Article
Biology
Maqsood Hayat, Muhammad Tahir, Fawaz Khaled Alarfaj, Ryan Alturki, Foziah Gazzawe
Summary: Plasmodium falciparum causes malaria and accurate prediction of the parasite is crucial for effective treatment. Automated parasite detection technologies are in high demand due to the complexities and potential errors of manual diagnosis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Environmental Sciences
Sushant K. Singh, Robert W. Taylor, Biswajeet Pradhan, Ataollah Shirzadi, Binh Thai Pham
Summary: This study evaluates the performance of different machine learning models in predicting preferences for sustainable arsenic mitigation. The results show that a Gaussian distribution-based Naive Bayes classifier performs the best, while linear classifiers underperform. Nonlinear or ensemble classifiers can better understand the complex relationships in socio-environmental data and provide accurate and robust prediction models. In cases of limited data, Gaussian Naive Bayes is the best option.
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
(2022)
Article
Biochemical Research Methods
Dhananjay Kimothi, Pravesh Biyani, James M. Hogan, Melissa J. Davis
Summary: Protein-protein interactions play a crucial role in cell function. This paper explores the use of sequence embeddings to predict these interactions. The authors propose a method that constructs a feature vector by combining the embeddings of the constituent sequences. The results show that low dimensional sequence embeddings outperform alternative representations based on physico-chemical properties.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Mathematical & Computational Biology
Ji-Yong An, Fan-Rong Meng, Zi-Ji Yan
Summary: The proposed WELM-SURF model is competent for predicting drug-target interactions with high accuracy and robustness. Experimental results demonstrate that WELM-SURF outperforms existing methods in the domain of DTIs prediction.
Article
Biochemical Research Methods
Xin Luo, Liwei Wang, Pengwei Hu, Lun Hu
Summary: This paper proposes a novel protein-protein interaction (PPI) prediction algorithm (PASNVGA) that combines sequence and network information to improve prediction accuracy. The algorithm utilizes principal component analysis to extract protein features and designs a scoring function to measure higher-order connectivity. By training a variational graph autoencoder model to learn integrated protein embeddings, the prediction task is completed using a feedforward neural network.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
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
Xinke Zhan, Mang Xiao, Zhuhong You, Chenggang Yan, Jianxin Guo, Liping Wang, Yaoqi Sun, Bingwan Shang
Summary: This paper proposes a computational method for predicting protein-protein interactions from protein sequences. The method utilizes PSSM, LPP, and RF for feature extraction and classification. Experimental results demonstrate that the method is stable, accurate, and promising as a useful tool for proteomics research.
Article
Biochemistry & Molecular Biology
Zhijie Xiang, Weijia Gong, Zehui Li, Xue Yang, Jihua Wang, Hong Wang
Summary: The study proposed a method of gated graph attention for signed networks (SN-GGAT) to predict protein-protein interactions. By applying the graph attention network (GAT) to signed networks and combining the balance theory and gating mechanism, the method achieved competitiveness on the Saccharomyces cerevisiae core dataset and the Human dataset.
Article
Biochemical Research Methods
Akanksha Arora, Sumeet Patiyal, Neelam Sharma, Naorem Leimarembi Devi, Dashleen Kaur, Gajendra P. S. Raghava
Summary: Non-invasive diagnostics and therapies are important for minimizing patient discomfort. Exosomal proteins are identified as potential biomarkers. This study presents a model for predicting exosomal proteins based on machine learning and sequence motifs. The hybrid model outperforms existing methods and a web server and standalone software have been developed for researchers to predict and discover exosomal proteins.
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
Biochemical Research Methods
Pankaj Singh Dholaniya, Samreen Rizvi
Summary: Understanding protein-protein interactions is crucial for investigating the biological role of proteins. This study evaluates the efficacy of various sequence-based descriptors in predicting PPIs, with the results showing that the conjoint-triad descriptors performed the best.
