. In practice, the ECG datasets are usually with multiple …

Real time monitoring of manufacturing processes using a single sensor often poses
significant challenge. Sensor fusion has thus been extensively investigated in recent years …

Given a regulatory pathway system consisting of a set of proteins, can we predict which
pathway class it belongs to? Such a problem is closely related to the biological function of …

Computational prediction of protein structural class based solely on sequence data remains
a challenging problem in protein science. Existing methods differ in the protein sequence …

The most important application of microarray in gene expression analysis is to classify the
unknown tissue samples according to their gene expression levels with the help of known …

An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was
proposed based upon combining a set of heterogeneous algorithms including support …

Knowledge of protein structural class can provide important information about its folding
patterns. Many approaches have been developed for the prediction of protein structural …