Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

AGN-Host Galaxy Connection: Morphology and Colours of X-ray Selected AGN at z < 2

The connection between AGN and their host galaxies has been widely studied over recent years, showing it to be of great importance for providing answers to some fundamental questions related with AGN fueling mechanisms, their formation and evolution. Using X-ray and one of the deepest broad-band optical data sets, we studied morphology and colours in relationship with X-ray properties for sources at redshifts z < 2.0, using a sample of 262 AGN in the Subaru/XMM-Newton Deep Survey (SXDS). Morphological classification was obtained using the galSVM code, one of the new methods useful especially when dealing with high-redshift sources and low-resolution data. Colour-magnitude diagrams were studied in relationship with redshift, morphology, X-ray obscuration, and X-ray-to-optical flux ratio. Finally, the significance of different regions was analysed on colour-magnitude diagrams, relating the observed properties of AGN populations with some models of their formation and evolution.Comment: 24 pages, 19 figures, accepted for publication in Astronomy&Astrophysic

The detection of globular clusters in galaxies as a data mining problem

We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods provided by the DAME (Data Mining & Exploration) web application, were tested and compared on the NGC1399 HST data described in Paolillo 2011. The best results were obtained using a Multi Layer Perceptron with Quasi Newton learning rule which achieved a classification accuracy of 98.3%, with a completeness of 97.8% and 1.6% of contamination. An extensive set of experiments revealed that the use of accurate structural parameters (effective radius, central surface brightness) does improve the final result, but only by 5%. It is also shown that the method is capable to retrieve also extreme sources (for instance, very extended objects) which are missed by more traditional approaches.Comment: Accepted 2011 December 12; Received 2011 November 28; in original form 2011 October 1

Classifying multisensor remote sensing data : Concepts, Algorithms and Applications

Today, a large quantity of the Earth’s land surface has been affected by human induced land cover changes. Detailed knowledge of the land cover is elementary for several decision support and monitoring systems. Earth-observation (EO) systems have the potential to frequently provide information on land cover. Thus many land cover classifications are performed based on remotely sensed EO data. In this context, it has been shown that the performance of remote sensing applications is further improved by multisensor data sets, such as combinations of synthetic aperture radar (SAR) and multispectral imagery. The two systems operate in different wavelength domains and therefore provide different yet complementary information on land cover. Considering the increase in revisit times and better spatial resolutions of recent and upcoming systems like TerraSAR-X (11 days; up to1 m), Radarsat-2 (24 days; up to 3 m), or RapidEye constellation (up to 1 day; 5 m), multisensor approaches become even more promising. However, these data sets with high spatial and temporal resolution might become very large and complex. Commonly used statistical pattern recognition methods are usually not appropriate for the classification of multisensor data sets. Hence, one of the greatest challenges in remote sensing might be the development of adequate concepts for classifying multisensor imagery. The presented study aims at an adequate classification of multisensor data sets, including SAR data and multispectral images. Different conventional classifiers and recent developments are used, such as support vector machines (SVM) and random forests (RF), which are well known in the field of machine learning and pattern recognition. Furthermore, the impact of image segmentation on the classification accuracy is investigated and the value of a multilevel concept is discussed. To increase the performance of the algorithms in terms of classification accuracy, the concept of SVM is modified and combined with RF for optimized decision making. The results clearly demonstrate that the use of multisensor imagery is worthwhile. Irrespective of the classification method used, classification accuracies increase by combining SAR and multispectral imagery. Nevertheless, SVM and RF are more adequate for classifying multisensor data sets and significantly outperform conventional classifier algorithms in terms of accuracy. The finally introduced multisensor-multilevel classification strategy, which is based on the sequential use of SVM and RF, outperforms all other approaches. The proposed concept achieves an accuracy of 84.9%. This is significantly higher than all single-source results and also better than those achieved on any other combination of data. Both aspects, i.e. the fusion of SAR and multispectral data as well as the integration of multiple segmentation scales, improve the results. Contrary to the high accuracy value by the proposed concept, the pixel-based classification on single-source data sets achieves a maximal accuracy of 65% (SAR) and 69.8% (multispectral) respectively. The findings and good performance of the presented strategy are underlined by the successful application of the approach to data sets from a second year. Based on the results from this work it can be concluded that the suggested strategy is particularly interesting with regard to recent and future satellite missions

Low Cost Open Source Modal Virtual Environment Interfaces Using Full Body Motion Tracking and Hand Gesture Recognition

Virtual environments provide insightful and meaningful ways to explore data sets through immersive experiences. One of the ways immersion is achieved is through natural interaction methods instead of only a keyboard and mouse. Intuitive tracking systems for natural interfaces suitable for such environments are often expensive. Recently however, devices such as gesture tracking gloves and skeletal tracking systems have emerged in the consumer market. This project integrates gestural interfaces into an open source virtual reality toolkit using consumer grade input devices and generates a set of tools to enable multimodal gestural interface creation. The AnthroTronix AcceleGlove is used to augment body tracking data from a Microsoft Kinect with fine grained hand gesture data. The tools are found to be useful as a sample gestural interface is implemented using them. The project concludes by suggesting studies targeting gestural interfaces using such devices as well as other areas for further research

Multivariate analysis of brain metabolism reveals chemotherapy effects on prefrontal cerebellar system when related to dorsal attention network

BACKGROUND: Functional brain changes induced by chemotherapy are still not well characterized. We used a novel approach with a multivariate technique to analyze brain resting state [(18) F]FDG-PET in patients with lymphoma, to explore differences on cerebral metabolic glucose rate between chemotherapy-treated and non-treated patients. METHODS: PET/CT scan was performed on 28 patients, with 14 treated with systemic chemotherapy. We used a support vector machine (SVM) classification, extracting the mean metabolism from the metabolic patterns, or networks, that discriminate the two groups. We calculated the correct classifications of the two groups using the mean metabolic values extracted by the networks. RESULTS: The SVM classification analysis gave clear-cut patterns that discriminate the two groups. The first, hypometabolic network in chemotherapy patients, included mostly prefrontal cortex and cerebellar areas (central executive network, CEN, and salience network, SN); the second, which is equal between groups, included mostly parietal areas and the frontal eye field (dorsal attention network, DAN). The correct classification membership to chemotherapy or not chemotherapy-treated patients, using only one network, was of 50% to 68%; however, when all the networks were used together, it reached 80%. CONCLUSIONS: The evidenced networks were related to attention and executive functions, with CEN and SN more specialized in shifting, inhibition and monitoring, DAN in orienting attention. Only using DAN as a reference point, indicating the global frontal functioning before chemotherapy, we could better classify the subjects. The emerging concept consists in the importance of the investigation of brain intrinsic networks and their relations in chemotherapy cognitive induced changes