CURRENT BIOINFORMATICS
(2021)
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
Genetics & Heredity
Li Shen, Jian Zhang, Fang Wang, Kai Liu
Summary: Essential proteins are crucial for cell survival and development. Predicting essential proteins through protein-protein interaction networks is more efficient than traditional methods, but existing algorithms have limitations. This study proposes a novel algorithm named LDS, which combines local fuzzy fractal dimension and protein subcellular location information to improve prediction accuracy.
Article
Biochemistry & Molecular Biology
Cheng Wang, Jun Zhang, Peng Chen, Bing Wang
Summary: The Ensemble-MFP method shows good prediction performance in new drug prediction, with AUC exceeding 94.0%. By weighting existing feature pairs, this method can effectively make general predictions.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biochemical Research Methods
Xiaojuan Wang, Wen Yang, Yue Yang, Yizhou He, Jun Zhang, Lusheng Wang, Lun Hu
Summary: This paper proposes an efficient network-based prediction algorithm called PPISB, which uses a mixed membership stochastic blockmodel to capture the latent community structures of proteins in a PPI network. PPISB optimizes the membership distributions of proteins and computes the similarity between proteins to determine their interaction. Experimental results show that PPISB performs well in predicting PPIs based on various evaluation metrics.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biotechnology & Applied Microbiology
Zhengwei Li, Tangbo Zhong, Deshuang Huang, Zhu-Hong You, Ru Nie
Summary: In this study, a novel deep learning model called HGANMDA was proposed to predict miRNA-disease associations. The model constructed a heterogeneous graph and utilized hierarchical graph attention network to achieve accurate prediction of miRNA-disease associations.
Article
Biochemical Research Methods
Zhi-Hua Du, Yang-Han Wu, Yu-An Huang, Jie Chen, Gui-Qing Pan, Lun Hu, Zhu-Hong You, Jian-Qiang Li
Summary: This study introduces a graph attention-based autoencoder model to predict TF-target gene interactions, which shows excellent prediction performance on a real dataset.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Han-Yuan Zhang, Lei Wang, Zhu-Hong You, Lun Hu, Bo-Wei Zhao, Zheng-Wei Li, Yang-Ming Li
Summary: Researchers have discovered a novel topology of RNA transcript called circular RNA (circRNA) that competes with messenger RNA (mRNA) and long noncoding RNA in gene regulation. This finding suggests that circRNA could be associated with complex diseases, thus identifying the relationship between them would contribute to medical research. However, in vitro experiments to determine the circRNA-disease association are time-consuming and lack direction. To address this, a computational method called iGRLCDA was proposed, which utilizes graph convolution network (GCN) and graph factorization (GF) to predict circRNA-disease associations. The performance of iGRLCDA was compared to other prediction models using five-fold cross-validation, showing strong competitiveness and high accuracy.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Xiaorui Su, Lun Hu, Zhuhong You, Pengwei Hu, Bowei Zhao
Summary: This paper proposes a KG-based drug-drug interaction (DDI) prediction framework called DDKG, which utilizes KG information and attention mechanism. Experimental results show that DDKG outperforms existing algorithms on different evaluation metrics.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biotechnology & Applied Microbiology
Zhong-Hao Ren, Chang-Qing Yu, Li-Ping Li, Zhu-Hong You, Yong-Jian Guan, Xin-Fei Wang, Jie Pan
Summary: Co-administration of drugs is an effective strategy for treating complex diseases, but predicting drug-drug interactions (DDIs) accurately is challenging. In this paper, a novel method called BioDKG-DDI is proposed to predict potential DDIs by integrating multiple features and biochemical information using an attention mechanism and deep neural network. Experimental results demonstrate that this method is robust and simple, and can serve as a beneficial supplement to the experimental process.
