SLNL: A novel method for gene selection and phenotype classification

Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach

Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have been employed to represent molecular structures and predict effective anti-leishmanial molecules. However, each method faces challenges and limitations that must be addressed to optimize the drug discovery and design process. Recently, machine learning approaches have gained significant importance in overcoming the limitations of traditional methods across various fields. Therefore, there is an urgent need to build a computational pipeline using advanced machine learning and deep learning methods that help to predict anti-leishmanial activity of drug candidates. The proposed pipeline in this paper involves data collection, feature extraction, feature selection and prediction techniques. This review presents a comprehensive computational pipeline for anti-leishmanial drug discovery, highlighting its strengths, limitations, challenges, and future directions to improve treatment for this neglected tropical disease.

Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture

Leukocytes or white blood cells plays an important role in protecting the body from various contagious diseases and infectious agents. Different conventional leukocyte analysis approaches often face several problems like inaccuracies, demanding the need for sophisticated approaches to improve diagnostic precision. Therefore, a holistic structure namely a novel Attention-based Dual Channel U-shaped Network (ADCU-Net) utilizing three datasets is introduced in this paper for effective leukocyte classification. The image quality is boosted in the preprocessing phase through noise reduction, contrast enhancement, and background removal, significantly improving clarity. Then, the Dung Beetle Optimization (DBO) algorithm enhanced with Levy flight optimization is implemented for effective image segmentation processes. A dung beetle with a levy flight strategy assists in streamlined exploration of the search space thereby the detection and delineation of specific regions within images are improved, which results in higher boundary detection accuracy. The evaluation of major quantitative measures such as standard deviation, mean and entropy is comprised in the feature extraction process which offers crucial insights into the structural characteristics of leukocytes. Finally, a novel ADCU-Net model is utilized for the effective classification process. This ADCU-Net model is particularly selected to effectively capture various features and preserve spatial data, achieving98.4% accuracy. Overall, this paper highlights the performance of integrated sophisticated deep-learning structures for accurate leukocyte classification and segmentation, enabling the path for improved diagnostic tools in clinical settings.

FCN-PD: An Advanced Deep Learning Framework for Parkinson's Disease Diagnosis Using MRI Data

Background/Objectives: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor dysfunction, cognitive decline, and a diminished quality of life. Early and accurate diagnosis is essential for effective disease management. However, traditional diagnostic approaches, which rely on clinical observations and subjective assessments, often lead to delays and inaccuracies. This research aims to address these limitations by proposing FCN-PD, an advanced deep learning framework for accurate PD diagnosis using MRI data. Methods: The FCN-PD framework incorporates a hybrid feature extraction phase that combines EfficientNet to capture local spatial details and attention mechanisms to extract global contextual information. These features are then processed by a Fully Connected Network (FCN) for final classification. This architecture enables the model to effectively represent hierarchical features and handle high-dimensional MRI data while mitigating issues such as overfitting and feature redundancy. Results: The performance of FCN-PD was evaluated on three publicly available MRI datasets. On the PPMI dataset, it achieved an accuracy of 97.2%, outperforming traditional CNN-based models by 5.3%. On the OASIS dataset, the model achieved 95.6% accuracy, and on the MIRIAD dataset, it reached 96.8% accuracy. These results establish FCN-PD as a superior alternative to existing PD diagnostic methods. Conclusions: FCN-PD demonstrates significant improvements in diagnostic accuracy and efficiency for Parkinson's disease using MRI data. Its robust architecture effectively captures both local and global features, making it a promising tool for clinical integration and early PD detection, ultimately contributing to better patient outcomes.

New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification

Microarray technology has become a vital tool in cardiovascular research, enabling the simultaneous analysis of thousands of gene expressions. This capability provides a robust foundation for heart disease classification and biomarker discovery. However, the high dimensionality, noise, and sparsity of microarray data present significant challenges for effective analysis. Gene selection, which aims to identify the most relevant subset of genes, is a crucial preprocessing step for improving classification accuracy, reducing computational complexity, and enhancing biological interpretability. Traditional gene selection methods often fall short in capturing complex, nonlinear interactions among genes, limiting their effectiveness in heart disease classification tasks. In this study, we propose a novel framework that leverages deep neural networks (DNNs) for optimizing gene selection and heart disease classification using microarray data. DNNs, known for their ability to model complex, nonlinear patterns, are integrated with feature selection techniques to address the challenges of high-dimensional data. The proposed method, DeepGeneNet (DGN), combines gene selection and DNN-based classification into a unified framework, ensuring robust performance and meaningful insights into the underlying biological mechanisms. Additionally, the framework incorporates hyperparameter optimization and innovative U-Net segmentation techniques to further enhance computational performance and classification accuracy. These optimizations enable DGN to deliver robust and scalable results, outperforming traditional methods in both predictive accuracy and interpretability. Experimental results demonstrate that the proposed approach significantly improves heart disease classification accuracy compared to other methods. By focusing on the interplay between gene selection and deep learning, this work advances the field of cardiovascular genomics, providing a scalable and interpretable framework for future applications.

