Deep learning improves antimicrobial peptide recognition.

Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results: In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation: Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm.

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming.

Growing bacterial resistance to antibiotics is spurring research on utilizing naturally-occurring antimicrobial peptides (AMPs) as templates for novel drug design. While experimentalists mainly focus on systematic point mutations to measure the effect on antibacterial activity, the computational community seeks to understand what determines such activity in a machine learning setting. The latter seeks to identify the biological signals or features that govern activity. In this paper, we advance research in this direction through a novel method that constructs and selects complex sequence-based features which capture information about distal patterns within a peptide. Comparative analysis with state-of-the-art methods in AMP recognition reveals our method is not only among the top performers, but it also provides transparent summarizations of antibacterial activity at the sequence level. Moreover, this paper demonstrates for the first time the capability not only to recognize that a peptide is an AMP or not but also to predict its target selectivity based on models of activity against only Gram-positive, only Gram-negative, or both types of bacteria. The work described in this paper is a step forward in computational research seeking to facilitate AMP design or modification in the wet laboratory.

Effective automated feature construction and selection for classification of biological sequences.

BACKGROUND: Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features. METHODOLOGY: We present an algorithmic framework (EFFECT) for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not. RESULTS: To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification of a specific signal. Code, documentation, and all data for the applications presented here are provided for the community at http://www.cs.gmu.edu/~ashehu/?q=OurTools.

The effect of Remin Pro® on bleached enamel hardness: an in-vitro study.

INTRODUCTION: Remineralization as a treatment procedure has received a lot of attention both from clinicians as well as researchers. The objective of this study was to assess the effect of Remin Pro® on enamel microhardness after bleaching the teeth with McInnes bleaching agent using Vickers microhardness tester. MATERIALS AND METHODS: In this study, freshly extracted ten central incisors were taken which were subjected to baseline indentation by using Vickers microhardness indenter and then McInnes bleaching solution was applied for 5 min to demineralize these teeth. Remin Pro a newer remineralizing agent was applied for 7 days, which showed an increase in microhardness at the end of 7 days. RESULTS: The values were subjected for statistical analysis using paired t-test. All the samples showed a decrease in the microhardness after bleaching with McInnes solution. The decrease in mean hardness from baseline to demineralization was found to be statistically significant (P < 0.001). However, remineralizing the same tooth with Remin Pro for 7 days, showed an increase in hardness, which was found to be statistically significant (P < 0.05). CONCLUSION: McInnes bleaching agent decreases the microhardness of enamel and Remin Pro® used in the study causes an increase in the microhardness of bleached enamel.

Role of delayed light polymerization of a dual-cured composite base on marginal adaptation of class II posterior composite open-sandwich restoration.

OBJECTIVE: To determine the effect of delayed light polymerization of a dual-cured composite base material on the marginal adaptation of class II composite restoration. MATERIALS AND METHODS: 35 extracted human molar teeth were used to prepare class II mesio-occlusal or disto-occlusal slot preparations with gingival margins at the CEJ. The teeth were restored using an open-sandwich technique with a 2mm base increment of dual-cured composite, and divided into 5 groups based on the mode of the polymerization of the dual-cured composite base: Group A - self-cured after placement (5 mins), Group B - light-cured immediately after placement, Group C - light-cured 30 seconds after placement, Group D - light-cured 60 seconds after placement, Group E - light-cured 120 seconds after placement. Then a top layer of a light-cured composite resin is placed to complete the restoration. The teeth were thermocycled and immersed in 1% aqueous solution of methylene blue for 24 hours. Sectioning of the teeth and scoring under stereomicroscope was done. Data will be statistically evaluated using the kruskal wallis 1-way ANOVA. RESULTS: Statistical analysis using kruskal wallis 1-way ANOVA showed that the dual-cured composite light polymerized 1 minute after placement exhibited the least microleakage. CONCLUSION: Delayed light polymerization of the dual-cured composite base reduced the microleakage in class II open-sandwich restorations.

An evolutionary algorithm approach for feature generation from sequence data and its application to DNA splice site prediction.

Associating functional information with biological sequences remains a challenge for machine learning methods. The performance of these methods often depends on deriving predictive features from the sequences sought to be classified. Feature generation is a difficult problem, as the connection between the sequence features and the sought property is not known a priori. It is often the task of domain experts or exhaustive feature enumeration techniques to generate a few features whose predictive power is then tested in the context of classification. This paper proposes an evolutionary algorithm to effectively explore a large feature space and generate predictive features from sequence data. The effectiveness of the algorithm is demonstrated on an important component of the gene-finding problem, DNA splice site prediction. This application is chosen due to the complexity of the features needed to obtain high classification accuracy and precision. Our results test the effectiveness of the obtained features in the context of classification by Support Vector Machines and show significant improvement in accuracy and precision over state-of-the-art approaches.

A two-stage evolutionary approach for effective classification of hypersensitive DNA sequences.

Hypersensitive (HS) sites in genomic sequences are reliable markers of DNA regulatory regions that control gene expression. Annotation of regulatory regions is important in understanding phenotypical differences among cells and diseases linked to pathologies in protein expression. Several computational techniques are devoted to mapping out regulatory regions in DNA by initially identifying HS sequences. Statistical learning techniques like Support Vector Machines (SVM), for instance, are employed to classify DNA sequences as HS or non-HS. This paper proposes a method to automate the basic steps in designing an SVM that improves the accuracy of such classification. The method proceeds in two stages and makes use of evolutionary algorithms. An evolutionary algorithm first designs optimal sequence motifs to associate explicit discriminating feature vectors with input DNA sequences. A second evolutionary algorithm then designs SVM kernel functions and parameters that optimally separate the HS and non-HS classes. Results show that this two-stage method significantly improves SVM classification accuracy. The method promises to be generally useful in automating the analysis of biological sequences, and we post its source code on our website.