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Summary Anti‐nutritional factors in plant seeds diminish the utilization of nutrients in feed and food. Among these, protease inhibitors inhibit protein degradation by exogenous proteases during digestion. Through conventional and selection‐gene‐free genome editing using ovules as explants, we used simplex and multiplex CRISPR/Cas9 for studying the impact of chymotrypsin inhibitor CI‐1A, CI‐1B and CI‐2, Bowman‐Birk trypsin inhibitor, Serpin‐Z4, and barley ɑ‐amylase/subtilisin inhibitor on barley and soybean storage protein degradation. Mutants were generated in the commercial cultivar Stairway, having a high level of protease inhibition, and the barley model cultivar Golden Promise, having a lower inhibition level. In Golden Promise, all individual knockouts decreased the inhibition of the three proteases α‐chymotrypsin, trypsin and the commercial feed protease Ronozyme ProAct significantly. The triple knockout of all chymotrypsin inhibitors further decreased the inhibition of α‐chymotrypsin and Ronozyme ProAct proteases. Degradations of recombinant barley storage proteins B‐ and C‐hordeins were significantly improved following mutagenesis. In Stairway, a single knockout of CI‐1A almost compares to the effect on the proteases achieved for the triple knockout in Golden promise, uncovering CI‐1A as the major protease inhibitor in that cultivar. The Stairway mutant demonstrated significantly improved degradation of recombinant barley hordeins and in the soybean storage proteins glycinin and β‐conglycinin. The results of this study provide insights into cereal protease inhibitor genes and their negative effects on the degradation of barley storage protein and the most important plant protein from soybeans. The study suggests a future focus on plant protease inhibitors as a major target for improving feed and food protein digestibility.

Due to the disproportionate difference between the number of genes and samples, microarray data analysis is considered an extremely difficult task in sample classification. Feature selection mitigates this problem by removing irrelevant and redundant genes from data. In this paper, we propose a new methodology for feature selection that aims to detect relevant, non-redundant and interacting genes by analysing the feature value space instead of the feature space. Following this methodology, we also propose a new feature selection algorithm, namely Pavicd (Probabilistic Attribute-Value for Class Distinction). Experiments in fourteen microarray cancer datasets reveal that Pavicd obtains the best performance in terms of running time and classification accuracy when using Ripper-k and C4.5 as classifiers. When using SVM (Support Vector Machine), the Gbc (Genetic Bee Colony) wrapper algorithm gets the best results. However, Pavicd is significantly faster.

An important aspect in microarray data analysis is the selection of an appropriate number of the most relevant genes among a large population of genes. In this study, we have proposed a composite gene selection using both unsupervised and supervised gene selections. In the unsupervised gene selection, we used the threshold number of misclassification (TNoM) score to select an appropriate number of the top-ranked genes for microarray data analysis. In the supervised gene selection, the minimum number of genes showing the highest accuracy is obtained using the non-overlap area distribution measurement (NADM) method provided by the neural network with weighted fuzzy membership functions (NEWFM) from the top-ranked genes. In this study, from a colon cancer dataset and a leukemia dataset, we selected the top-ranked 93 colon cancer and 143 leukemia genes with ≤14 (colon cancer) and ≤13 (leukemia) TNoM scores from a total of 2000 colon cancer and 7129 leukemia genes. By the NADM method, a minimum of 4 colon cancer and 13 leukemia genes were selected from the top-ranked 93 colon cancer and 143 leukemia genes. When the minimal 4 colon cancer and 13 leukemia genes were used as inputs for the NEWFM, the performance accuracies were 98.39 % and 100 % for colon cancer and leukemia, respectively.

In the last two decades, analyzing microarray data plays a critical role in disease diagnosis and identification of different tumors. However, it is difficult to classify microarray data because of the curse of the dimensionality problem, in which the number of features is huge while the number of samples is small. Thus, dimension reduction techniques, such as feature selection methods, play a vital role in eliminating non-informative features and enhancing cancer classification. In this paper, we propose a Filter-embedded hybrid feature selection method for the gene selection problem. First, the proposed method selects the top-ranked features obtained from the Fisher score to provide a candidate subset for the embedded stage. Second, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) applies to the candidate subset to find the optimal subset. We assess the performance of our proposed method over ten high-dimensional microarray datasets. The results reveal that the proposed method enhances the classification accuracy, reduces the number of selected features, and decreases computational time.

Background Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc. Results In addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods. Conclusions FeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular filter methods and generates various comparison diagrams and statistical measurements. It is available from GitHub ( https://github.com/LBBSoft/FeatureSelect ) and is free open source software under an MIT license.

