Supervised learning of short and high-dimensional temporal sequences for life science measurements

The analysis of physiological processes over time are often given by spectrometric or gene expression profiles over time with only few time points but a large number of measured variables. The analysis of such temporal sequences is challenging and only few methods have been proposed. The information can be encoded time independent, by means of classical expression differences for a single time point or in expression profiles over time. Available methods are limited to unsupervised and semi-supervised settings. The predictive variables can be identified only by means of wrapper or post-processing techniques. This is complicated due to the small number of samples for such studies. Here, we present a supervised learning approach, termed Supervised Topographic Mapping Through Time (SGTM-TT). It learns a supervised mapping of the temporal sequences onto a low dimensional grid. We utilize a hidden markov model (HMM) to account for the time domain and relevance learning to identify the relevant feature dimensions most predictive over time. The learned mapping can be used to visualize the temporal sequences and to predict the class of a new sequence. The relevance learning permits the identification of discriminating masses or gen expressions and prunes dimensions which are unnecessary for the classification task or encode mainly noise. In this way we obtain a very efficient learning system for temporal sequences. The results indicate that using simultaneous supervised learning and metric adaptation significantly improves the prediction accuracy for synthetically and real life data in comparison to the standard techniques. The discriminating features, identified by relevance learning, compare favorably with the results of alternative methods. Our method permits the visualization of the data on a low dimensional grid, highlighting the observed temporal structure

Parametric t-Distributed Stochastic Exemplar-centered Embedding

Parametric embedding methods such as parametric t-SNE (pt-SNE) have been widely adopted for data visualization and out-of-sample data embedding without further computationally expensive optimization or approximation. However, the performance of pt-SNE is highly sensitive to the hyper-parameter batch size due to conflicting optimization goals, and often produces dramatically different embeddings with different choices of user-defined perplexities. To effectively solve these issues, we present parametric t-distributed stochastic exemplar-centered embedding methods. Our strategy learns embedding parameters by comparing given data only with precomputed exemplars, resulting in a cost function with linear computational and memory complexity, which is further reduced by noise contrastive samples. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed methods significantly outperform pt-SNE in terms of robustness, visual effects, and quantitative evaluations.Comment: fixed typo

Adversarial Edit Attacks for Tree Data

Many machine learning models can be attacked with adversarial examples, i.e. inputs close to correctly classified examples that are classified incorrectly. However, most research on adversarial attacks to date is limited to vectorial data, in particular image data. In this contribution, we extend the field by introducing adversarial edit attacks for tree-structured data with potential applications in medicine and automated program analysis. Our approach solely relies on the tree edit distance and a logarithmic number of black-box queries to the attacked classifier without any need for gradient information. We evaluate our approach on two programming and two biomedical data sets and show that many established tree classifiers, like tree-kernel-SVMs and recursive neural networks, can be attacked effectively.Comment: accepted at the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL

The Impact of Myoglobin on the Efficiency of the Therapeutic Effect of Laser Radiation

The results of numerical simulation of the interaction of laser optical radiation with myoglobin and oxymyoglobin in muscle tissue are presented. It is shown that the photodissociation of $MbO_2$ can adjust the concentration of oxygen in muscle tissue, directly in the irradiation zone. The criteria of the effectiveness of oxygenation combined effect on the biological tissue with laser radiation at two wavelengths are considered

Out-of-Sample Kernel Extensions for Nonparametric Dimensionality Reduction

Contains fulltext : 103597.pdf (publisher's version ) (Closed access)ESANN 2012 : 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 25-27 April 201

Clinical and Experimental Study of Gaalas Phototherapy for Tемрoromandibular Disorders

The objective of this study was to test the clinical effectiveness of the gallium-aluminum-arsenide laser (GaAlAs; 785 nm) and superluminiscent diodes (633 nm) phototherapy (MedX 1100 device) for the treatment of patients with temporomandibular disorders and myofascial pain syndrome. The results demonstrated a positive effect in pain relief. A significant reduction (p < 0.05) in the level of pain was observed for the temporomandibular joint and for the masseter muscles using paired samples t-test and Wilcoxon signed rank test. The experimental study on pork muscle samples showed that a) the main part of laser radiation is absorbed by the tissue in thin layer of 3-4 mm, b) in the spectral region 650-950 nm the intensity of light penetration is about 0.2-0.25 percent of the initial light intensity