Unraveling the flavor formation process of mellow and thick-type ripened Pu-erh tea through non-targeted metabolomics and metagenomics

Ripened Pu-erh tea (RPT) is renowned for its distinctive flavor and health benefits. However, its complex fermentation process poses challenges in ensuring consistency in production. This study investigated RPT flavor formation through sensory evaluation, multi-omics analysis, and multivariate statistical approaches. By day 24, the tea exhibited a reddish-brown infusion and a mellow, thick taste (MT_RPT), achieving the highest sensory score (94.0, P < 0.05). Sixteen flavor-related chemical components exhibited significant changes (P < 0.05). The contents of free amino acids, L-theanine, tea polyphenols, flavonoids, catechins, and thearubigins decreased. In contrast, the contents of total soluble sugars, caffeine, theobromine, epicatechin, and theabrownins (TBs) increased, reaching 74.1 mg/g, 65.38 mg/g, 3.13 mg/g, 3.33 mg/g, and 134.84 mg/g, respectively. Additionally, 33 nonvolatile metabolites (e.g., pelargonidin 3-O-glucoside, dihydroisorhamnetin, and puerarin) were significantly correlated with MT_RPT flavor (VIP > 1, |r| ≥ 0.8, P < 0.05) and influenced by key functional microbes, including Pantoea, Aspergillus, Brachybacterium, and Staphylococcus. By day 30, the infusion darkened, and sensory scores declined (81.4, P < 0.05), attributed to the dominance of Brevibacterium. This microbial shift reduced water-soluble pectin, free amino acids, and 11 metabolites while increasing TBs and theophylline (219.33 mg/g and 0.09 mg/g, respectively). Therefore, TBs were identified as a crucial indicator of optimal fermentation. Moreover, redundancy analysis indicated that the tea pile's central temperature, moisture content, and pH were essential fermentation parameters (P < 0.05). These findings deepen our understanding of MT_RPT flavor development mechanisms and provide valuable insights into precise fermentation control.

Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification

The daily surge in cases in many nations has made the growing number of human monkeypox (Mpox) cases an important global concern. Therefore, it is imperative to identify Mpox early to prevent its spread. The majority of studies on Mpox identification have utilized deep learning (DL) models. However, research on developing a reliable method for accurately detecting Mpox in its early stages is still lacking. This study proposes an ensemble model composed of three improved DL models to more accurately classify Mpox in its early phases. We used the widely recognized Mpox Skin Images Dataset (MSID), which includes 770 images. The enhanced Swin Transformer (SwinViT), the proposed ensemble model Mpox-XDE, and three modified DL models-Xception, DenseNet201, and EfficientNetB7-were used. To generate the ensemble model, the three DL models were combined via a Softmax layer, a dense layer, a flattened layer, and a 65% dropout. Four neurons in the final layer classify the dataset into four categories: chickenpox, measles, normal, and Mpox. Lastly, a global average pooling layer is implemented to classify the actual class. The Mpox-XDE model performed exceptionally well, achieving testing accuracy, precision, recall, and F1-score of 98.70%, 98.90%, 98.80%, and 98.80%, respectively. Finally, the popular explainable artificial intelligence (XAI) technique, Gradient-weighted Class Activation Mapping (Grad-CAM), was applied to the convolutional layer of the Mpox-XDE model to generate overlaid areas that effectively highlight each illness class in the dataset. This proposed methodology will aid professionals in diagnosing Mpox early in a patient's condition.

An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models

Skin cancer is the most dominant and critical method of cancer, which arises all over the world. Its damaging effects can range from disfigurement to major medical expenditures and even death if not analyzed and preserved timely. Conventional models of skin cancer recognition require a complete physical examination by a specialist, which is time-wasting in a few cases. Computer-aided medicinal analytical methods have gained massive popularity due to their efficiency and effectiveness. This model can assist dermatologists in the initial recognition of skin cancer, which is significant for early diagnosis. An automatic classification model utilizing deep learning (DL) can help doctors perceive the kind of skin lesion and improve the patient's health. The classification of skin cancer is one of the hot topics in the research field, along with the development of DL structure. This manuscript designs and develops a Detection of Skin Cancer Using an Ensemble Deep Learning Model and Gray Wolf Optimization (DSC-EDLMGWO) method. The proposed DSC-EDLMGWO model relies on the recognition and classification of skin cancer in biomedical imaging. The presented DSC-EDLMGWO model initially involves the image preprocessing stage at two levels: contract enhancement using the CLAHE method and noise removal using the wiener filter (WF) model. Furthermore, the proposed DSC-EDLMGWO model utilizes the SE-DenseNet method, which is the fusion of the squeeze-and-excitation (SE) module and DenseNet to extract features. For the classification process, the ensemble of DL models, namely the long short-term memory (LSTM) technique, extreme learning machine (ELM) model, and stacked sparse denoising autoencoder (SSDA) method, is employed. Finally, the gray wolf optimization (GWO) method optimally adjusts the ensemble DL models' hyperparameter values, resulting in more excellent classification performance. The effectiveness of the DSC-EDLMGWO approach is evaluated using a benchmark image database, with outcomes measured across various performance metrics. The experimental validation of the DSC-EDLMGWO approach portrayed a superior accuracy value of 98.38% and 98.17% under HAM10000 and ISIC datasets across other techniques.

