Article
Automation & Control Systems
Weicheng Guo, Chongjun Wu, Zishan Ding, Qinzhi Zhou
Summary: This article presents a novel prediction system for surface roughness by collecting signals during grinding process, extracting features, and utilizing long short-term memory network for accurate prediction. The proposed system shows excellent prediction performance and reduced costs, proving its practicality and feasibility.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Lizeng Gong, Shanshan Xie, Yan Zhang, Mengyao Wang, Xiaoyan Wang
Summary: This paper proposes a feature selection method based on factor analysis, which improves the classification accuracy and reduces the dimensionality by removing redundancy and obtaining the optimal feature subsets.
Article
Remote Sensing
Masud Ibn Afjal, Md. Nazrul Islam Mondal, Md. Al Mamun
Summary: The use hyperspectral imaging sensors improves the classification of remotely sensed data, but the numerous wavelength bands captured in hyperspectral images can hinder the classification process. To overcome this, feature extraction and feature selection techniques are employed. Linear discriminant analysis is commonly used for feature extraction, but it has limitations in preserving intrinsic characteristics and selecting a low number of features.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Vikas Singh, Nishchal K. Verma
Summary: In recent years, intelligent condition-based monitoring of rotary machinery systems has become a major research focus. Using mRMR and deep learning models can improve fault diagnostics performance by reducing data redundancy and decreasing data dependency for training the model. The proposed frameworks show better diagnostic accuracy and faster processing of data with many features.
IEEE SENSORS JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Chengzhe Lv, Yuefeng Lu, Miao Lu, Xinyi Feng, Huadan Fan, Changqing Xu, Lei Xu
Summary: In this study, a feature dimension reduction algorithm combining Fisher Score and mRMR feature selection method was proposed and tested on GF-2 remote sensing imagery. The experimental analysis showed that this method provides higher accuracy in remote sensing image classification.
APPLIED SCIENCES-BASEL
(2022)
Article
Biochemical Research Methods
Pooja Arora, Neha Periwal, Yash Goyal, Vikas Sood, Baljeet Kaur
Summary: This study presents an improved method for predicting IL-13-inducing peptides, which provides a more accurate tool for identifying and classifying such peptides, contributing to the discovery of novel therapies. The results show that this method outperforms the existing approaches in terms of sensitivity, specificity, accuracy, AUCROC, and other performance metrics.
BMC BIOINFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Shanshan Xie, Yan Zhang, Danjv Lv, Xu Chen, Jing Lu, Jiang Liu
Summary: Feature selection plays a crucial role in pattern recognition and data mining. This paper proposes an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on feature subset, which adjusts the weights of measurement criteria and generates candidate feature subsets using equal grouping and incremental search methods. Experimental results demonstrate that ImRMR can effectively remove irrelevant and redundant features, leading to improved classification performance.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Biochemical Research Methods
Guohao Lv, Yingchun Xia, Zhao Qi, Zihao Zhao, Lianggui Tang, Cheng Chen, Shuai Yang, Qingyong Wang, Lichuan Gu
Summary: This paper proposes a reweighting boosting feature selection method for predicting LncRNA-protein interactions. The experimental results demonstrate that this method achieves higher accuracy and less redundancy with fewer features.
BMC BIOINFORMATICS
(2023)
Article
Chemistry, Medicinal
Lei Deng, Ying Jiang, Xiaowen Hu, Rongtao Zheng, Zhijian Huang, Jingpu Zhang
Summary: With the development of ribosome profiling, sequencing technology, and proteomics, there is growing evidence that noncoding RNA (ncRNA) may serve as a new source of peptides or proteins. These molecules play important roles in inhibiting tumor progression and interfering with cancer metabolism and other physiological processes. In this study, we propose a new network called ABLNCPP that utilizes an attention mechanism-based bidirectional LSTM network to assess the coding potential of ncRNAs. Our evaluations demonstrate that ABLNCPP outperforms other existing models, providing valuable insights for cancer research and treatment.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Genetics & Heredity
Qian Wang, Jun Ye, Teng Xu, Ning Zhou, Zhongqiu Lu, Jianchao Ying
Summary: This study developed a classifier for predicting prokaryotic transposases (Tnps) in bacteria and archaea using machine learning (ML) approaches, achieved good performance with an average AUC of 0.955, and established a stand-alone command-line tool named TnpDiscovery for Tnp prediction, demonstrating the effectiveness of ML approaches in identifying Tnps.