Mangrove sediments host a diverse array of microbial populations and are characterized by high heterogeneity along their vertical depths. However, the genetic diversity within these populations is largely unknown, hindering our understanding of their adaptive evolution across the sediment depths. To elucidate their genetic diversity, we utilized metagenome sequencing to identify 16 high‐frequency microbial populations comprised of two archaea and 14 bacteria from mangrove sediment cores (0–100 cm, with 10 depths) in Qi'ao Island, China. Our analysis of the genome‐wide genetic variation revealed extensive nucleotide diversity in the microbial populations. The genes involved in the transport and the energy metabolism displayed a high nucleotide diversity (HND; 0.0045–0.0195; an indicator of shared minor alleles with the microbial populations). By tracking the processes of homologous recombination, we found that each microbial population was subjected to different purification selection levels at different depths (44.12% genes). This selection resulted in significant differences in synonymous/non‐synonymous mutation ratio between 0–20 and 20–100 cm layers, indicating the adaptive evolutionary process of microbial populations. Furthermore, our assessment of differentiation in the allele frequencies between these two layers showed that the functional genes involved in the metabolic processes of amino acids or cofactors were highly differential in more than half of them. Together, we showed that the nucleotide diversity of microbial populations was shaped by homologous recombination and gene‐specific selection, finally resulting in the stratified differentiation occurring between 0–20 and 20–100 cm. These results enhance our cognition of the microbial adaptation mechanisms to vertical environmental changes during the sedimentation process of coastal blue carbon ecosystems. The alleles were widespread but not completely isolated across mangrove sediments. The genes showed wide nucleotide diversity among microbial populations. Gene‐specific selection reduced the non‐synonymous mutants in microbial populations.

The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancerous microarray datasets. Then, a genetic algorithm has been employed to further optimize and enhance the selected features in order to improve the proposed method’s capability for cancer classification. To test the proficiency of the proposed scheme, four cancerous microarray datasets were used in the study—this primarily included breast, lung, central nervous system, and brain cancer datasets. The experimental results show that the proposed hybrid filter-genetic feature selection approach achieved better performance of several common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure.

Background Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is “large p and small n” in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer classification. Results This study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies–Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes selected. Conclusions The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.

Microarray technology has revolutionized cancer genomics by enabling the simultaneous analysis of thousands of gene expressions, providing critical insights into gene regulation and disease mechanisms. However, the inherent challenges of high-dimensionality, noise, and sparsity in microarray data demand robust analytical approaches. Image processing techniques further enhance this analysis by extracting meaningful patterns from histological and microarray-derived visual data, aiding in biomarker discovery and classification. This study presents a novel framework leveraging deep neural networks for gene selection and cancer classification using microarray data, addressing the challenges of high dimensionality, noise, and sparsity. The proposed Gene-Optimized Neural Framework (GONF) integrates the Minimum Redundancy Maximum Relevance (mRMR) gene selection method with a deep Convolutional Neural Network (CNN) for effective feature selection and classification. By optimizing hyperparameters and employing advanced preprocessing techniques, the framework enhances computational efficiency and accuracy. Experiments were conducted on TCGA and AHBA datasets, utilizing metrics such as accuracy, precision and recall for evaluation. The GONF outperformed other methods, achieving a classification accuracy of 97% on the TCGA dataset and 95% on the AHBA dataset. The framework demonstrated significant reductions in false positive and false negative rates, improving cancer subtype predictions and providing biologically interpretable results. The findings highlight GONF’s robustness and adaptability, paving the way for its application in other genomic studies and clinical settings.

The classification of cancer based on gene expression profiles is a central challenge in precision oncology due to the high dimensionality and low sample size inherent in microarray datasets. Effective gene selection is crucial for improving classification accuracy while minimizing computational overhead and model complexity. This study introduces Genetic-Embedded Nuclear Reaction Optimization (GNR), a novel hybrid metaheuristic that enhances the conventional Nuclear Reaction Optimization (NRO) algorithm by embedding a genetic uniform crossover mechanism into its fusion phase. The proposed algorithm leverages a two-stage process: an initial F-score filtering step to reduce dimensionality, followed by GNR-driven optimization to identify compact, informative gene subsets. Evaluations were conducted on six widely used microarray cancer datasets, with Support Vector Machines (SVM) employed as classifiers and performance assessed via Leave-One-Out Cross-Validation (LOOCV). Results show that GNR consistently outperforms the original NRO and several benchmark hybrid algorithms, achieving 100% classification accuracy with significantly smaller gene subsets across all datasets. These findings confirm the efficacy of the genetic-embedded fusion strategy in enhancing local exploitation while preserving the global search capabilities of NRO, thereby offering a robust and interpretable approach for gene selection in cancer classification.