Predicting breast cancer using an expression values weighted clinical classifier

Background: Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. Results: We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. Conclusions: Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems

Nonparametric Regression via StatLSSVM

We present a new MATLAB toolbox under Windows and Linux for nonparametric regression estimation based on the statistical library for least squares support vector machines (StatLSSVM). The StatLSSVM toolbox is written so that only a few lines of code are necessary in order to perform standard nonparametric regression, regression with correlated errors and robust regression. In addition, construction of additive models and pointwise or uniform confidence intervals are also supported. A number of tuning criteria such as classical cross-validation, robust cross-validation and cross-validation for correlated errors are available. Also, minimization of the previous criteria is available without any user interaction

Determining the region of origin of blood spatter patterns considering fluid dynamics and statistical uncertainties

Trajectory reconstruction in bloodstain pattern analysis is currently performed by assuming that blood drop trajectories are straight along directions inferred from stain inspection. Recently, several attempts have been made at reconstructing ballistic trajectories backwards, considering the effects of gravity and drag forces. Here, we propose a method to reconstruct the region of origin of impact blood spatter patterns that considers fluid dynamics and statistical uncertainties. The fluid dynamics relies on defining for each stain a range of physically possible trajectories, based on known physics of how drops deform, both in flight and upon slanted impact. Statistical uncertainties are estimated and propagated along the calculations, and a probabilistic approach is used to determine the region of origin as a volume most compatible with the backward trajectories. A publicly available data set of impact spatter patterns on a vertical wall with various impactor velocities and distances to target is used to test the model and evaluate its robustness, precision, and accuracy. Results show that the proposed method allows reconstruction of bloodletting events with distances between the wall and blood source larger than ∼1 m. The uncertainty of the method is determined, and its dependency on the distance between the blood source and the wall is characterized. Causes of error and uncertainty are discussed. The proposed method allows the consideration of stains indicating impact velocities that point downwards, which are typically not used for determining the height of the origin. Based on the proposed method, two practical recommendations on crime scene documentation are drawn

Sparse LS-SVMs with L0-norm minimization

This is an electronic version of the paper presented at the 19th European Symposium on Artificial Neural Networks, held in Bruges on 2011Least-Squares Support Vector Machines (LS-SVMs) have been successfully applied in many classification and regression tasks. Their main drawback is the lack of sparseness of the final models. Thus, a procedure to sparsify LS-SVMs is a frequent desideratum. In this paper, we adapt to the LS-SVM case a recent work for sparsifying classical SVM classifiers, which is based on an iterative approximation to the L0-norm. Experiments on real-world classification and regression datasets illustrate that this adaptation achieves very sparse models, without significant loss of accuracy compared to standard LS-SVMs or SVMs

Science and Innovation Strategic Policy Plans for the 2020s (EU,AU,UK): Will They Prepare Us for the World in 2050?

The world in 2050 will be very different from the world in which we currently live. An in-depth analysis suggests five main forces that will reshape the global economy and influence the “modus operandi” of the world in fifty years. These are defined as "the great rebalancing,"" the productivity imperative," "the global grid," "pricing the planet," and "the market state." This paper is a theoretical comparative review, backed by hypotheses methods, to illustrate the conceptual framework of how and if national and international policy makers and stockholders are preparing their communities (countries) for the challenges of the future. EU (2013) Horizon 2020 a major policy plan of the European Union which is built around the three focal pillars of "excellent science," "industrial leadership," and research to tackle "societal challenges," has decided to support research towards meeting seven broad challenges: Health; demographic changes and wellbeing; food security; sustainable agriculture and forestry; marine, maritime and inland water research; bio-economy; secure, clean, and efficient energy; smart, green, and integrated transport; climate action; environment, resource efficiency, and raw materials; inclusive, innovative, and reflective societies; and secure and innovative societies. The United Kingdom (2014) is aiming at being foremost in science and business. They plan to achieve this by prioritizing, nurturing scientific talent, investing in scientific infrastructure, supporting research, and catalyzing innovation through participation in global science and innovation. They intend on realizing these goals by taking the lead in accelerating the pace and seizing new opportunities. Support is needed to accommodate and foster higher levels of collaboration between disciplines, sectors, institutions, people, and countries. Australia (2014) declared the need for clear innovation priorities supported by a solid research foundation and strong linkages between business and research sectors, in order to increase the translation of knowledge into new products, processes and services. Also needed is a flexible workforce with the entrepreneurial skills to thrive in an environment of rapid technological change, and a regulatory environment that supports collaboration and creativity. Are these national objectives consistent with 2050 world challenges? What can we learn from national priorities and objectives? Are they driven by the science level and/or situation in a given country, or by previous investments in infrastructure and achievement status? Are they driven by geographic location or economic sustainability? Are the challenges common to all nations as global challenges? Are there any tools, strategy and solutions to meet those challenges? How will they influence science? And finally, does it reflect on science administration in this global world