Discovering differences in gender-related skeletal muscle aging through the majority voting-based identification of differently expressed genes

Understanding gene function (GF) is still a significant challenge in system biology. Previously, several machine learning and computational techniques have been used to understand GF. However, these previous attempts have not produced a comprehensive interpretation of the relationship between genes and differences in both age and gender. Although there are several thousands of genes, very few differentially expressed genes play an active role in understanding the age and gender differences. The core aim of this study is to uncover new biomarkers that can contribute towards distinguishing between male and female according to the gene expression levels of skeletal muscle (SM) tissues. In our proposed multi-filter system (MFS), genes are first sorted using three different ranking techniques (t-test, Wilcoxon and Receiver Operating Characteristic (ROC)). Later, important genes are acquired using majority voting based on the principle that combining multiple models can improve the generalization of the system. Experiments were conducted on Micro Array gene expression dataset and results have indicated a significant increase in classification accuracy when compared with existing system

CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds

The classification of environmental sounds (ESC) has been increasingly studied in recent years. The main reason is that environmental sounds are part of our daily life, and associating them with our environment that we live in is important in several aspects as ESC is used in areas such as managing smart cities, determining location from environmental sounds, surveillance systems, machine hearing, environment monitoring. The ESC is however more difficult than other sounds because there are too many parameters that generate background noise in the ESC, which makes the sound more difficult to model and classify. The main aim of this study is therefore to develop more robust convolution neural networks architecture (CNN). For this purpose, 150 different CNN-based models were designed by changing the number of layers and values of their tuning parameters used in the layers. In order to test the accuracy of the models, the Urbansound8k environmental sound database was used. The sounds in this data set were first converted into an image format of 32x32x3. The proposed CNN model has yielded an accuracy of as much as 82.5% being higher than its classical counterpart. As there was not that much fine-tuning, the obtained accuracy has been found to be better and satisfactory compared to other studies on the Urbansound8k when both accuracy and computational complexity are considered. The results also suggest further improvement possible due to low complexity of the proposed CNN architecture and its applicability in real-world settings

Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression

Support vector machines have a wide use for the prediction problems in life sciences. It has been shown to offer more generalisation ability in input–output mapping. However, the performance of predictive models is often negatively influenced due to the complex, high-dimensional, and non-linear nature of the post-genome data. Soft computing methods can be used to model such non-linear systems. Fuzzy systems are one of the widely used methods of soft computing that model uncertainties. It is formed of interpretable rules aiding one to gain insight into applied model. This study is therefore concerned to provide more interpretable and efficient biological model with the development of a hybrid method that integrates the fuzzy system and support vector regression. In order to demonstrate the robustness of this new hybrid method, it is applied to the prediction of peptide binding affinity being one of the most challenging problems in the post-genomic era due to diversity in peptide families and complexity and high-dimensionality in the characteristic features of the peptides. Having used four different case studies, this hybrid predictive model has yielded the highest predictive power in all the four cases and achieved an improvement of as much as 34% compared to the results presented in the literature. Availability: Matlab scripts are available at https://github.com/sekerbigdatalab/tsksvr

Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression

Support vector machines have a wide use for the prediction problems in life sciences. It has been shown to offer more generalisation ability in input–output mapping. However, the performance of predictive models is often negatively influenced due to the complex, high-dimensional, and non-linear nature of the post-genome data. Soft computing methods can be used to model such non-linear systems. Fuzzy systems are one of the widely used methods of soft computing that model uncertainties. It is formed of interpretable rules aiding one to gain insight into applied model. This study is therefore concerned to provide more interpretable and efficient biological model with the development of a hybrid method that integrates the fuzzy system and support vector regression. In order to demonstrate the robustness of this new hybrid method, it is applied to the prediction of peptide binding affinity being one of the most challenging problems in the post-genomic era due to diversity in peptide families and complexity and high-dimensionality in the characteristic features of the peptides. Having used four different case studies, this hybrid predictive model has yielded the highest predictive power in all the four cases and achieved an improvement of as much as 34% compared to the results presented in the literature. Availability: Matlab scripts are available at https://github.com/sekerbigdatalab/tsksvr

