Article
Computer Science, Interdisciplinary Applications
Ahmed H. Alkenani, Yuefeng Li, Yue Xu, Qing Zhang
Summary: The study emphasizes the importance of automating the diagnosis of AD using language deficiency, developing multiple heterogeneous stacked fusion models to improve generalizability and robustness of AD diagnostic ML models. The models trained on two different datasets achieved high AUC, accuracy, and F1 score values. The suggestion is to replace traditional screening tests with these models for fully automated remote diagnosis.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Yuelong Xia, Ke Chen, Yun Yang
Summary: In this study, a novel stacked ensemble approach that simultaneously exploits label correlations and the process of learning weights of ensemble members is proposed. Experimental results demonstrate that the proposed algorithm outperforms related state-of-the-art methods in multi-label classification tasks.
INFORMATION SCIENCES
(2021)
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
Computer Science, Artificial Intelligence
Qingmei Zhang, Peishun Liu, Xue Wang, Yaqun Zhang, Yu Han, Bin Yu
Summary: In this paper, a method named StackPDB is proposed for predicting DNA-binding proteins (DBPs) using a stacked ensemble classifier, which shows excellent predictive ability in the context of high-cost and low-efficiency experimental methods.
APPLIED SOFT COMPUTING
(2021)
Article
Biochemical Research Methods
Qi Zhang, Yandan Zhang, Shan Li, Yu Han, Shuping Jin, Haiming Gu, Bin Yu
Summary: This article introduces a prediction method called Mps-mvRBRL for multi-label protein subcellular localization, achieving high prediction accuracy for different types of bacteria through feature fusion and weighted linear discriminant analysis.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Genetics & Heredity
Hui Min, Xiao-Hong Xin, Chu-Qiao Gao, Likun Wang, Pu-Feng Du
Summary: This study proposed a method named XGEM to predict essential miRNAs using the XGBoost framework with CART. XGEM showed promising prediction performance compared to other state-of-the-art methods, suggesting its potential in identifying essential miRNAs.
FRONTIERS IN GENETICS
(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
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
Biology
Cheng Chen, Han Shi, Zhiwen Jiang, Adil Salhi, Ruixin Chen, Xuefeng Cui, Bin Yu
Summary: The study introduces a novel method DNN-DTIs for predicting drug-target interactions, demonstrating superior accuracy compared to other predictors, especially suitable for drug repositioning research.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Engineering, Mechanical
Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias Kersemans
Summary: This paper introduces a novel ensemble classifier based on the Dempster-Shafer theory of evidence, aiming to improve fusion performance through several steps and validating its effectiveness by applying to multiple datasets and vibration-based datasets.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Junsheng Qiao, Tengbiao Li
Summary: This paper continues to study the r-quasi-grouping functions proposed by the author recently based on grouping functions. It shows the relationship among certain classes of r-quasi-grouping functions and provides the constructions and equivalent characterizations of these functions and their generalized forms. Finally, a r-quasi-grouping functions-based classifier ensemble algorithm (G-CEA) is proposed, and its performance is tested through numerical experiments, comparative analysis with three classical machine learning ensemble algorithms, and sensitivity analysis of parameter r.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias Kersemans
Summary: The article introduces a novel multi-classifier fusion approach using Dempster-Shafer theory to improve classifiers' performance. A preprocessing technique is designed to measure and mitigate conflicts in the presence of conflicting evidences. Experimental results show that the proposed method excels in classification accuracy and outperforms individual classifiers.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(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
Automation & Control Systems
Qi Zhang, Shan Li, Qingmei Zhang, Yandan Zhang, Yu Han, Ruixin Chen, Bin Yu
Summary: The paper introduces a new prediction model, MpsLDA-ProSVM, which accurately predicts the specific subcellular localization of multi-label proteins in cells. By utilizing various coding algorithms and a weighted multi-label linear discriminant analysis framework, the model demonstrates high accuracy in virus, plant, Gram-positive bacteria, and Gram-negative bacteria datasets.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Wafa Boukellouz, Abdelouahab Moussaoui
Summary: Recent research has shown a growing interest in using MRI as the sole modality for radiation therapy due to its superior soft-tissue visualization and non-ionizing properties. Machine learning algorithms, such as ensemble learning, have been successful in improving prediction accuracy for MRI-only RT, outperforming other methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Review
Pharmacology & Pharmacy
Zhenyu Wu, Patrick J. Lawrence, Anjun Ma, Jian Zhu, Dong Xu, Qin Ma
TRENDS IN PHARMACOLOGICAL SCIENCES
(2020)
Article
Cardiac & Cardiovascular Systems
Jeffrey S. Bennett, David M. Gordon, Uddalak Majumdar, Patrick J. Lawrence, Adrianna Matos-Nieves, Katherine Myers, Anna N. Kamp, Julie C. Leonard, Kim L. McBride, Peter White, Vidu Garg
Summary: This study used a machine learning approach to predict pathogenic LMNA variants and identified a novel LMNA variant associated with conduction system disease. The results suggest that machine learning methods can assist in identifying high-risk variants of uncertain significance.
Article
Genetics & Heredity
David M. Gordon, David Cunningham, Gloria Zender, Patrick J. Lawrence, Jacqueline S. Penaloza, Hui Lin, Sara M. Fitzgerald-Butt, Katherine Myers, Tiffany Duong, Donald J. Corsmeier, Jeffrey B. Gaither, Harkness C. Kuck, Saranga Wijeratne, Blythe Moreland, Benjamin J. Kelly, Vidu Garg, Peter White, Kim L. McBride
Summary: This study investigates the genetic causes of congenital heart disease by studying families with multiple individuals affected by heart defects. By identifying potential disease-causing genetic variants that are common among all affected individuals, the study was able to find plausible disease-causing variants in several genes and identify new genes that may contribute to the presence of a heart defect. The findings suggest that studying families may be more effective in finding causes of heart defects than studying individuals, and that changes in multiple genes may be required for a heart defect to occur.
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)