MICROBIAL GENOMICS
(2021)
Article
Ecology
Shanshan Xie, Jing Lu, Jiang Liu, Yan Zhang, Danjv Lv, Xu Chen, Youjie Zhao
Summary: Birds, as important members of the ecosystem, are good indicators of the ecological environment. This paper proposes a birdsong classification model that combines deep learning and machine learning by utilizing multi-view features. The experimental results show that this method achieves higher accuracy and lower dimensionality in birdsong recognition.
ECOLOGICAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
S. Eskandari, M. Seifaddini
Summary: This paper proposes a new approach for streaming feature selection by defining the redundancy analysis step as a binary optimization problem and adopting the binary bat algorithm to find the minimal informative subsets. Experimental studies show that this method outperforms other online and offline streaming feature selection methods in terms of classification accuracy.
PATTERN RECOGNITION
(2023)
Article
Genetics & Heredity
Bin Liu, Ziman Yang, Qing Liu, Ying Zhang, Hui Ding, Hongyan Lai, Qun Li
Summary: This study proposes a machine learning model based on multi-feature fusion to efficiently predict allergenic proteins. By extracting and optimizing protein sequence features, the model can accurately distinguish allergenic proteins from non-allergenic proteins, providing guidance for users to identify allergenic proteins.
FRONTIERS IN GENETICS
(2023)
Article
Automation & Control Systems
Adnan Khan, Jamal Uddin, Farman Ali, Ameen Banjar, Ali Daud
Summary: Antifreeze proteins (AFPs) are found in various organisms and play a crucial role in preventing the formation of ice crystals. The development of accurate predictors for identifying AFPs is essential. This review article provides a comprehensive summary of existing AFP predictors, including their applied datasets, feature descriptors, model training classifiers, performance assessment parameters, and web servers. The drawbacks of current predictors are highlighted, and suggestions for future improvements, such as more effective feature descriptors and efficient classifiers, are discussed.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Biochemistry & Molecular Biology
Zheng Chen, Shihu Jiao, Da Zhao, Abd El-Latif Hesham, Quan Zou, Lei Xu, Mingai Sun, Lijun Zhang
Summary: This study developed a tool for identifying channel proteins using feature coding methods and machine learning classifiers, and established an efficient prediction model. The results showed that CAPs-LGBM performed well in identifying channel proteins.
FRONTIERS IN BIOSCIENCE-LANDMARK
(2022)
Article
Biology
Xianchao Zhou, Shijian Ding, Deling Wang, Lei Chen, Kaiyan Feng, Tao Huang, Zhandong Li, Yudong Cai
Summary: A computational pipeline was developed to investigate the pathological mechanisms of skin diseases and identify potential therapeutic and diagnostic targets.
Article
Biotechnology & Applied Microbiology
Qiao Sun, Lin Bai, Shaopin Zhu, Lu Cheng, Yang Xu, Yu-Dong Cai, Hui Chen, Jian Zhang
Summary: This study utilized gene ontology and KEGG pathway analyses to identify lymphoma-associated genes and determine their biological processes. Features were selected and ranked using various methods, and a decision tree model was used to extract classification rules. The predicted features were consistent with recent publications and provide a new perspective for understanding the molecular mechanisms of lymphoma.
BIOMED RESEARCH INTERNATIONAL
(2022)
Article
Biotechnology & Applied Microbiology
FeiMing Huang, Lei Chen, Wei Guo, Tao Huang, Yu-dong Cai
Summary: The study constructs efficient classifiers based on single-cell RNA sequencing data and identifies essential gene biomarkers, while also mining a series of classification rules that can distinguish different cell cycle phases, providing a novel method for determining the cell cycle and identifying new potential cell cycle-related genes.