Melanoma classification using generative adversarial network and proximal policy optimization

In oncology, melanoma is a serious concern, often arising from DNA changes caused mainly by ultraviolet radiation. This cancer is known for its aggressive growth, highlighting the necessity of early detection. Our research introduces a novel deep learning framework for melanoma classification, trained and validated using the extensive SIIM-ISIC Melanoma Classification Challenge-ISIC-2020 dataset. The framework features three dilated convolution layers that extract critical feature vectors for classification. A key aspect of our model is incorporating the Off-policy Proximal Policy Optimization (Off-policy PPO) algorithm, which effectively handles data imbalance in the training set by rewarding the accurate classification of underrepresented samples. In this framework, the model is visualized as an agent making a series of decisions, where each sample represents a distinct state. Additionally, a Generative Adversarial Network (GAN) augments training data to improve generalizability, paired with a new regularization technique to stabilize GAN training and prevent mode collapse. The model achieved an F-measure of 91.836% and a geometric mean of 91.920%, surpassing existing models and demonstrating the model's practical utility in clinical environments. These results demonstrate its potential in enhancing early melanoma detection and informing more accurate treatment approaches, significantly advancing in combating this aggressive cancer.

An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis

As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features. Evaluation on public data sets, at various of training samples percentages, showed that the proposed strategy achieves promising performance. Namely, the system yielded an overall accuracy of 98.91% with testing run time of 5.5 seconds, while using machine classifiers with small number of hyper-parameters. Additional experimental comparison reveals superior performance of the proposed system over literature approaches using various metrics. Statistical analysis also confirmed that the proposed AMDS outperformed other models after running 50 times. Finally, the generalizability of the proposed model is evaluated by testing its performance on external data sets for monkeypox and COVID-19. Our model achieved an overall diagnostic accuracy of 98.00% and 99.00% on external COVID and monkeypox data sets, respectively.

CRISPR applications in microbial World: Assessing the opportunities and challenges

Genome editing has emerged during the past few decades in the scientific research area to manipulate genetic composition, obtain desired traits, and deal with biological challenges by exploring genetic traits and their sequences at a level of precision. The discovery of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) as a genome editing tool has offered a much better understanding of cellular and molecular mechanisms. This technology emerges as one of the most promising candidates for genome editing, offering several advantages over other techniques such as high accuracy and specificity. In the microbial world, CRISPR/Cas technology enables researchers to manipulate the genetic makeup of micro-organisms, allowing them to achieve almost impossible tasks. This technology initially discovered as a bacterial defense mechanism, is now being used for gene cutting and editing to explore more of its dimensions. CRISPR/Cas 9 systems are highly efficient and flexible, leading to its widespread uses in microbial research areas. Although this technology is widely used in the scientific community, many challenges, including off-target activity, low efficiency of Homology Directed Repair (HDR), and ethical considerations, still need to be overcome before it can be widely used. As CRISPR/Cas technology has revolutionized the field of microbiology, this review article aimed to present a comprehensive overview highlighting a brief history, basic mechanisms, and its application in the microbial world along with accessing the opportunities and challenges.

Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification

The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification. This study introduces a revolutionary approach for accurately classifying diabetes, aiming to provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) is proposed in combination with Kernel Extreme Learning Machine (KELM) for a diabetes classification prediction model. First, the Secretary Bird Optimization Algorithm (SBOA) is enhanced by integrating a particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, and quantum computing-based t-distribution variations. The performance of QHSBOA is validated using the CEC2017 benchmark suite. Subsequently, QHSBOA is used to optimize the kernel penalty parameter [Formula: see text] and bandwidth [Formula: see text] of the KELM. Comparative experiments with other classification models are conducted on diabetes datasets. The experimental results indicate that the QHSBOA-KELM classification model outperforms other comparative models in four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity. This approach offers an effective method for the early diagnosis and prediction of diabetes.

Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies

Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using advanced genome sequencing platforms to discover variants, if there is no connection between the variants found in the patient's genome and their phenotypes in the literature, then the patient will remain undiagnosed. When a direct variant-phenotype connection is not known, putting a patient's information in the larger context of phenotype relationships and protein-protein interactions may provide an opportunity to find an indirect explanation. Databases such as STRING contain millions of protein-protein interactions, and the Human Phenotype Ontology (HPO) contains the relations of thousands of phenotypes. By integrating these networks and clustering the entities within, we can potentially discover latent gene-to-phenotype connections. The historical records for STRING and HPO provide a unique opportunity to create a network time series for evaluating the cluster significance. Most excitingly, working with Children's Hospital Colorado, we have provided promising hypotheses about latent gene-to-phenotype connections for 38 patients. We also provide potential answers for 14 patients listed on MyGene2. Clusters our tool finds significant harbor 2.35 to 8.72 times as many gene-to-phenotype edges inferred from known drug interactions than clusters found to be insignificant. Our tool, BOCC, is available as a web app and command line tool.

An enhanced lightweight T-Net architecture based on convolutional neural network (CNN) for tomato plant leaf disease classification

Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.

CRISPR innovations in tissue engineering and gene editing

The CRISPR/Cas9 system is a powerful tool for genome editing, utilizing the Cas9 nuclease and programmable single guide RNA (sgRNA). However, the Cas9 nuclease activity can be disabled by mutation, resulting in catalytically deactivated Cas9 (dCas9). By combining the customizable sgRNA with dCas9, researchers can inhibit specific gene expression (CRISPR interference, CRISPRi) or activate the expression of a target gene (CRISPR activation, CRISPRa). In this review, we present the principles and recent advancements of these CRISPR technologies, as well as their delivery vectors. We also explore their applications in stem cell engineering and regenerative medicine, with a focus on in vitro stem cell fate manipulation and in vivo treatments. These include the prevention of retinal and muscular degeneration, neural regeneration, bone regeneration, cartilage tissue engineering, and the treatment of blood, skin, and liver diseases. Furthermore, we discuss the challenges of translating CRISPR technologies into regenerative medicine and provide future perspectives. Overall, this review highlights the potential of CRISPR in advancing regenerative medicine and offers insights into its application in various areas of research and therapy.

Plant Microbe Interaction-Predicting the Pathogen Internalization Through Stomata Using Computational Neural Network Modeling

Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant-pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as Salmonella enterica (S. enterica), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant-microbe interactions. These methods will aid researchers in understanding plant-pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context.

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA's exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA's exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.

Application of a single-cell-RNA-based biological-inspired graph neural network in diagnosis of primary liver tumors

Single-cell technology depicts integrated tumor profiles including both tumor cells and tumor microenvironments, which theoretically enables more robust diagnosis than traditional diagnostic standards based on only pathology. However, the inherent challenges of single-cell RNA sequencing (scRNA-seq) data, such as high dimensionality, low signal-to-noise ratio (SNR), sparse and non-Euclidean nature, pose significant obstacles for traditional diagnostic approaches. The diagnostic value of single-cell technology has been largely unexplored despite the potential advantages. Here, we present a graph neural network-based framework tailored for molecular diagnosis of primary liver tumors using scRNA-seq data. Our approach capitalizes on the biological plausibility inherent in the intercellular communication networks within tumor samples. By integrating pathway activation features within cell clusters and modeling unidirectional inter-cellular communication, we achieve robust discrimination between malignant tumors (including hepatocellular carcinoma, HCC, and intrahepatic cholangiocarcinoma, iCCA) and benign tumors (focal nodular hyperplasia, FNH) by scRNA data of all tissue cells and immunocytes only. The efficacy to distinguish iCCA from HCC was further validated on public datasets. Through extending the application of high-throughput scRNA-seq data into diagnosis approaches focusing on integrated tumor microenvironment profiles rather than a few tumor markers, this framework also sheds light on minimal-invasive diagnostic methods based on migrating/circulating immunocytes.

Multimodal functional deep learning for multiomics data

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

Genome-Wide Characterization of Fructose 1,6-Bisphosphate Aldolase Genes and Expression Profile Reveals Their Regulatory Role in Abiotic Stress in Cucumber

The fructose-1,6-bisphosphate aldolase (FBA) gene family exists in higher plants, with the genes of this family playing significant roles in plant growth and development, as well as response to abiotic stresses. However, systematic reports on the FBA gene family and its functions in cucumber are lacking. In this study, we identified five cucumber FBA genes, named CsFBA1-5, that are distributed randomly across chromosomes. Phylogenetic analyses involving these cucumber FBAs, alongside eight Arabidopsis FBA proteins and eight tomato FBA proteins, were conducted to assess their homology. The CsFBAs were grouped into two clades. We also analyzed the physicochemical properties, motif composition, and gene structure of the cucumber FBAs. This analysis highlighted differences in the physicochemical properties and revealed highly conserved domains within the CsFBA family. Additionally, to explore the evolutionary relationships of the CsFBA family further, we constructed comparative syntenic maps with Arabidopsis and tomato, which showed high homology but only one segmental duplication event within the cucumber genome. Expression profiles indicated that the CsFBA gene family is responsive to various abiotic stresses, including low temperature, heat, and salt. Taken together, the results of this study provide a theoretical foundation for understanding the evolution of and future research into the functional characterization of cucumber FBA genes during plant growth and development.

Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement

Introduction

Global Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.

Methods

This paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.

Results

On the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.

Discussion

These findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model's use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis

Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction.

Diagnostic Accuracy of Exosomal Long Noncoding RNAs in Diagnosis of NSCLC: A Meta-Analysis

PURPOSE

Globally, non-small cell lung cancer (NSCLC) is the primary cause of cancer-related mortality, both early and accurate diagnosis are essential for effective treatment and improved patient outcomes. Exosomal noncoding RNAs (ncRNAs) have emerged as promising biomarkers for NSCLC diagnosis. This meta-analysis aims to assess the diagnostic accuracy of exosomal long noncoding RNAs (lncRNAs) for diagnosing NSCLC.

METHODS

A comprehensive literature search was conducted to identify relevant studies that assessed the diagnostic performance of exosomal lncRNAs in NSCLC. Quality assessment and data extraction were performed independently by two reviewers. Pooled sensitivity, specificity, and other relevant diagnostic parameters were calculated using a bivariate random-effects model. Subgroup analyses and meta-regression were conducted to explore potential sources of heterogeneity.

RESULTS

Sixteen studies, comprising 1843 NSCLC cases and 1298 controls, were included in this meta-analysis. The pooled sensitivity and specificity of nine exosomal lncRNAs for diagnosing NSCLC were 0.74 [95% confidence interval (CI) 0.69-0.79] and 0.78 (95% CI 0.68-0.85). The pooled area under the receiver operating characteristic curve (AUC) for fifteen lncRNAs was 0.80 (95% CI 0.768-0.831). Meta-regression could not find any source for interstudy heterogeneity.

CONCLUSION

Exosomal lncRNAs, particularly AL139294.1, GAS5, LUCAT1, and SOX2-OT, have excellent diagnostic accuracy and promising diagnostic potential in NSCLC. Therefore, they can be used as diagnostic tools for early detection of NSCLC.

Targeting the Wnt/β-catenin cascade in osteosarcoma: The potential of ncRNAs as biomarkers and therapeutics

Osteosarcoma (OS) is a bone cancer which stems from several sources and presents with diverse clinical features, making evaluation and treatment difficult. Chemotherapy tolerance and restricted treatment regimens hinder progress in survival rates, requiring new and creative therapeutic strategies. The Wnt/β-catenin system has been recognised as an essential driver of OS development, providing potential avenues for therapy. Non-coding RNAs (ncRNAs), such as circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and microRNAs (miRNAs), are essential in modulating the Wnt/β-catenin cascade in OS. MiRNAs control the system by targeting vital elements, while lncRNAs and circRNAs interact with system genes, impacting OS growth and advancement. This paper thoroughly analyses the intricate interplay between ncRNAs and the Wnt/β-catenin cascade in OS. We examine how uncontrolled levels of miRNAs, lncRNAs, and circRNAs lead to an abnormal Wnt/β-catenin network, which elevates the development, spread, and susceptibility to the treatment of OS. We emphasise the potential of ncRNAs as diagnostic indicators and avenues for treatment in OS care. The review offers valuable insights for academics and clinicians studying OS aetiology and creating new treatment techniques for the ncRNA-Wnt/β-catenin cascade. Utilising the oversight roles of ncRNAs in the Wnt/β-catenin system shows potential for enhancing the outcomes of patients and progressing precision medicine in OS therapy.

Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC's ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.

Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.

Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions

Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.

Diagnostic value of miR-200 family in non-small cell lung cancer: a meta-analysis

Aim: To investigate the diagnostic potential of the miR-200 family for early detection in non-small cell lung cancer (NSCLC). Materials & methods: A systematic search was conducted of PubMed, Embase and Web of Science databases to identify studies of the miR-200 family in NSCLC. Sixteen studies meeting the inclusion criteria were included in the analysis with a total of 20 cohorts. Results: The combined sensitivity and specificity reached 73% and 85%, with an area under the curve of 0.83. Notably, miR-200b introduced heterogeneity. Subgroup analysis highlighted miR-200a and miR-141 as more sensitive, while blood-derived miRNAs showed slightly lower accuracy. Conclusion: The miR-200 family, predominantly assessed in blood, exhibits significant diagnostic potential for NSCLC, especially in distinguishing it from benign diseases.

