Digging into acceptor splice site prediction : an iterative feature selection approach

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction. We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature. The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets

Identify error-sensitive patterns by decision tree

© Springer International Publishing Switzerland 2015. When errors are inevitable during data classification, finding a particular part of the classification model which may be more susceptible to error than others, when compared to finding an Achilles’ heel of the model in a casual way, may help uncover specific error-sensitive value patterns and lead to additional error reduction measures. As an initial phase of the investigation, this study narrows the scope of problem by focusing on decision trees as a pilot model, develops a simple and effective tagging method to digitize individual nodes of a binary decision tree for node-level analysis, to link and track classification statistics for each node in a transparent way, to facilitate the identification and examination of the potentially “weakest” nodes and error-sensitive value patterns in decision trees, to assist cause analysis and enhancement development. This digitization method is not an attempt to re-develop or transform the existing decision tree model, but rather, a pragmatic node ID formulation that crafts numeric values to reflect the tree structure and decision making paths, to expand post-classification analysis to detailed node-level. Initial experiments have shown successful results in locating potentially high-risk attribute and value patterns; this is an encouraging sign to believe this study worth further exploration

Effect of Temperature on the Compressibility Behavior of Glass Fiber-bentonite Mixture

In line with the global need for energy, various renewable and clean energy sources have become increasingly popular. Heat piles, buried high-voltage cables, and high-level nuclear waste (HLW) storage areas are examples of energy structures. Since these energy structures emit high temperatures and increase the temperature of the surrounding soil, investigating and improving the thermo-hydro-mechanical behavior of soils under high temperatures has become essential. Bentonite is a clay with high montmorillonite content, which is preferred as a buffer material due to its high swelling capacity, low hydraulic conductivity and chemical resistance.In the present study, a series of laboratory experiments were conducted to investigate the volumetric deformation behavior of bentonite at 80 °C. Tests were performed on the samples kept at 80 °C to observe the effect of high temperature on the volumetric deformation of bentonite in short and long (6 months and 1 year) terms. Glass fiber (GF) was added to bentonite due to its favorable engineering properties at high temperatures. The results have shown that high temperature increased the compressibility of bentonite mixtures while decreasing swelling deformation. The compressibility of the mixtures after curing decreased. Compared to room temperature (RT), the compression strain increased by 22.8% at 80 °C. With 6-months curing at 80 °C, it further increased by up to 33.2%. However, after 1-year curing, a slight decrease of 4.9% was observed. GF significantly increased the swelling behavior of bentonite at RT. However, this effect decreased at high temperature

Uncertainty in context-aware systems: A case study for intelligent environments

Data used be context-aware systems is naturally incomplete and not always reflect real situations. The dynamic nature of intelligent environments leads to the need of analysing and handling uncertain information. Users can change their acting patterns within a short space of time. This paper presents a case study for a better understanding of concepts related to context awareness and the problem of dealing with inaccurate data. Through the analysis of identification of elements that results in the construction of unreliable contexts, it is aimed to identify patterns to minimize incompleteness. Thus, it will be possible to deal with flaws caused by undesired execution of applications.Programa Operacional Temático Factores de Competitividade (POCI-01-0145-

Electrophysiological Properties of Motor Neurons in a Mouse Model of Severe Spinal Muscular Atrophy: In Vitro versus In Vivo Development

We examined the electrophysiological activity of motor neurons from the mouse model of severe spinal muscular atrophy (SMA) using two different methods: whole cell patch clamp of neurons cultured from day 13 embryos; and multi-electrode recording of ventral horns in spinal cord slices from pups on post-natal days 5 and 6. We used the MED64 multi-electrode array to record electrophysiological activity from motor neurons in slices from the lumbar spinal cord of SMA pups and their unaffected littermates. Recording simultaneously from up to 32 sites across the ventral horn, we observed a significant decrease in the number of active neurons in 5–6 day-old SMA pups compared to littermates. Ventral horn activity in control pups is significantly activated by serotonin and depressed by GABA, while these agents had much less effect on SMA slices. In contrast to the large differences observed in spinal cord, neurons cultured from SMA embryos for up to 21 days showed no significant differences in electrophysiological activity compared to littermates. No differences were observed in membrane potential, frequency of spiking and synaptic activity in cells from SMA embryos compared to controls. In addition, we observed no difference in cell survival between cells from SMA embryos and their unaffected littermates. Our results represent the first report on the electrophysiology of SMN-deficient motor neurons, and suggest that motor neuron development in vitro follows a different path than in vivo development, a path in which loss of SMN expression has little effect on motor neuron function and survival

Estimating Level of Engagement from Ocular Landmarks

E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of – social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners’ motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users’ head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders

Effects of noise on convergent game learning dynamics

We study stochastic effects on the lagging anchor dynamics, a reinforcement learning algorithm used to learn successful strategies in iterated games, which is known to converge to Nash points in the absence of noise. The dynamics is stochastic when players only have limited information about their opponents' strategic propensities. The effects of this noise are studied analytically in the case where it is small but finite, and we show that the statistics and correlation properties of fluctuations can be computed to a high accuracy. We find that the system can exhibit quasicycles, driven by intrinsic noise. If players are asymmetric and use different parameters for their learning, a net payoff advantage can be achieved due to these stochastic oscillations around the deterministic equilibrium.Comment: 17 pages, 8 figure

A peridynamic based machine learning model for one-dimensional and two-dimensional structures

With the rapid growth of available data and computing resources, using data-driven models is a potential approach in many scientific disciplines and engineering. However, for complex physical phenomena that have limited data, the data-driven models are lacking robustness and fail to provide good predictions. Theory-guided data science is the recent technology that can take advantage of both physics-driven and data-driven models. This study presents a novel peridynamics based machine learning model for one and two-dimensional structures. The linear relationships between the displacement of a material point and displacements of its family members and applied forces are obtained for the machine learning model by using linear regression. The numerical procedure for coupling the peridynamic model and the machine learning model is also provided. The numerical procedure for coupling the peridynamic model and the machine learning model is also provided. The accuracy of the coupled model is verified by considering various examples of a one-dimensional bar and two-dimensional plate. To further demonstrate the capabilities of the coupled model, damage prediction for a plate with a pre-existing crack, a two-dimensional representation of a three-point bending test, and a plate subjected to dynamic load are simulated