Article
Biochemical Research Methods
Changkun Jiang, Weipeng Lv, Jianqiang Li
Summary: In this study, a novel model combining convolutional neural networks (CNNs) with Batch Normalization is designed to predict protein-protein interaction sites (PPIs), using an oversampling technique Borderline-SMOTE to address the problem of sample imbalance. The effectiveness of the method is validated by comparing it with existing state-of-the-art schemes, and achieved improved accuracies on three public datasets.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Yushuang Liu, Shuping Jin, Lili Song, Yu Han, Bin Yu
Summary: This study introduces a ubiquitination site prediction method, UbiSite-XGBoost, based on multi-view features. Experimental results demonstrate that this method is superior to other ubiquitination prediction methods.
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
(2021)
Article
Biochemical Research Methods
Hao Lv, Yang Zhang, Jia-Shu Wang, Shi-Shi Yuan, Zi-Jie Sun, Fu-Ying Dao, Zheng-Xing Guan, Hao Lin, Ke-Jun Deng
Summary: In this study, a comprehensive method called iRice-MS based on eXtreme Gradient Boosting (XGBoost) was developed to identify multiple post-translational modifications (PTMs) in rice. The method displayed excellent performance in cross-validation and independent dataset test, and showed superiority to existing tools in terms of AUC value.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Agriculture, Multidisciplinary
Liang Han, Guijun Yang, Xiaodong Yang, Xiaoyu Song, Bo Xu, Zhenhai Li, Jintao Wu, Hao Yang, Jianwei Wu
Summary: This study uses machine learning models based on remote sensing images to detect crop lodging. The study uses Synthetic Minority Oversampling Technique and Edited Nearest Neighbors to handle imbalanced datasets, and proposes the SMOTE-ENN-XGBoost model for identifying maize lodging at the plot scale. SHapley Additive exPlanations approach is employed to interpret the features that determine lodging classification and activity prediction. The results suggest that canopy structure, spectral, and textural features should be considered simultaneously for accurate detection of crop lodging in crop breeding programs.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Biochemical Research Methods
Yan Zheng, Hao Wang, Yijie Ding, Fei Guo
Summary: The study emphasized the importance of identifying DNase I hypersensitive sites (DHSs) using computational techniques based on composition information and physicochemical properties. By enhancing the feature selection model CEPZ, the research achieved significant improvements in accuracy and Matthews correlation coefficient, indicating its potential as a valuable tool for future DHS research.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Environmental Sciences
Mengying Lin, Xuefen Zhu, Teng Hua, Xinhua Tang, Gangyi Tu, Xiyuan Chen
Summary: An improved XGBoost algorithm was proposed to detect ionospheric scintillation events, showing significant effectiveness in detecting events imbalanced with respect to weak, medium, and strong ionospheric scintillation. By using the synthetic minority oversampling technique and edited nearest neighbor resampling technique, the method demonstrated high accuracy and stability in testing.
Article
Computer Science, Artificial Intelligence
Congjun Rao, Ying Liu, Mark Goh
Summary: This paper investigates the credit risk assessment mechanism for personal auto loans. A machine learning based model incorporating Smote-Tomek Link algorithm and Filter-Wrapper feature selection method is proposed. By combining Particle Swarm Optimization and eXtreme Gradient Boosting model, a superior PSO-XGBoost model is formed, showing better classification performance and effect.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Tao Ma, Lizhou Wu, Shuairun Zhu, Hongzhou Zhu
Summary: This study investigates the performance of extreme gradient boosting (XGBoost) in predicting multiclass clay sensitivity and the ability of synthetic minority over-sampling technique (SMOTE) in addressing imbalanced categories. The results show that XGBoost performs the best in the prediction of clay sensitivity, while SMOTE is useful in addressing imbalanced issues.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Hao Tian, Xi Jiang, Peng Tao
Summary: Allostery plays a crucial role in regulating protein activity, and identifying allosteric sites is essential for drug development. The ensemble learning method presented in this study shows good performance in predicting allosteric sites, with 84.9% accuracy in the test set.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2021)
Article
Chemistry, Multidisciplinary
Xuemei Wang, Ping Wu
Summary: This paper proposes an improved process monitoring method by considering autocorrelation among process data, integrating ensemble learning and kernel canonical variate analysis. The method achieves significantly enhanced fault detection performance.
