scholarly journals Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

2020 ◽  
Vol 11 ◽  
Author(s):  
Nivedhitha Mahendran ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Sample Size ◽  
Gene Selection ◽  
Future Research ◽  
Minimal Sample ◽  
Genomic Studies ◽  
Selection Algorithms ◽  
Microarray Dna ◽  
Open Issues

Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription. It is the flow of information from DNA to RNA with enzymes’ help, and the end product is proteins and other biochemical molecules. Many technologies can capture Gene Expression from the DNA or RNA. One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. The issue in handling such a heavyweight dataset is that the learning model will be over-fitted. This problem should be addressed by reducing the dimension of the data source to a considerable amount. In recent years, Machine Learning has gained popularity in the field of genomic studies. In the literature, many Machine Learning-based Gene Selection approaches have been discussed, which were proposed to improve dimensionality reduction precision. This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. This study also lists out and briefly discusses the open issues in handling the high-dimension and less sample size data.

GENE SELECTION FOR CANCER CLASSIFICATION USING WRAPPER APPROACHES

2004 ◽  
Vol 18 (08) ◽  
pp. 1373-1390 ◽  
Author(s):  
ROSA BLANCO ◽  
PEDRO LARRAÑAGA ◽  
IÑAKI INZA ◽  
BASILIO SIERRA
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
Cancer Classification ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Selection Algorithms ◽  
Selection Of ◽  
General Method

Despite the fact that cancer classification has considerably improved, nowadays a general method that classifies known types of cancer has not yet been developed. In this work, we propose the use of supervised classification techniques, coupled with feature subset selection algorithms, to automatically perform this classification in gene expression datasets. Due to the large number of features of gene expression datasets, the search of a highly accurate combination of features is done by means of the new Estimation of Distribution Algorithms paradigm. In order to assess the accuracy level of the proposed approach, the naïve-Bayes classification algorithm is employed in a wrapper form. Promising results are achieved, in addition to a considerable reduction in the number of genes. Stating the optimal selection of genes as a search task, an automatic and robust choice in the genes finally selected is performed, in contrast to previous works that research the same types of problems.

Informative Gene Selection Based on Cost-Sensitive Fast Correlation-Based Feature Selection

2021 ◽  
Vol 16 ◽  
Author(s):  
Yueling Xiong ◽  
Qingqing Li ◽  
Peipei Wang ◽  
Mingquan Ye
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Candidate Gene ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Informative Gene ◽  
Gene Subset ◽  
Correlation Based Feature Selection ◽  
Selection Algorithms

Background: Informative gene selection is an essential step in performing tumor classification. However, it is difficult to select informative genes related to tumors from large-scale gene expression profiles because of their characteristics, such as high dimensionality, relatively small samples, and class imbalance, and some genes being superfluous and irrelevant. Objective: Many researchers analyze and process gene expression data to obtain classified gene subsets by using machine learning methods. However, the gene expression profiles of tumors often have massive computational challenges. In addition, when improving feature importance and classification accuracy, cost estimation is often ignored in traditional feature selection algorithms, which makes tumor classification more difficult. Method: In this study, a novel informative gene selection method based on cost-sensitive fast correlation-based feature selection (CS-FCBF) is proposed. Results: First, the symmetric uncertainty index is used to evaluate the correlation between informative genes and class labels, and then a large number of irrelevant and redundant genes are quickly filtered according to importance. Thereby, a candidate gene subset is generated. Second, cost-sensitive learning, which introduces the misclassification cost matrix and support vector machine attribute evaluation, is used to obtain the top-ranked gene subset with minimum misclassification loss. Finally, the candidate gene subset is optimized. Conclusion: This experiment was verified in eight independent tumor datasets. By comparing and analyzing CS-FCBF with another three hybrids of typical gene selection algorithms combined with cost-sensitive learning, we found that the method proposed in this study exhibited a better classification performance with fewer selected genes, which might provide guidance in tumor diagnosis and research.

scholarly journals A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yosef Masoudi-Sobhanzadeh ◽  
Habib Motieghader ◽  
Yadollah Omidi ◽  
Ali Masoudi-Nejad
Keyword(s):  
Machine Learning ◽  
Gene Selection ◽  
Critical Role ◽  
Parameter Tuning ◽  
Cancer Diagnostics ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Biological Applications ◽  
Minimum Number ◽  
Selection Algorithms

AbstractGene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.

scholarly journals Multi-view based integrative analysis of gene expression data for identifying biomarkers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zi-Yi Yang ◽  
Xiao-Ying Liu ◽  
Jun Shu ◽  
Hui Zhang ◽  
Yan-Qiong Ren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Small Sample Size ◽  
Small Sample ◽  
Integrative Analysis ◽  
Expression Data ◽  
Microarray Technology ◽  
Classification Problems

Abstract The widespread applications in microarray technology have produced the vast quantity of publicly available gene expression datasets. However, analysis of gene expression data using biostatistics and machine learning approaches is a challenging task due to (1) high noise; (2) small sample size with high dimensionality; (3) batch effects and (4) low reproducibility of significant biomarkers. These issues reveal the complexity of gene expression data, thus significantly obstructing microarray technology in clinical applications. The integrative analysis offers an opportunity to address these issues and provides a more comprehensive understanding of the biological systems, but current methods have several limitations. This work leverages state of the art machine learning development for multiple gene expression datasets integration, classification and identification of significant biomarkers. We design a novel integrative framework, MVIAm - Multi-View based Integrative Analysis of microarray data for identifying biomarkers. It applies multiple cross-platform normalization methods to aggregate multiple datasets into a multi-view dataset and utilizes a robust learning mechanism Multi-View Self-Paced Learning (MVSPL) for gene selection in cancer classification problems. We demonstrate the capabilities of MVIAm using simulated data and studies of breast cancer and lung cancer, it can be applied flexibly and is an effective tool for facing the four challenges of gene expression data analysis. Our proposed model makes microarray integrative analysis more systematic and expands its range of applications.

Antecedents of Family Conflict in Uganda

2020 ◽  
Vol 12 (3) ◽  
pp. 35-54
Author(s):  
Rogers Matama ◽  
Kezia H. Mkwizu
Keyword(s):  
Content Analysis ◽  
Social Identity ◽  
Sample Size ◽  
Social Identity Theory ◽  
Family Conflict ◽  
Qualitative Approach ◽  
Future Research ◽  
Family Conflicts ◽  
The Core ◽  
Quantitative Studies

The purpose of this study was to explore the antecedents of family conflict in Uganda. A qualitative approach was used in this study. A sample size of 139 participants provided data which was subjected to content analysis. Results revealed that the core themes associated with family conflict are finances and priority of resources. Further findings show that differences in tastes and interests, selfishness and lack of communication played a key role as causes of family conflicts. The implication of this study is that finances and priority of resources are antecedents of family conflict in the context of Uganda. Therefore, the antecedents of family conflict that emerged from this study can be understood, defined and analyzed through the lens of social identity theory. Future research may include conducting quantitative studies with a particular demographic using the themes that have emerged from this study.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

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