BIOMED RESEARCH INTERNATIONAL
(2022)
Article
Chemistry, Multidisciplinary
Lei Chen, Kuangshi Sun, Donghao Hu, Xianlong Su, Linna Guo, Jiamiao Yin, Yuetian Pei, Yiwei Fan, Qian Liu, Ming Xu, Wei Feng, Fuyou Li
Summary: Photochemical afterglow systems have attracted significant attention for their adjustable photophysical properties and potential applications. However, conventional photochemical afterglow lacks repeatability due to the consumption of energy cache units. In this study, we propose a novel strategy to achieve repeatable photochemical afterglow through the reversible storage of O-1(2). This strategy enables the generation of near-infrared afterglow with a lifetime over 10 s, and its initial intensity remains stable over 50 excitation cycles. Mechanism study confirms the repeatable photochemical afterglow is realized through singlet oxygen-sensitized fluorescence emission. The generality of this strategy is demonstrated, allowing for tunable afterglow lifetimes and colors through rational design. Furthermore, the repeatable photochemical afterglow is applied for attacker-misleading information encryption, providing repeatable readout.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2023)
Article
Biotechnology & Applied Microbiology
FeiMing Huang, QingLan Ma, JingXin Ren, JiaRui Li, Fen Wang, Tao Huang, Yu-Dong Cai
Summary: Long-term cigarette smoking is associated with various human diseases, and this study used advanced machine learning methods to identify specific isoforms and pathways that play important roles in distinguishing smokers from former smokers. The study evaluated multiple feature selection algorithms and utilized a decision tree approach to establish high-performance classification models. The identified isoforms and classification rules were validated through previous research. The results highlight the relevance of isoforms such as ENST00000464835, ENST00000622663, and ENST00000284311, as well as pathways related to smoking response.
BIOMED RESEARCH INTERNATIONAL
(2023)
Article
Chemistry, Organic
Alma R. Perez, Evan C. Bornowski, Lei Chen, John P. Wolfe
Summary: The synthesis of indanes containing substituted cyanomethyl groups at C2 has been achieved through Pd-catalyzed coupling reactions. Alkenyl triflates were used to generate partially saturated analogues via similar transformations. The use of a preformed BrettPhosPd(allyl)(Cl) complex as a precatalyst was crucial for the success of these reactions.
Article
Biology
Jingxin Ren, Yuhang Zhang, Wei Guo, Kaiyan Feng, Ye Yuan, Tao Huang, Yu-Dong Cai
Summary: COVID-19 can cause impairment of smell and taste, and this study used machine learning to analyze gene expression levels in COVID-19 patient samples to identify important biomarkers associated with this loss of sensory ability. The study suggests potential mechanisms for COVID-19 complications and provides biomarkers for predicting olfactory and gustatory impairment.
Article
Biology
Yaochen Xu, Qinglan Ma, Jingxin Ren, Lei Chen, Wei Guo, Kaiyan Feng, Zhenbing Zeng, Tao Huang, Yudong Cai
Summary: COVID-19 not only damages the respiratory system, but also puts strain on the cardiovascular system. This study analyzed the gene expression levels of vascular endothelial cells and cardiomyocytes in COVID-19 patients and healthy controls using a machine learning-based workflow. The findings suggest that COVID-19 affects the gene expression levels in cardiac cells, providing insights into the pathogenesis of COVID-19 and potential therapeutic targets.
Article
Biology
Qinglan Ma, FeiMing Huang, Wei Guo, KaiYan Feng, Tao Huang, Yudong Cai
Summary: Phase-separation proteins (PSPs) play a role in liquid-liquid phase separation and have implications for cellular biology and disease development. Identifying PSPs and their functions can provide valuable insights.
Article
Biology
Qing-Lan Ma, Fei-Ming Huang, Wei Guo, Kai-Yan Feng, Tao Huang, Yu-Dong Cai
Summary: Vaccines elicit an immune response involving B and T cells, with B cells producing antibodies. The immunity to SARS-CoV-2 diminishes over time after vaccination. This study aimed to identify important changes in antigen-reactive antibodies post-vaccination to enhance vaccine efficacy.
Article
Biology
Jing-Xin Ren, Qian Gao, Xiao-Chao Zhou, Lei Chen, Wei Guo, Kai-Yan Feng, Lin Lu, Tao Huang, Yu-Dong Cai
Summary: A machine-learning-based method was used to analyze the scRNA-seq data of B cells, T cells, and myeloid cells from patients with COVID-19. Key genes related to SARS-CoV-2 infection were identified. The study revealed the dynamic changes in the immune system of COVID-19 patients at different stages, providing valuable insights into the ongoing effect of COVID-19 development on the immune system.
Article
Biology
Yong Yang, Yuhang Zhang, Jingxin Ren, Kaiyan Feng, Zhandong Li, Tao Huang, Yudong Cai
Summary: This study analyzed single-cell RNA sequencing data from a normal colon to identify genetic markers of 25 immune cell types and reveal quantitative differences between them. Machine learning-based methods were used to analyze the importance of gene features and classify the most important genetic markers. The results provide a reference for exploring the cell composition of the colon cancer microenvironment and clinical immunotherapy.