FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier.

Here, we propose a heuristic technique of data trimming for SVM termed (), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features.

A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.

Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data.

Early warning indicators for hock burn in broiler flocks.

Hock burn is a common disease of broiler chickens affecting flock welfare and farmer income. Here we use hierarchical logistic regression (HLR) models to identify risk factors for hock burn using data from 5895 flocks, collected over 3.5 years by a large UK broiler company.

Sequence-based Protein-Protein Interaction Prediction Optimized for Target Selection in Biological Experiments.

A set of protein pairs predicted to be interacting with high ratio of true positive is valuable for target selection in experiments like protein structure determination. Our goal in this paper is to investigate the problem of finding such a set of protein pairs in an organism by machine learning methods. Yeast genome was taken for this study and support vector machine was adopted as the classification model.

Ortholog clustering on a multipartite graph.

We present a method for automatically extracting groups of orthologous genes from a large set of genomes by a new clustering algorithm on a weighted multipartite graph. The method assigns a score to an arbitrary subset of genes from multiple genomes to assess the orthologous relationships between genes in the subset. This score is computed using sequence similarities between the member genes and the phylogenetic relationship between the corresponding genomes.

Optimal requantization of deep grayscale images and Lloyd-Max quantization.

The classic signal quantization problem was introduced by Lloyd. We formulate another, similar problem: The optimal mapping of digital fine grayscale images (such as 9-13 bits-per-pixel medical images) to a coarser scale (e.g.