An embedded two-layer feature selection approach for microarray data analysis

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.<br /

A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis

An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.<br /

An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.<br /

Examining scholars' activity on a Chinese blogging and academic social network site

This study analyzes scholars' activity on a popular academic blogging and social network site (SNS) in China, ScienceNet. We collected blogs, comments, recommendations, likes, and user profile information and analyzed how different groups of users differ in their patterns of activity with others in different disciplines, professional ranks, and universities. Results indicate that: 1) scholars in management and mathematics are active in recommending and commenting other users; 2) scholars from well-known universities and research institutes often receive more comments and recommendations than those from other universities; 3) scholars with higher professional ranks are more active, and are more likely to receive comments and recommendations from others. These findings suggest different usage of academic SNS among scholars of different disciplines, ranks, and universities

A Literature Review Study: a Meta-Analysis and Investigation of the Frequency Pattern of Point Selection Based on Clinical Studies of Acupuncture for Postoperative Treatment of the Anterior Cruciate Ligament

A Literature Review Study: a Meta-Analysis and Investigation of the Frequency Pattern of Point Selection Based on Clinical Studies of Acupuncture for Postoperative Treatment of the Anterior Cruciate Ligamen

An agent-based hybrid system for microarray data analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.<br /

Clinical Progress of Acupuncture in the Treatment of ACL after Reconstructive Surgery

Anterior cruciate ligament (ACL) injury of the knee is one of the common sports injuries, and ACL reconstruction is currently the mainstream treatment. However,ACL reconstruction often produces postoperative complications such as swelling, pain, muscle atrophy, joint adhesion and stifness, and timely interventional rehabilitation is needed for patients to recover the expected level. Studies have shown that acupuncture treatment can regulate infammatory factors and related signalling pathways, etc., and has obvious efcacy in joint rehabilitation after ACL reconstruction. The authors collated clinical reports on the use of acupuncture therapy such as general acupuncture, electroacu-puncture, moxibustion with acupuncture, snap needling, and ethnic acupuncture in the rehabilitation process after ACL reconstruction in re-cent years, to explore the feasibility and efectiveness of the intervention of acupuncture therapy, with the aim of providing a more systematic reference for the treatment ofACL injury in the future clinic after surgery

Interpretable deep learning in single-cell omics

Recent developments in single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them `black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions. We anticipate this review to bring together the single-cell and machine learning research communities to foster future development and application of interpretable deep learning in single-cell omics research

A particle swarm based hybrid system for imbalanced medical data sampling

BackgroundMedical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.ResultsOne important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.ConclusionThe experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.<br /