A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models

Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria.With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models

Overlapping Clusters and Support Vector Machines Based Interval Type-2 Fuzzy System for the Prediction of Peptide Binding Affinity

In the post-genome era, it is becoming more complex to process high dimensional, low-instance available, and nonlinear biological datasets. This paper aims to address these characteristics as they have adverse effects on the performance of predictive models in bioinformatics. In this paper, an interval type-2 Takagi Sugeno fuzzy predictive model is proposed in order to manage high-dimensionality and nonlinearity of such datasets which is the common feature in bioinformatics. A new clustering framework is proposed for this purpose to simplify antecedent operations for an interval type-2 fuzzy system. This new clustering framework is based on overlapping regions between the clusters. The cluster analysis of partitions and statistical information derived from them has identified the upper and lower membership functions forming the premise part. This is further enhanced by adapting the regression version of support vector machines in the consequent part. The proposed method is used in experiments to quantitatively predict affinities of peptide bindings to biomolecules. This case study imposes a challenge in post-genome studies and remains an open problem due to the complexity of the biological system, diversity of peptides, and curse of dimensionality of amino acid index representation characterizing the peptides. Utilizing four different peptide binding affinity datasets, the proposed method resulted in better generalization ability for all of them yielding an improved prediction accuracy of up to 58.2% on unseen peptides in comparison with the predictive methods presented in the literature. Source code of the algorithm is available at https://github.com/sekerbigdatalab

Texture based characterization of sub-skin features by specified laser speckle effects at λ=650nm region

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Objective: The textural structure of “skin age” related sub-skin components enables us to identify and analyse their unique characteristics, thus making substantial progress towards establishing an accurate skin age model. Methods: This is achieved by a two stage process. First by the application of textural analysis using laser speckle imaging, which is sensitive to textural effects within the λ=650 nm spectral band region. In the second stage a Bayesian inference method is used to select attributes from which a predictive model is built. Results: This technique enables us to contrast different skin age models, such as the laser-speckle effect against the more widely used normal light (LED) imaging method, whereby it is shown that our laser speckle based technique yields better results. Conclusion: The method introduced here is non-invasive, low-cost and capable of operating in real-time; having the potential to compete against high-cost instrumentation such as confocal microscopy or similar imaging devices used for skin age identification purposes

Overlapping Clusters and Support Vector Machines based Interval Type-2 Fuzzy System for the Prediction of Peptide Binding Affinity

In the post-genome era, it is becoming more complex to process high-dimensional, low-instance available and nonlinear biological datasets. This study aims at addressing these characteristics as they have adverse effects on the performance of predictive models in bioinformatics. In this paper, an interval type-2 Takagi Sugeno fuzzy predictive model is proposed in order to manage high-dimensionality and nonlinearity of such datasets which is the common feature in bioinformatics. A new clustering framework is proposed for this purpose to simplify antecedent operations for an interval type-2 fuzzy system. This new clustering framework is based on overlapping regions between the clusters. The cluster analysis of partitions and statistical information derived from them have identified the upper and lower membership functions forming the premise part. This is further enhanced by adapting the regression version of support vector machines in the consequent part. The proposed method is used in experiments to quantitatively predict affinities of peptide bindings to biomolecules. This case study imposes a challenge in post-genome studies and remains an open problem due to the complexity of the biological system, diversity of peptides and curse of dimensionality of amino acid index representation characterising the peptides. Utilizing four different peptide binding affinity datasets, the proposed method resulted in better generalisation ability for all of them yielding an improved prediction accuracy of up to 58.2% on unseen peptides in comparison with the predictive methods presented in the literature

FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams

Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorised as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns are interesting in a data stream context as easy, fast, reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory and continuous model updates. In this paper, an approach for the extraction of emerging patterns in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential