MOSDNET: A multi-omics classification framework using simplified multi-view deep discriminant representation learning and dynamic edge GCN with multi-task learning.

The integration of multi-omics data offers a robust approach to understanding the complexity of diseases by combining information from various biological levels, such as genomics, transcriptomics, proteomics, and metabolomics. This integrated approach is essential for a comprehensive understanding of disease mechanisms and for developing more effective diagnostic and therapeutic strategies. Nevertheless, most current methodologies fail to effectively extract both shared and specific representations from omics data. This study introduces MOSDNET, a multi-omics classification framework that effectively extracts shared and specific representations. This framework leverages Simplified Multi-view Deep Discriminant Representation Learning (S-MDDR) and Dynamic Edge GCN (DEGCN) to enhance the accuracy and efficiency of disease classification. Initially, MOSDNET utilizes S-MDDR to establish similarity and orthogonal constraints for extracting these representations, which are then concatenated to integrate the multi-omics data. Subsequently, MOSDNET constructs a comprehensive view of the sample data by employing patient similarity networks. By incorporating similarity networks into DEGCN, MOSDNET learns intricate network structures and node representations, which enables superior classification outcomes. MOSDNET is trained through a multitask learning approach, effectively leveraging the complementary knowledge from both the data integration and classification components. After conducting extensive comparative experiments, we have conclusively demonstrated that MOSDNET outperforms leading state-of-the-art multi-omics classification models in terms of classification accuracy. Simultaneously, we employ MOSDNET to identify pivotal biomarkers within the multi-omics data, providing insights into disease etiology and progression.

PCA-constrained multi-core matrix fusion network: A novel approach for cancer subtype identification.

Cancer subtyping refers to categorizing a particular cancer type into distinct subtypes or subgroups based on a range of molecular characteristics, clinical manifestations, histological features, and other relevant factors. The identification of cancer subtypes can significantly enhance precision in clinical practice and facilitate personalized diagnosis and treatment strategies. Recent advancements in the field have witnessed the emergence of numerous network fusion methods aimed at identifying cancer subtypes. The majority of these fusion algorithms, however, solely rely on the fusion network of a single core matrix for the identification of cancer subtypes and fail to comprehensively capture similarity. To tackle this issue, in this study, we propose a novel cancer subtype recognition method, referred to as PCA-constrained multi-core matrix fusion network (PCA-MM-FN). The PCA-MM-FN algorithm initially employs three distinct methods to obtain three core matrices. Subsequently, the obtained core matrices are projected into a shared subspace using principal component analysis, followed by a weighted network fusion. Lastly, spectral clustering is conducted on the fused network. The results obtained from conducting experiments on the mRNA expression, DNA methylation, and miRNA expression of five TCGA datasets and three multi-omics benchmark datasets demonstrate that the proposed PCA-MM-FN approach exhibits superior accuracy in identifying cancer subtypes compared to the existing methods.

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset.

BACKGROUND AND OBJECTIVE: Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. METHODS: NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. RESULTS: The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. CONCLUSIONS: The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

Population characteristic exploitation-based multi-orientation multi-objective gene selection for microarray data classification.

Gene selection is a process of selecting discriminative genes from microarray data that helps to diagnose and classify cancer samples effectively. Swarm intelligence evolution-based gene selection algorithms can never circumvent the problem that the population is prone to local optima in the process of gene selection. To tackle this challenge, previous research has focused primarily on two aspects: mitigating premature convergence to local optima and escaping from local optima. In contrast to these strategies, this paper introduces a novel perspective by adopting reverse thinking, where the issue of local optima is seen as an opportunity rather than an obstacle. Building on this foundation, we propose MOMOGS-PCE, a novel gene selection approach that effectively exploits the advantageous characteristics of populations trapped in local optima to uncover global optimal solutions. Specifically, MOMOGS-PCE employs a novel population initialization strategy, which involves the initialization of multiple populations that explore diverse orientations to foster distinct population characteristics. The subsequent step involved the utilization of an enhanced NSGA-II algorithm to amplify the advantageous characteristics exhibited by the population. Finally, a novel exchange strategy is proposed to facilitate the transfer of characteristics between populations that have reached near maturity in evolution, thereby promoting further population evolution and enhancing the search for more optimal gene subsets. The experimental results demonstrated that MOMOGS-PCE exhibited significant advantages in comprehensive indicators compared with six competitive multi-objective gene selection algorithms. It is confirmed that the "reverse-thinking" approach not only avoids local optima but also leverages it to uncover superior gene subsets for cancer diagnosis.

Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification.

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes' group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.

Color image segmentation using adaptive hierarchical-histogram thresholding.

Histogram-based thresholding is one of the widely applied techniques for conducting color image segmentation. The key to such techniques is the selection of a set of thresholds that can discriminate objects and background pixels. Many thresholding techniques have been proposed that use the shape information of histograms and identify the optimum thresholds at valleys. In this work, we introduce the novel concept of a hierarchical-histogram, which corresponds to a multigranularity abstraction of the color image. Based on this, we present a new histogram thresholding-Adaptive Hierarchical-Histogram Thresholding (AHHT) algorithm, which can adaptively identify the thresholds from valleys. The experimental results have demonstrated that the AHHT algorithm can obtain better segmentation results compared with the histon-based and the roughness-index-based techniques with drastically reduced time complexity.

Production and evaluation of biodiesel and bioethanol from high oil corn using three processing routes.

Six Korea high oil (KHO) corn varieties varying in germ and endosperm size and oil content (4-21%, wet basis) were subjected to three sequential combinations of milling (M), germ separation (S), fermentation (F), and in situ transesterification (T) to produce bioethanol and biodiesel. Production parameters including saccharification, bioethanol yield, biodiesel yield and composition, and conversion rate were evaluated. The effects of the contents of germ, endosperm size, oil, and non-oil solid mass on the production parameters strongly depended on the processing routes, namely M-F-T, M-T-F, and S-T|F. The M-F-T route produced the highest bioethanol yield while the S-T|F route produced the highest biodiesel yield. The in situ transesterification reaction, if proceeded before fermentation, reduced the bioethanol yield while fermentation and/or presence of endosperm reduced the biodiesel yield.

Inactivation of Escherichia coli on almonds using nonthermal plasma.

This study was carried out to investigate the applicability of nonthermal plasma (NTP) technology for the pasteurization of almonds. Almonds were spiked with various levels of Escherichia coli by dipping the almonds in E. coli culture broth followed by drying. The spiked almonds were treated with NTP under different treatment conditions. The pattern of the microorganisms reduction by NTP was analyzed. NTP was found to be effective on reduction of E. coli on almond evidenced by almost 5-log reduction after 30-sec treatment at 30 kV and 2000 Hz. The NTP bactericidal effect on E. coli inoculated on almond increased with the applied voltage and the frequency. The NTP reduction followed the 1st-order reaction kinetics, and the reduction rate constants varied with almond types and grades. The E. coli cells at logarithmic phase were more sensitive to the NTP than those at stationary and declining phases.

Physical and chemical properties of bio-oils from microwave pyrolysis of corn stover.

This study was aimed to understand the physical and chemical properties of pyrolytic bio-oils produced from microwave pyrolysis of corn stover regarding their potential use as gas turbine and home heating fuels. The ash content, solids content, pH, heating value, minerals, elemental ratio, moisture content, and viscosity of the bio-oils were determined. The water content was approx 15.2 wt%, solids content 0.22 wt%, alkali metal content 12 parts per million, dynamic viscosity 185 mPa.s at 40 degrees C, and gross high heating value 17.5 MJ/kg for a typical bio-oil produced. Our aging tests showed that the viscosity and water content increased and phase separation occurred during the storage at different temperatures. Adding methanol and/or ethanol to the bio-oils reduced the viscosity and slowed down the increase in viscosity and water content during the storage. Blending of methanol or ethanol with the bio-oils may be a simple and cost-effective approach to making the pyrolytic bio-oils into a stable gas turbine or home heating fuels.