Prediction of protein-protein interaction sites using an ensemble method

Background  

Prediction of protein-protein interaction sites is one of the most challenging and intriguing problems in the field of computational biology. Although much progress has been achieved by using various machine learning methods and a variety of available features, the problem is still far from being solved.     

Constraint Logic Programming approach to protein structure prediction

Background  

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems.     

Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests

Background  

Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example Kruskal's algorithm. The efficiency of the algorithm makes it tractable for high-dimensional problems.     

A genetic approach for building different alphabets for peptide and protein classification

Background  

In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.     

A next-generation sequencing method for overcoming the multiple gene copy problem in polyploid phylogenetics,applied to <Emphasis Type="Italic">Poa</Emphasis> grasses

Background  

Polyploidy is important from a phylogenetic perspective because of its immense past impact on evolution and its potential future impact on diversification, survival and adaptation, especially in plants. Molecular population genetics studies of polyploid organisms have been difficult because of problems in sequencing multiple-copy nuclear genes using Sanger sequencing. This paper describes a method for sequencing a barcoded mixture of targeted gene regions using next-generation sequencing methods to overcome these problems.     

Effects of dependence in high-dimensional multiple testing problems

Background  

We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices.     

Laser-induced hyperthermia of nanoshell mediated vascularized tissue – A numerical study

Laser-induced hyperthermia treatment of tumor in a 2-D axisymmetric tissue embedded with moderate size (100–150 µm) blood vessels is studied. Laser absorption is enhanced by embedding gold–silica nanoshells in the tumor. Heat transfer in the tissue is modeled using Weinbaum–Jiji bioheat transfer equation. With laser irradiation, the volumetric radiation is accounted in the governing bioheat equation. Radiative information needed in the bioheat equation is calculated using the discrete ordinate method, and the coupled bioheat-radiation equation is solved using the finite volume method. Effects of power density, laser exposure time, beam radius, diameter of blood vessel and volume fractions of nanoshells on temperature spread in the tissue are analyzed.     

Robust algorithm for arrhythmia classification in ECG using extreme learning machine

Background  

Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.     

An evolutionary method for learning HMM structure: prediction of protein secondary structure

Background  

The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used much for this problem, as the complexity of the task makes manual design of HMMs difficult. Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs).     

Similarity searches in genome-wide numerical data sets

   

We present psi-square, a program for searching the space of gene vectors. The program starts with a gene vector, i.e., the set of measurements associated with a gene, and finds similar vectors, derives a probabilistic model of these vectors, then repeats search using this model as a query, and continues to update the model and search again, until convergence. When applied to three different pathway-discovery problems, psi-square was generally more sensitive and sometimes more specific than the ad hoc methods developed for solving each of these problems before.     

Tooth loss and its relationship with protein intake by elderly Brazilians—A structural equation modelling approach

Objective

This study aimed at assessing the relationship between self‐perceived tooth loss and wearing dentures, on the one hand, and the consumption of protein, on the other hand, among the elderly population of Botucatu, SP. Food consumption tends to decrease with ageing, especially protein intake, and one of the causes could be the precariousness of oral health. Several risk factors associated with deficient dietary protein intake have been identified, namely greater physical dependence, reduced caloric intake and food insecurity, but no studies have analysed whether tooth loss and prostheses interfere with protein intake.

Methods

An interview was conducted among 365 elderly individuals, in which we examined oral health‐related quality of life (OHRQoL) as the only latent variable, in a 24‐hour nutritional assessment dietary recall repeated 3 times, conducted in person by a trained nutritionist and also performed an analysis of nutritional needs using the Nutrition Data System Research (NDSR) Program.

Results

The structural equation model, performed using Stata v.14, showed that lack of teeth (standardised coefficient [SC] = 0.21, P < .001), and prosthesis use (SC = ?0.21, P < .001) was associated with OHRQoL. Lack of teeth had a direct effect on the consumption of animal protein (SC = 0.08, P = .02), a strong total effect on animal protein intake (SC = 0.51, P = .04) and a medium effect on total protein intake (SC = 0.20, P = .03), adjusted for confounders (depression and medical problems).

Conclusion

Tooth loss had a strong and significant total effect on animal protein intake and a medium effect on total protein intake among elderly Brazilians.     

Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE

Background:  

In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin. However, the performance can be easily affected by noise and outliers, when it is applied to noisy, small sample size microarray data.     

Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm

Background  

Myoelectric control of a robotic manipulator may be disturbed by failures due to disconnected electrodes, interface impedance changes caused by movements, problems in the recording channel and other various noise sources. To correct these problems, this paper presents two fusing techniques, Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF), both based on the myoelectric signal variance as selecting criterion.     

Whole-proteome phylogeny of large dsDNA viruses and parvoviruses through a composition vector method related to dynamical language model

Background  

The vast sequence divergence among different virus groups has presented a great challenge to alignment-based analysis of virus phylogeny. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignment could not be directly applied to the whole-genome comparison and phylogenomic studies of viruses. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among the alignment-free methods, a dynamical language (DL) method proposed by our group has successfully been applied to the phylogenetic analysis of bacteria and chloroplast genomes.     

Building multiclass classifiers for remote homology detection and fold recognition

Background  

Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems.     

Production of <Emphasis Type="Italic">in vitro</Emphasis> amplified DNA pseudolibraries and high-throughput cDNA target amplification

Background  

Many structural biology- and high-throughput laboratories experience the acquisition of multiple cDNAs from different sources as a rather time- and resource-consuming procedure. The techniques presented here solve these problems.     

Altered sense of Agency in children with spastic cerebral palsy

Background  

Children diagnosed with spastic Cerebral Palsy (CP) often show perceptual and cognitive problems, which may contribute to their functional deficit. Here we investigated if altered ability to determine whether an observed movement is performed by themselves (sense of agency) contributes to the motor deficit in children with CP.     

A discriminative method for protein remote homology detection and fold recognition combining Top-<Emphasis Type="Italic">n</Emphasis>-grams and latent semantic analysis

Background  

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.     

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

Background  

Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data.     

Revealing divergent evolution, identifying circular permutations and detecting active-sites by protein structure comparison

Background  

Protein structure comparison is one of the most important problems in computational biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and protein docking.