Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression.

Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings.

Unsupervised selection of RV144 HIV vaccine-induced antibody features correlated to natural killer cell-mediated cytotoxic reactions.

HIV-1 vaccine injection has been shown less effective due to the diversity of antigens. Increasing the knowledge of the associations between immune system and virus would ultimately result in producing effective vaccines against HIV-1 virus. To increase the understanding of immunological information, computational models can be utilised to construct predictive models.

Comparison of unsupervised feature selection methods for high-dimensional regression problems in prediction of peptide binding affinity.

Identification of robust set of predictive features is one of the most important steps in the construction of clustering, classification and regression models from many thousands of features. Although there have been various attempts to select predictive feature sets from high-dimensional data sets in classification and clustering, there is a limited attempt to study it in regression problems. As semi-supervised and supervised feature selection methods tend to identify noisy features in addition to discriminative variables, unsupervised feature selection methods (USFSMs) are generally regarded as more unbiased approach.

The quantitative prediction of HLA-B*2705 peptide binding affinities using Support Vector Regression to gain insights into its role for the Spondyloarthropathies.

Computational methods are increasingly utilised in many immunoinformatics problems such as the prediction of binding affinity of peptides. The peptides could provide valuable insight into the drug design and development such as vaccines. Moreover, they can be used to diagnose diseases.

Support vector-based Takagi-Sugeno fuzzy system for the prediction of binding affinity of peptides.

High dimensional, complex and non-linear nature of the post-genome data often adversely affects the performance of predictive models. There are two methods that have been widely used to model such non-linear systems, namely Fuzzy System (FS) and Support Vector Machine (SVM). FS is good at modelling uncertainty and yielding a set of interpretable IF-THEN rules, but suffers from the curse of dimensionality whereas SVM is a method that has been shown to effectively deal with large number of dimensions leading to better generalization ability.

Clustering-based spot segmentation of cDNA microarray images.

Microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarray image processing has been implemented. Clustering-based methods, fuzzy c-means and k-means, have been applied for the segmentation step that separates the spots from the background.