Investigation of salt tolerance in cotton germplasm by analyzing agro-physiological traits and ERF genes expression

The development of genotypes that can tolerate high levels of salt is crucial for the efficient use of salt-affected land and for enhancing crop productivity worldwide. Therefore, incorporating salinity tolerance is a critical trait that crops must possess. Salt resistance is a complex character, controlled by multiple genes both physiologically and genetically. To examine the genetic foundation of salt tolerance, we assessed 16 F1 hybrids and their eight parental lines under normal and salt stress (15 dS/m) conditions. Under salt stress conditions significant reduction was observed for plant height (PH), bolls/plant (NBP), boll weight (BW), seed cotton yield (SCY), lint% (LP), fiber length (FL), fiber strength (FS), potassium to sodium ratio (K+/Na+), potassium contents (K+), total soluble proteins (TSP), carotenoids (Car) and chlorophyll contents. Furthermore, the mean values for hydrogen peroxide (H2O2), sodium contents (Na+), catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), and fiber fineness (FF) were increased under salt stress. Moderate to high heritability and genetic advancement was observed for NBP, BW, LP, SCY, K+/Na+, SOD, CAT, POD, Car, TSP, FL, and FS. Mean performance and multivariate analysis of 24 cotton genotypes based on various agro-physiological and biochemical parameters suggested that the genotypes FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, along with crosses FBS-FALCON × JSQ-White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 achieved the maximum values for K+/Na+, K+, TSP, POD, Chlb, CAT, Car, LP, FS, FL, PH, NBP, BW, and SCY under salt stress and declared as salt resistant genotypes. The above-mentioned genotypes also showed relatively higher expression levels of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 at 15 dS/m and proved the role of these ERF genes in salt tolerance in cotton. These findings suggest that these genotypes have the potential for the development of salt-tolerant cotton varieties with desirable fiber quality traits.

Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis

Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value<0.05) divided into risk groups using extracted latent variables from the fused multi-omics data. The pipeline is open source to help researchers and clinicians identify the patients' risk groups using genomics.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Updated review on analysis of long non-coding RNAs as emerging diagnostic and therapeutic targets in prostate cancers

Despite advancements, prostate cancers (PCa) pose a significant global health challenge due to delayed diagnosis and therapeutic resistance. This review delves into the complex landscape of prostate cancer, with a focus on long-noncoding RNAs (lncRNAs). Also explores the influence of aberrant lncRNAs expression in progressive PCa stages, impacting traits like proliferation, invasion, metastasis and therapeutic resistance. The study elucidates how lncRNAs modulate crucial molecular effectors, including transcription factors and microRNAs, affecting signaling pathways such as androgen receptor signaling. Besides, this manuscript sheds light on novel concepts and mechanisms driving PCa progression through lncRNAs, providing a critical analysis of their impact on the disease's diverse characteristics. Besides, it discusses the potential of lncRNAs as diagnostics and therapeutic targets in PCa. Collectively, this work highlights state of art mechanistic comprehension and rigorous scientific approaches to advance our understanding of PCa and depict innovations in this evolving field of research.

Role of long non-coding RNA ELFN1-AS1 in carcinogenesis

As one of the leading causes of death worldwide, cancer significantly burdens patients and the healthcare system. The role of long non-protein coding RNAs (lncRNAs) in carcinogenesis has been extensively studied. The lncRNA ELFN1-AS1 was discovered recently, and subsequent studies have revealed its aberrantly high expression in various cancer tissues. In vitro and in vivo experiments have consistently demonstrated the close association between increased ELFN1-AS1 expression and malignant tumor characteristics, particularly in gastrointestinal malignancies. Functional assays have further revealed the mechanistic role of ELFN1-AS1 as a competitive endogenous RNA for microRNAs, inducing tumor growth, invasive features, and drug resistance. Additionally, the investigation into the clinical implication of ELFN1-AS1 has demonstrated its potential as a diagnostic, therapeutic, and, notably, prognostic marker. This review provides a comprehensive summary of evidence regarding the involvement of ELFN1-AS1 in cancer initiation and development, highlighting its clinical significance.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

Beyond the silence: A comprehensive exploration of long non-coding RNAs as genetic whispers and their essential regulatory functions in cardiovascular disorders

Long non-coding RNAs (lncRNAs) are RNA molecules that regulate gene expression at several levels, including transcriptional, post-transcriptional, and translational. They have a length of more than 200 nucleotides and cannot code. Many human diseases have been linked to aberrant lncRNA expression, highlighting the need for a better knowledge of disease etiology to drive improvements in diagnostic, prognostic, and therapeutic methods. Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide. LncRNAs play an essential role in the complex process of heart formation, and their abnormalities have been associated with several CVDs. This Review article looks at the roles and relationships of long non-coding RNAs (lncRNAs) in a wide range of CVDs, such as heart failure, myocardial infarction, atherosclerosis, and cardiac hypertrophy. In addition, the review delves into the possible uses of lncRNAs in diagnostics, prognosis, and clinical treatments of cardiovascular diseases. Additionally, it considers the field's future prospects while examining how lncRNAs might be altered and its clinical applications.

