MOTIVATION: A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning. RESULTS: We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse-engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes and Inflammatory/Innate Response. AVAILABILITY: Data and code are available on the Gene Expression Omnibus and github. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: We present a suite of on-line tools to design candidate vaccine proteins, and to assess antigen potential, using coverage of k-mers (as proxies for potential T-cell epitopes) as a metric. The vaccine design tool uses the recently published ‘mosaic’ method to generate protein sequences optimized for coverage of high-frequency k-mers; the coverage-assessment tools facilitate coverage comparisons for any potential antigens. To demonstrate these tools, we designed mosaic protein sets for B-clade HIV-1 Gag, Pol and Nef, and compared them to antigens used in a recent human vaccine trial. Availability: http://hiv.lanl.gov/content/sequence/MOSAIC/ Contact: [email protected] Supplementary information: Supplementary data are available at ftp://ftp-t10.lanl.gov/pub/btk/WebToolsData

We describe a modification of the standard support vector machine (SVM) classifier that exploits the tendency for spatially contiguous pixels to be similarly classified. A quadratic term characterizing the spatial correlations in a multispectral image is added into the standard SVM optimization criterion. The mathematical structure of the SVM programming problem is retained, and the solution can be expressed in terms of the ordinary SVM solution with a modified dot product. The spatial correlations are characterized by a "contiguity matrix" psi whose computation does not require labeled data; thus, the method provides a way to use a mix of labeled and unlabeled data. We present numerical comparisons of classification performance for this contiguity-enhanced SVM against a standard SVM for two multispectral data sets.

Time-series novelty detection, or anomaly detection, refers to the automatic identification of novel or abnormal events embedded in normal time-series points. Although it is a challenging topic in data mining, it has been acquiring increasing attention due to its huge potential for immediate applications. In this paper, a new algorithm for time-series novelty detection based on one-class support vector machines (SVMs) is proposed. The concepts of phase and projected phase spaces are first introduced, which allows us to convert a time-series into a set of vectors in the (projected) phase spaces. Then we interpret novel events in time-series as outliers of the "normal" distribution of the converted vectors in the (projected) phase spaces. One-class SVMs are employed as the outlier detectors. In order to obtain robust detection results, a technique to combine intermediate results at different phase spaces is also proposed. Experiments on both synthetic and measured data are presented to demonstrate the promising performance of the new algorithm.

In this paper, we present a new framework for online novelty detection on temporal sequences. This framework include a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm. Experiments on both synthetic and real world data are performed to demonstrate the promising performance of our proposed detection algorithm.

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented. In both scenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.

Abstract — We have developed an automated feature detection/classification system, called Genie (GENetic Imagery Exploitation), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. Genie is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multi-spectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of Genie with several conventional supervised classification techniques, for a number of classification tasks using multi-spectral remotely-sensed imagery.

Automated methods for designing robot controllers based on machine-learning techniques have shown great promise when applied to simple robot tasks, but in order to 'scale up' to more complicated problems they will require assistance from human experts, a process that is often called 'robot shaping'. In this paper, the difficult problem of learning how to visually track moving objects is examined. It is shown that through the use of shaping techniques, this intractable learning problem can be made solvable. Controllers are evolved in simulation and then transferred to a real robot.