Optimal search-based gene subset selection for gene array cancer classification

The global burden of cancers attributable to occupational factors, 1990-2021

This study assessed the global cancer burden due to occupational carcinogens (OCs) using data from Global Burden of Disease (GBD) 2021. Mortality and disability-adjusted life years (DALYs) were employed to assess the evolving trend of cancer attributable to occupational risk. The analysis was conducted by age, year, geographical location, and socio-demographic index (SDI). Subsequently, the estimated annual percentage change (EAPC) values were calculated. Globally, asbestos exposure showed the most severe impact on age-standardized death rate (ASDR) and age-standardized DALY rate but decreased significantly. Conversely, diesel engine exhaust exposure increased, with EAPCs of 0.80 for deaths. Trichloroethylene exposure, although low in absolute terms, exhibited the fastest growth with an EAPC of 1.21 in age-standardized DALY rate. Notably, diesel engine exhaust exposure in South Asia and polycyclic aromatic hydrocarbons (PAHs) in Southeast Asia, East Asia, and Oceania increased significantly in age-standardized DALY rate. Regions with low to middle SDI, such as South Asia and sub-Saharan Africa, showed the highest increases in OC-related cancer burdens in age-standardized DALY rate. Lesotho, Kenya, and Egypt exhibited the fastest growth, with EAPCs in age-standardized DALY rate of 3.45, 2.13, and 2.95, respectively. High-income regions like the Netherlands, the United Kingdom, and Italy had the most severe OC-related cancer of ASDR burdens in 2021. OC exposure remains a major contributor to the global cancer burden, especially from asbestos and silica. Exposure to diesel engine exhaust was associated with increased risk of cancers, particularly in low -to -middle SDI regions such as South Asia and sub-Saharan Africa.

Global disease burden and trends of leukemia attributable to occupational risk from 1990 to 2019: An observational trend study

Background

Leukemia caused by occupational risk is a problem that needs more attention and remains to be solved urgently, especially for acute lymphoid leukemia (ALL), acute myeloid leukemia (AML), and chronic lymphoid leukemia (CLL). However, there is a paucity of literature on this issue. We aimed to assess the global burden and trends of leukemia attributable to occupational risk from 1990 to 2019.

Methods

This observational trend study was based on the Global Burden of Disease (GBD) 2019 database, the global deaths, and disability-adjusted life years (DALYs), which were calculated to quantify the changing trend of leukemia attributable to occupational risk, were analyzed by age, year, geographical location, and socio-demographic index (SDI), and the corresponding estimated annual percentage change (EAPC) values were calculated.

Results

Global age-standardized DALYs and death rates of leukemia attributable to occupational risk presented significantly decline trends with EAPC [-0.38% (95% CI: -0.58 to -0.18%) for DALYs and -0.30% (95% CI: -0.45 to -0.146%) for death]. However, it was significantly increased in people aged 65-69 years [0.42% (95% CI: 0.30-0.55%) for DALYs and 0.38% (95% CI: 0.26-0.51%) for death]. At the same time, the age-standardized DALYs and death rates of ALL, AML, and CLL were presented a significantly increased trend with EAPCs [0.78% (95% CI: 0.65-0.91%), 0.87% (95% CI: 0.81-0.93%), and 0.66% (95% CI: 0.51-0.81%) for DALYs, respectively, and 0.75% (95% CI: 0.68-0.82%), 0.96% (95% CI: 0.91-1.01%), and 0.55% (95% CI: 0.43-0.68%) for death], respectively. The ALL, AML, and CLL were shown an upward trend in almost all age groups.

Conclusion

We observed a substantial reduction in leukemia due to occupational risks between 1990 and 2019. However, the people aged 65-69 years and burdens of ALL, AML, and CLL had a significantly increased trend in almost all age groups. Thus, there remains an urgent need to accelerate efforts to reduce leukemia attributable to occupational risk-related death burden in this population and specific causes.

Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem

Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy.

Nature-Inspired Multiobjective Cancer Subtype Diagnosis

Cancer gene expression data is of great importance in cancer subtype diagnosis and drug discovery. Many computational methods have been proposed to classify subtypes using those data. However, most of the previous computational methods suffer from poor interpretability, experimental noises, and low diagnostic quality. To address those problems, multiobjective ensemble cuckoo search based on decomposition (MOECSA) is proposed to optimize those four objectives simultaneously including the number of features, the accuracy, and two entropy-based measures: the relevance and the redundancy, classifying the cancer gene expression data with high predictive power for different cardinality levels under multiple objectives. A novel binary encoding is proposed to choose gene subsets from the cancer gene expression data for calculating four objective functions. Furthermore, an effective ensemble mechanism blended in the cuckoo search algorithm framework is applied to balance the convergence speed and population diversity in MOECSA. To demonstrate the effectiveness and efficiency of the proposed algorithm, experiments on thirty-five benchmark cancer gene expression datasets, four independent disease datasets, and one sequencing-based dataset are carried out to compare MOECSA with the six state-of-the-art multiobjective evolutionary algorithms and seven traditional classification algorithms. The experimental results in different perspectives demonstrate that MOECSA has better diagnosis performance than others at multiple levels.

A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system

Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.

Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method

Within the emerging context of Web 2.0 social media, online customer reviews are playing an increasingly important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. The sheer volume of customer reviews on the web produces information overload for readers. Developing a system that can automatically identify the most helpful reviews would be valuable to businesses that are interested in gathering informative and meaningful customer feedback. Because the target variable---review helpfulness---is continuous, common feature selection techniques from text classification cannot be applied. In this article, we propose and investigate a text mining model, enhanced using the Regressional ReliefF (RReliefF) feature selection method, for predicting the helpfulness of online reviews from Amazon.com. We find that RReliefF significantly outperforms two popular dimension reduction methods. This study is the first to investigate and compare different dimension reduction techniques in the context of applying text regression for predicting online review helpfulness. Another contribution is that our analysis of the keywords selected by RReliefF reveals meaningful feature groupings.

A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data

Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.

Automatic online news monitoring and classification for syndromic surveillance

Syndromic surveillance can play an important role in protecting the public's health against infectious diseases. Infectious disease outbreaks can have a devastating effect on society as well as the economy, and global awareness is therefore critical to protecting against major outbreaks. By monitoring online news sources and developing an accurate news classification system for syndromic surveillance, public health personnel can be apprised of outbreaks and potential outbreak situations. In this study, we have developed a framework for automatic online news monitoring and classification for syndromic surveillance. The framework is unique and none of the techniques adopted in this study have been previously used in the context of syndromic surveillance on infectious diseases. In recent classification experiments, we compared the performance of different feature subsets on different machine learning algorithms. The results showed that the combined feature subsets including Bag of Words, Noun Phrases, and Named Entities features outperformed the Bag of Words feature subsets. Furthermore, feature selection improved the performance of feature subsets in online news classification. The highest classification performance was achieved when using SVM upon the selected combination feature subset.

Fuzzy logic for elimination of redundant information of microarray data

Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, we use fuzzy inference rules to transform the gene expression levels of a given dataset into fuzzy values. Then we apply a similarity relation to these fuzzy values to define fuzzy equivalence groups, each group containing strongly similar genes. Dimension reduction is achieved by considering for each group of similar genes a single representative based on mutual information. To assess the usefulness of this approach, extensive experimentations were carried out on three well-known public datasets with a combined classification model using three statistic filters and three classifiers.

Sentiment analysis in multiple languages

The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91% on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.