PulmoNet: a novel deep learning based pulmonary diseases detection model

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model's performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.

Melatonin affects the expression of microRNA-21: A mini-review of current evidence

Melatonin (MLT) is an endogenous hormone produced by pineal gland which possess promising anti-tumor effects. Anti-inflammatory and anti-oxidant properties of MLT, along with its immunomodulatory, proapoptotic, and anti-angiogenic properties, are often referred to the main mechanisms of its anti-tumor effects. Recent evidence has suggested that epigenetic alterations are also involved in the anti-tumor properties of MLT. Among these MLT-induced epigenetic alterations is modulation of the expression of several oncogenic and tumor suppressor microRNAs(miRNAs). MiRNAs are among the most promising and potential therapeutic and diagnostic tools in different diseases and enhanced the development of better therapeutic drugs. Suppression of oncomicroRNAs such as microRNA-21, - 20a, and - 27a as well as, up-regulation of microRNA-34 a/c are among the most important effects of MLT on microRNAs homeostasis. Recently, miR-21 has attracted the attention of scientists due to the its wide range of effects on different cancers and diseases. Regulation of this RNA may be a key to the development of better therapeutic targets. The present review will summarize the findings of in vitro and experimental studies of MLT-induced impacts on the expression of microRNAs which are involved in different models and numerous stages of tumor initiation, growth, metastasis, and chemo-resistance.

Mechanisms of Plant Epigenetic Regulation in Response to Plant Stress: Recent Discoveries and Implications

Plant stress is a significant challenge that affects the development, growth, and productivity of plants and causes an adverse environmental condition that disrupts normal physiological processes and hampers plant survival. Epigenetic regulation is a crucial mechanism for plants to respond and adapt to stress. Several studies have investigated the role of DNA methylation (DM), non-coding RNAs, and histone modifications in plant stress responses. However, there are various limitations or challenges in translating the research findings into practical applications. Hence, this review delves into the recent recovery, implications, and applications of epigenetic regulation in response to plant stress. To better understand plant epigenetic regulation under stress, we reviewed recent studies published in the last 5-10 years that made significant contributions, and we analyzed the novel techniques and technologies that have advanced the field, such as next-generation sequencing and genome-wide profiling of epigenetic modifications. We emphasized the breakthrough findings that have uncovered specific genes or pathways and the potential implications of understanding plant epigenetic regulation in response to stress for agriculture, crop improvement, and environmental sustainability. Finally, we concluded that plant epigenetic regulation in response to stress holds immense significance in agriculture, and understanding its mechanisms in stress tolerance can revolutionize crop breeding and genetic engineering strategies, leading to the evolution of stress-tolerant crops and ensuring sustainable food production in the face of climate change and other environmental challenges. Future research in this field will continue to unveil the intricacies of epigenetic regulation and its potential applications in crop improvement.

Gene expression insights: Chronic stress and bipolar disorder: A bioinformatics investigation

Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD.

Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model

Introduction

Endometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. Its clinical presentation includes abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the patient's quality of life. However, the pathogenesis underlying EM and associated regulatory genes are unknown.

Methods

Telomere-related genes (TRGs) were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the pathways involved in the action of the signature genes. Finally, the CAMP database was used to screen drugs for potential use in EM treatment.

Results

Fifteen total genes were screened as EM–telomere differentially expressed genes. Further screening by machine learning obtained six genes as characteristic predictive of EM. Immuno-infiltration analysis of the telomeric genes showed that expressions including macrophages and natural killer cells were significantly higher in cluster A. Further enrichment analysis showed that the differential genes were mainly enriched in biological pathways like cell cycle and extracellular matrix. Finally, the Connective Map database was used to screen 11 potential drugs for EM treatment.

Discussion

TRGs play a crucial role in EM development, and are associated with immune infiltration and act on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for EM.

Bitter-RF: A random forest machine model for recognizing bitter peptides

Introduction

Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides.

Methods

In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set.

Results

The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides.

Discussion

We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.

Neural networks prediction of the protein-ligand binding affinity with circular fingerprints

BACKGROUND

Protein-ligand binding affinity is of significant importance in structure-based drug design. Recently, the development of machine learning techniques has provided an efficient and accurate way to predict binding affinity. However, the prediction performance largely depends on how molecules are represented.

