Article
Computer Science, Information Systems
Zhaoxi Zhang, Yulan Zhao, Juan Wang, Maozu Guo
Summary: This study developed a novel method called DeepRCI for predicting ATP-binding proteins, achieving high accuracy through experiments and model selection. The comparison of residue-residue contact information datasets showed that high noise levels can reduce prediction accuracy, but this problem is expected to be solved with an increase in sequence data.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Bibhuti Bhusana Palai, Nagendra K. Sharma
Summary: This study demonstrates the significant influence of the troponyl group on the structure and conformation of short peptides, confirmed through various experimental methods.
Article
Biochemical Research Methods
Shima Shafiee, Abdolhossein Fathi, Ghazaleh Taherzadeh
Summary: Peptide-binding proteins play important roles in various applications. SPPPred is a novel ensemble machine learning-based approach that can predict protein-peptide binding residues with consistent and comparable performance.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Information Systems
Ji Yang, Shuning Zhang
Summary: The interaction between proteins and DNA is crucial in various biological processes. Identifying protein-DNA binding sites is important for understanding protein function and designing drugs. This study proposes a new method for predicting protein-DNA binding sites, based on neighboring residue correlations and improved feature representation. Experimental results demonstrate that this method outperforms other predictors and has significant implications in the field of biotechnology.
Article
Geochemistry & Geophysics
Haoyang Yu, Xiaodi Shang, Xiao Zhang, Lianru Gao, Meiping Song, Jiaochan Hu
Summary: In this letter, we analyze the SR-based framework from the perspective of sparse coefficient, develop the participation degree (PD)-driven decision mechanism, and establish a concise model called constraint representation (CR). An improved version called adjacent CR (ACR) is further proposed, considering spatial coherence via adjacent constraint. Experimental results using two real hyperspectral datasets verify the improvements of the proposed methods over the other related models and their spatial variants.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Automation & Control Systems
Georgios Mademlis, Nimananda Sharma, Yujing Liu, Junfei Tang
Summary: This article proposes a new technique for suppressing zero-sequence current in electrical machine emulators with reduced component count. The proposed control scheme effectively suppresses the current by compensating for common-mode voltage, eliminating the need for additional hardware filters. This technique can be applied in laboratory test-bench applications, offering a low-hardware requirement solution for electrical machine emulators.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Jiali Gu, Runjie Jin, Guangwei Chen, Zhanjiang Ye, Qi Li, Hengwei Wang, Dan Li, George Christakos, Susana Agusti, Carlos M. Duarte, Yongming Luo, Jiaping Wu
Summary: Coastal saltmarshes in China have been studied using remote sensing to delineate their spatial distribution and extent, identifying dominant species and their distribution regions. The results provide a baseline for future monitoring and conservation efforts.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Microbiology
Ailan Huang, Fuping Lu, Fufeng Liu
Summary: In this study, four machine learning methods and three descriptors were systematically analyzed to identify psychrophilic enzymes efficiently. The support vector machine model based on the amino acid composition descriptor achieved the best prediction accuracy of 80.6%. The amino acid frequencies between psychrophilic and non-psychrophilic proteins revealed specific patterns related to protein psychrophilicity. Furthermore, a ternary classification model was developed to effectively classify psychrophilic, mesophilic, and thermophilic proteins, with a predictive accuracy of 75.8%.
FRONTIERS IN MICROBIOLOGY
(2023)
Article
Geochemistry & Geophysics
Hua Wang, Weiwei Li, Xueye Chen, Jiqiang Niu
Summary: This study aims to improve the classification accuracy of hyperspectral images (HSIs) by using a multiscale superpixel segmentation method, and experimental results show a significant increase in effective classification accuracies across three datasets.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Genetics & Heredity
Wenxia Su, Shuyi Deng, Zhifeng Gu, Keli Yang, Hui Ding, Hui Chen, Zhaoyue Zhang
Summary: Apoptosis proteins play a crucial role in cellular apoptosis, regulating the balance between cell proliferation and death. The subcellular localization of these proteins is closely related to their functions and studying it is of great significance. This study proposes a novel method for predicting the subcellular location of apoptosis proteins using amphiphilic pseudo amino acid composition and support vector machine algorithm, achieving improved prediction accuracy.
FRONTIERS IN GENETICS
(2023)
Article
Forestry
Benjamin T. Fraser, Russell G. Congalton
Summary: The study compared visual interpretation and digital processing for forest plot composition and individual tree identification, finding that digital processing had higher accuracy in detecting individual trees and improved overall accuracy for forest composition.
Article
Biochemical Research Methods
Yinuo Lyu, Zhen Zhang, Jiawei Li, Wenying He, Yijie Ding, Fei Guo
Summary: This study introduces a novel enhancer predictor, iEnhancer-KL, which identifies and classifies enhancers using computational biology algorithms. KL divergence is creatively used to enhance feature extraction, and LASSO is employed for feature dimension reduction, leading to improved prediction performance with the SVM algorithm achieving the best results.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Jun Li, Guokang Fang
Summary: The proposed DGODE-SVM algorithm improves parameter selection for SVM by integrating opposition-based learning and hybrid competition between adjacent two generations. Experimental results demonstrate that it outperforms other algorithms in terms of classification accuracy.
Proceedings Paper
Computer Science, Artificial Intelligence
Parth C. Upadhyay, Lokesh Karanam, John A. Lory, Guilherme N. DeSouza
Summary: In this research, two approaches for determining the percentage of crop residue cover using SVM were compared. A hierarchical ensemble SVM classifier outperformed a single SVM classifier in terms of cross-validation and testing accuracy. Other metrics such as precision, recall, and F1 score also favored the ensemble SVM.
2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021)
(2021)
Article
Food Science & Technology
T. Garde-Cerdan, N. L. da Costa, P. Rubio-Breton, R. Barbosa, E. Baroja, J. M. Martinez-Vidaurre, S. Marin-San Roman, I. Saenz de Urturi, E. P. Perez-Alvarez
Summary: A study was conducted to differentiate Tempranillo and Tempranillo blanco grapes and wines from A.O.C. Rioja, Spain. Chemical compounds in the grapes and wines were analyzed using HPLC-DAD and GC-MS, with a machine learning approach employed to determine the most important parameters for discrimination. Four importance levels were established, with some chemical compounds showing good predictive capabilities for discrimination.
FOOD ANALYTICAL METHODS
(2021)