Article
Genetics & Heredity
Minli Tang, Longxin Wu, Xinyu Yu, Zhaoqi Chu, Shuting Jin, Juan Liu
Summary: A new HANPPIS model was proposed in this study to predict protein-protein interaction sites by adding six protein sequence features, which proved to be effective and superior. Additionally, the use of a double-layer attention mechanism improved the interpretability of the model and solved the black box problem of deep neural networks.
FRONTIERS IN GENETICS
(2021)
Review
Genetics & Heredity
Ziye Zhao, Wen Yang, Yixiao Zhai, Yingjian Liang, Yuming Zhao
Summary: The exploration of DNA-binding proteins is crucial in studying biological life activities, and machine learning algorithms have shown excellent performance in detecting DBPs. Our method, using feature extraction and the XGBoost model, achieves better results with high accuracy and simplicity compared to other methods.
FRONTIERS IN GENETICS
(2022)
Article
Construction & Building Technology
Mohammad Hassan Daneshvari, Ebrahim Nourmohammadi, Mahmoud Ameri, Barat Mojaradi
Summary: The primary cause of decreasing road safety, comfort, and service life is the raveling of asphalt pavement. This study proposes and verifies a computer vision technique based on image texture features for automatic detection of asphalt pavement raveling. Two feature extraction scenarios are compared, and the results show that the second scenario offers higher prediction performance.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Article
Green & Sustainable Science & Technology
Hongbin Dai, Guangqiu Huang, Huibin Zeng, Fan Yang
Summary: This study optimized the prediction model for atmospheric pollutant PM2.5 concentration using XGBoost and MSCNN, extracting spatio-temporal feature relationships and optimizing parameters with genetic algorithm. The experimental results demonstrate that the model offers higher accuracy and generalization ability in predicting PM2.5 concentration.
Article
Genetics & Heredity
Yinbo Liu, Yingying Shen, Hong Wang, Yong Zhang, Xiaolei Zhu
Summary: In this study, a new method called m5Cpred-XS was proposed to predict m5C sites in three different organisms. The method utilized powerful feature selection and machine learning algorithms to train the models, and its superiority was confirmed through comparison with other methods. A web server was also deployed for easy access to the model, making it a useful tool for studying m5C sites.
FRONTIERS IN GENETICS
(2022)
Article
Biochemical Research Methods
Yushuang Liu, Shuping Jin, Hongli Gao, Xue Wang, Congjing Wang, Weifeng Zhou, Bin Yu
Summary: This article proposes a novel method called ML-locMLFE, which can effectively predict the multi-label subcellular localization of proteins and has obvious advantages. By using different feature extraction methods and information processing methods, this method demonstrates good accuracy in predicting the protein localization of diseases such as SARS-CoV-2.
Article
Mathematical & Computational Biology
Yan Zhang, Zhiwen Jiang, Cheng Chen, Qinqin Wei, Haiming Gu, Bin Yu
Summary: Accurate prediction of drug-target interactions is a key challenge in drug science, and the proposed method DeepStack-DTIs achieves higher accuracy compared to existing methods by extracting various features and utilizing a stacked ensemble classifier. The method shows excellent predictive ability on different datasets, providing new insights for drug-target interaction prediction.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Bin Yu, Xue Wang, Yaqun Zhang, Hongli Gao, Yifei Wang, Yushuang Liu, Xin Gao
Summary: In this study, a deep learning-based framework called RPI-MDLStack is developed for predicting RNA-protein interactions (RPI). By optimizing feature extraction and using a stacking ensemble strategy, RPI-MDLStack achieves high prediction accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Automation & Control Systems
Yaqun Zhang, Zhaomin Yu, Bin Yu, Xue Wang, Hongli Gao, Jianqiang Sun, Shuangyi Li
Summary: This paper proposes a novel cross-species computational method StackRAM for identifying m(6)A sites in RNA. The method utilizes machine learning algorithms and features fusion and selection techniques to improve prediction accuracy. Experimental results demonstrate that StackRAM has superior prediction performance in multiple species and is of great significance for studying the biological functions and mechanisms of m(6)A modification.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Minghui Wang, Lili Song, Yaqun Zhang, Hongli Gao, Lu Yan, Bin Yu
Summary: In this paper, a new prediction model, Malsite-Deep, is proposed for predicting protein malonylation sites. The model combines feature extraction and deep neural networks to achieve accurate predictions, and its performance is evaluated on multiple test sets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Wankun Chen, Weifeng Zhou, Ling Zhu, Yuan Cao, Haiming Gu, Bin Yu