OBJECTIVE

Different molecular descriptors are designed to capture different features. The study aims to identify the optimal circular fingerprints for predicting protein-ligand binding affinity with matched neural network architectures.

METHODS

Extended-connectivity fingerprints (ECFP) and protein-ligand extended connectivity fingerprints (PLEC) encode circular atomic and bonding connectivity environments with the preference for intra- and inter-molecular features, respectively. Densely-connected neural networks are employed to map the circular fingerprints of protein-ligand complexes to binding affinitiesRESULTS:The performance of neural networks is sensitive to the parameters used for ECFP and PLEC fingerprints. The R2_score of the evaluated ECFP and PLEC fingerprints reaches 0.52 and 0.49, higher than that of the improperly set ECFP and PLEC fingerprints with R2_score of 0.45 and 0.38, respectively. Additionally, compared to the predictions from the standalone fingerprints, the ECFP+PLEC conjoint ones slightly improve the prediction accuracy with R2_score of approximately 0.55.

CONCLUSION

Both intra- and inter-molecular structural features encoded in the circular fingerprints contribute to the protein-ligand binding affinity. Optimizing the parameters of ECFP and PLEC can enhance performance. The conjoint fingerprint scheme can be generally extended to other molecular descriptors for enhanced feature engineering and improved predictive performance.

Feature Genes in Neuroblastoma Distinguishing High-Risk and Non-high-Risk Neuroblastoma Patients: Development and Validation Combining Random Forest With Artificial Neural Network

There is a significant difference in prognosis among different risk groups. Therefore, it is of great significance to correctly identify the risk grouping of children. Using the genomic data of neuroblastoma samples in public databases, we used GSE49710 as the training set data to calculate the feature genes of the high-risk group and non-high-risk group samples based on the random forest (RF) algorithm and artificial neural network (ANN) algorithm. The screening results of RF showed that EPS8L1, PLCD4, CHD5, NTRK1, and SLC22A4 were the feature differentially expressed genes (DEGs) of high-risk neuroblastoma. The prediction model based on gene expression data in this study showed high overall accuracy and precision in both the training set and the test set (AUC = 0.998 in GSE49710 and AUC = 0.858 in GSE73517). Kaplan–Meier plotter showed that the overall survival and progression-free survival of patients in the low-risk subgroup were significantly better than those in the high-risk subgroup [HR: 3.86 (95% CI: 2.44–6.10) and HR: 3.03 (95% CI: 2.03–4.52), respectively]. Our ANN-based model has better classification performance than the SVM-based model and XGboost-based model. Nevertheless, more convincing data sets and machine learning algorithms will be needed to build diagnostic models for individual organization types in the future.

Construction of a Six-Gene Prognostic Risk Model Related to Hypoxia and Angiogenesis for Cervical Cancer

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model.

Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves.

Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores.

Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets.

Construction of a miRNA Signature Using Support Vector Machine to Identify Microsatellite Instability Status and Prognosis in Gastric Cancer

Background. The specific role and prognostic value of DNA repair and replication-associated miRNAs in gastric cancer (GC) have not been clearly elucidated. Therefore, comprehensive analysis of miRNAs in GC is crucial for proposing therapeutic strategies and survival prediction. Methods. Firstly, clinical information and transcriptome data of TCGA-GC were downloaded from the database. In the entire cohort, we performed differential analysis in all miRNAs and support vector machine (SVM) was used to eliminate redundant miRNAs. Subsequently, we combined survival data and cox regression analysis to construct a miRNA signature in the training cohort. In addition, we used PCA, Kaplan-Meier, and ROC analysis to explore the prognosis value of risk score in the training and testing cohort. It is worth noting that multiple algorithms were used to evaluate difference of immune microenvironment (TME), microsatellite instability (MSI), tumor mutational burden (TMB), and immunotherapy in different risk groups. Finally, we investigated the potential mechanism about miRNA signature. Results. We constructed miRNA signature based on the following 4 miRNAs: hsa-miR-139-5p, hsa-miR-139-3p, hsa-miR-146b-5p, and hsa-miR-181a-3p. Univariate and multivariate Cox regression analyses suggested that risk score is a risk factor and an independent prognostic factor in GC patients. The AUC value of ROC analysis showed a robust prediction accuracy in each cohort. Moreover, significant differences in immune functions, immune cell content, immune checkpoint, MSI status, and TMB score were excavated in different groups distinguished by risk score. Finally, based on the above four miRNA target genes, we revealed that the signature was enriched in DNA repair and replication. Conclusion. We have developed a robust risk-formula based on 4 miRNAs that provides accurate risk stratification and prognostic prediction for GC patients. In addition, different risk subgroups may potentially guide the choice of targeted therapy.