THE ELECTROMYOGRAPHY CHARACTERISTICS BETWEEN DIFFERENT LEVELS OF SOCCER PLAYER ON INSTEP KICKING

This study improves kicking performance by comparing muscle activity between different levels of players. Twelve soccer players in the college cup in division I and division II volunteered to participate in this study. A VlCON motion capture system (200 Hz) was used to capture the kicking motion including back-swing and forward-swing. The Noraxon electromyography system was used to collect and analyze the percentage of maximum voluntary contraction on rectus femoris, bicepsfemoris, tibialis anterior, and gastrocnemius. The Mann-Whilney U (a = -05) test was applied to assess significant differences in this study. The results indicated that division II players had a greater percentage maximum voluntary contraction in tibialis anterior in the back-swing. To avoid stiff movements in soccer kicks, division II players should decrease muscle contraction in the tiblalis anterior In the back-swing

Predicting protein-protein interactions in unbalanced data using the primary structure of proteins

<p>Abstract</p> <p>Background</p> <p>Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.</p> <p>Results</p> <p>This study presents a method for PPI prediction based only on sequence information, which contributes in three aspects. First, we propose a probability-based mechanism for transforming protein sequences into feature vectors. Second, the proposed predictor is designed with an efficient classification algorithm, where the efficiency is essential for handling highly unbalanced datasets. Third, the proposed PPI predictor is assessed with several unbalanced datasets with different positive-to-negative ratios (from 1:1 to 1:15). This analysis provides solid evidence that the degree of dataset imbalance is important to PPI predictors.</p> <p>Conclusions</p> <p>Dealing with data imbalance is a key issue in PPI prediction since there are far fewer interacting protein pairs than non-interacting ones. This article provides a comprehensive study on this issue and develops a practical tool that achieves both good prediction performance and efficiency using only protein sequence information.</p

Using a kernel density estimation based classifier to predict species-specific microRNA precursors

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules participating in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches obtain more attention because that they can discover species-specific pre-miRNAs. Most <it>ab initio </it>approaches proposed novel features to characterize RNA molecules. However, there were fewer discussions on the associated classification mechanism in a miRNA predictor.</p> <p>Results</p> <p>This study focuses on the classification algorithm for miRNA prediction. We develop a novel <it>ab initio </it>method, miR-KDE, in which most of the features are collected from previous works. The classification mechanism in miR-KDE is the relaxed variable kernel density estimator (RVKDE) that we have recently proposed. When compared to the famous support vector machine (SVM), RVKDE exploits more local information of the training dataset. MiR-KDE is evaluated using a training set consisted of only human pre-miRNAs to predict a benchmark collected from 40 species. The experimental results show that miR-KDE delivers favorable performance in predicting human pre-miRNAs and has advantages for pre-miRNAs from the genera taxonomically distant to humans.</p> <p>Conclusion</p> <p>We use a novel classifier of which the characteristic of exploiting local information is particularly suitable to predict species-specific pre-miRNAs. This study also provides a comprehensive analysis from the view of classification mechanism. The good performance of miR-KDE encourages more efforts on the classification methodology as well as the feature extraction in miRNA prediction.</p

Genome-wide analysis of the cis-regulatory modules of divergent gene pairs in yeast

AbstractIn budding yeast, approximately a quarter of adjacent genes are divergently transcribed (divergent gene pairs). Whether genes in a divergent pair share the same regulatory system is still unknown. By examining transcription factor (TF) knockout experiments, we found that most TF knockout only altered the expression of one gene in a divergent pair. This prompted us to conduct a comprehensive analysis in silico to estimate how many divergent pairs are regulated by common sets of TFs (cis-regulatory modules, CRMs) using TF binding sites and expression data. Analyses of ten expression datasets show that only a limited number of divergent gene pairs share CRMs in any single dataset. However, around half of divergent pairs do share a regulatory system in at least one dataset. Our analysis suggests that genes in a divergent pair tend to be co-regulated in at least one condition; however, in most conditions, they may not be co-regulated

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these <it>ab initio </it>approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.</p> <p>Results</p> <p>This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G²DE) based classifier. The G²DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.</p> <p>Conclusion</p> <p>Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G²DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G²DE based predictor.</p

Real value prediction of protein solvent accessibility using enhanced PSSM features

<p>Abstract</p> <p>Background</p> <p>Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).</p> <p>Results</p> <p>This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction.</p> <p>Conclusion</p> <p>Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.</p

A study on the flexibility of enzyme active sites

<p>Abstract</p> <p>Background</p> <p>A common assumption about enzyme active sites is that their structures are highly conserved to specifically distinguish between closely similar compounds. However, with the discovery of distinct enzymes with similar reaction chemistries, more and more studies discussing the structural flexibility of the active site have been conducted.</p> <p>Results</p> <p>Most of the existing works on the flexibility of active sites focuses on a set of pre-selected active sites that were already known to be flexible. This study, on the other hand, proposes an analysis framework composed of a new data collecting strategy, a local structure alignment tool and several physicochemical measures derived from the alignments. The method proposed to identify flexible active sites is highly automated and robust so that more extensive studies will be feasible in the future. The experimental results show the proposed method is (a) consistent with previous works based on manually identified flexible active sites and (b) capable of identifying potentially new flexible active sites.</p> <p>Conclusions</p> <p>This proposed analysis framework and the former analyses on flexibility have their own advantages and disadvantage, depending on the cause of the flexibility. In this regard, this study proposes an alternative that complements previous studies and helps to construct a more comprehensive view of the flexibility of enzyme active sites.</p