Summary: A novel 3D multithreading dilated convolutional network (MTDC-Net) was proposed for automatic brain tumor segmentation. By introducing multi-threading dilated convolution strategy, pyramid matrix fusion algorithm, spatial pyramid convolution operation, and multi-threading adaptive pooling up-sampling strategy, the model achieved high dice scores on public validation datasets.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Qinqin Wei, Qingmei Zhang, Hongli Gao, Tao Song, Adil Salhi, Bin Yu
Summary: This article introduces a novel RBPs prediction tool, DEEPStack-RBP, based on deep learning and ensemble learning. The tool utilizes various feature extraction methods and employs autoencoder and sample balancing techniques for prediction. Experimental results show that DEEPStack-RBP achieves high accuracy and MCC values, making it a powerful tool for RBPs prediction.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Biology
Hongli Gao, Cheng Chen, Shuangyi Li, Congjing Wang, Weifeng Zhou, Bin Yu
Summary: In this paper, the EResCNN model is developed to predict protein-protein interactions using deep learning techniques. The model combines multiple feature representation methods and utilizes a residual convolutional neural network to capture high-level information. Experimental results show that EResCNN achieves good predictive performance on different datasets and can be applied to cross-species prediction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemical Research Methods
Xiaolin Wang, Hongli Gao, Ren Qi, Ruiqing Zheng, Xin Gao, Bin Yu
Summary: This study proposes a novel clustering method called scBKAP, which addresses the issues of high dropout rate and curse of dimensionality in scRNA-seq data by utilizing an autoencoder network and a dimensionality reduction model MPDR. Comprehensive experiments on 21 public scRNA-seq datasets and simulated datasets demonstrate the superior performance of scBKAP over nine state-of-the-art single-cell clustering methods.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Pengju Ding, Yifei Wang, Xinyu Zhang, Xin Gao, Guozhu Liu, Bin Yu
Summary: In this paper, a deep learning model called DeepSTF is proposed to predict transcription factor binding sites (TFBSs) by integrating DNA sequence and shape profiles. Experimental results show that DeepSTF significantly outperforms other algorithms in predicting TFBSs, and the usefulness of the transformer encoder structure and the combined strategy using sequence features and shape profiles in capturing multiple dependencies and learning essential features is explained.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Hongli Gao, Bin Zhang, Long Liu, Shan Li, Xin Gao, Bin Yu
Summary: In this study, a universal framework called GCN-SC is proposed for integrating single-cell multi-omics data. GCN-SC selects one dataset as the reference and the rest as the query datasets, and uses mutual nearest neighbor algorithm to identify cell-pairs that connect cells within and across datasets. Then, a GCN algorithm adjusts the count matrices from query datasets, followed by dimension reduction using non-negative matrix factorization.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Yutong Yu, Pengju Ding, Hongli Gao, Guozhu Liu, Fa Zhang, Bin Yu
Summary: Interactions between DNA and transcription factors play a vital role in understanding transcriptional regulation and gene expression. Deep learning methods, such as the proposed DSAC model, combining self-attention and convolution, have shown great potential in predicting TF binding sites and enhancing the representation learning. The experiment results demonstrate that DSAC outperforms other deep learning methods in predicting TFBSs based on sequence features alone.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Mingxiang Zhang, Hongli Gao, Xin Liao, Baoxing Ning, Haiming Gu, Bin Yu
Summary: This article introduces a new method called DBGRU-SE for predicting drug-drug interactions. It utilizes various techniques such as feature extraction, feature selection, and data balancing to achieve good predictive performance. The results demonstrate high accuracy and area under the curve (AUC) values, indicating its effectiveness in predicting drug-drug interactions.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biology
Minghui Wang, Lu Yan, Jihua Jia, Jiali Lai, Hongyan Zhou, Bin Yu
Summary: In this paper, a new prediction model called DE-MHAIPs is proposed to accurately identify phosphorylation sites of SARS-CoV-2. The model combines six feature extraction methods, utilizes differential evolution algorithm to learn feature weights, and employs Group LASSO for feature selection. The experimental results demonstrate that DE-MHAIPs method shows excellent predictive ability compared with other methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Tingting Zhang, Jihua Jia, Cheng Chen, Yaqun Zhang, Bin Yu
Summary: S-sulfenylation is an important post-translational modification of proteins that has implications for signal transduction and protein function regulation. Predicting S-sulfenylation sites using computational methods is crucial for studying protein function and related biological mechanisms due to experimental limitations. In this paper, a method called BiGRUD-SA, based on BiGRU and self-attention mechanism, is proposed to predict protein S-sulfenylation sites.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)