BRIEFINGS IN FUNCTIONAL GENOMICS
(2022)
Article
Genetics & Heredity
Zhong-Hao Ren, Chang-Qing Yu, Li-Ping Li, Zhu-Hong You, Yong-Jian Guan, Yue-Chao Li, Jie Pan
Summary: Non-coding RNAs (ncRNAs) play important roles in biological processes through interactions with RNA binding proteins (RBPs). Computational methods have been developed to predict ncRNA-protein interactions, but some of them have limited applicability. In this study, a computational method called SAWRPI is proposed to predict ncRNA-protein interactions using sequence information. The method achieved high performance in experiments, showing its potential as a reliable tool for predicting ncRNA-protein interactions.
FRONTIERS IN GENETICS
(2022)
Article
Biology
Zhong-Hao Ren, Chang-Qing Yu, Li-Ping Li, Zhu-Hong You, Jie Pan, Yong-Jian Guan, Lu-Xiang Guo
Summary: Combining drugs to fight against diseases has a long history, but potential drug interactions can lead to unknown toxicity. Our study introduces a computational framework and an online tool for researchers to identify potential interactions in the fields of biomedicine and pharmacology. This approach provides new insights for rapidly identifying drug-drug interactions.
Article
Biochemistry & Molecular Biology
Ying Wang, Lin-Lin Wang, Leon Wong, Yang Li, Lei Wang, Zhu-Hong You
Summary: Protein is the fundamental organic substance in cells that plays a crucial role in biological activities. Self-interacting protein (SIP) is an important protein interaction. This study presents a SIP prediction method, SIPGCN, using a deep learning graph convolutional network (GCN). The results demonstrate excellent performance of SIPGCN.
Review
Computer Science, Information Systems
Zhanheng Chen, Zhuhong You, Qinhu Zhang, Zhenhao Guo, Siguo Wang, Yanbin Wang
Summary: This review provides a comprehensive overview of recent literature on computational prediction of self-interacting proteins (SIPs), serving as a valuable reference for future work. The review first describes the data required for predicting drug-target interactions (DTIs), followed by the presentation of interesting feature extraction methods and computational models. An empirical comparison is then conducted to demonstrate the prediction performance of various classifiers under different feature extraction and encoding schemes. Overall, potential methods for further enhancing SIPs prediction performance and related research directions are summarized and highlighted.
FRONTIERS OF COMPUTER SCIENCE
(2023)
Article
Biology
Chang-Qing Yu, Xin-Fei Wang, Li-Ping Li, Zhu-Hong You, Wen-Zhun Huang, Yue-Chao Li, Zhong-Hao Ren, Yong-Jian Guan
Summary: With the advancement of circRNA-miRNA-mediated models, circRNAs have been identified as key players in the diagnosis and treatment of complex diseases such as cancer. Utilizing computer technology for large-scale predictions can guide biological experiments and reduce costs. The computational model SGCNCMI introduced in this study achieved satisfactory results, highlighting its significance in circRNA research.
Article
Automation & Control Systems
Lei Wang, Zhu-Hong You, De-Shuang Huang, Jian-Qiang Li
Summary: This study presents a new computational model, MGRCDA, which utilizes metagraph recommendation theory to predict potential circRNA-disease associations. By integrating heterogeneous biological networks and utilizing an iterative search algorithm, MGRCDA achieved high prediction accuracy and reliability. The experimental results demonstrate its feasibility and efficiency in reducing the scope of wet-lab experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Biology
Yong-Jian Guan, Chang-Qing Yu, Yan Qiao, Li-Ping Li, Zhu-Hong You, Zhong-Hao Ren, Yue-Chao Li, Jie Pan
Summary: This study presents a computational method called MFIDMA for predicting drug-miRNA associations. The proposed model demonstrates excellent performance in experiments and can be used for the development and research of miRNA-targeted drugs, providing new perspectives on miRNA therapeutics research and drug discovery.
Article
Biochemical Research Methods
Jie Pan, Zhuhong You, Wencai You, Tian Zhao, Chenlu Feng, Xuexia Zhang, Fengzhi Ren, Sanxing Ma, Fan Wu, Shiwei Wang, Yanmei Sun
Summary: This study developed a model called PTBGRP based on microbial heterogeneous interaction network to predict new phages for bacterial hosts. By integrating different biological attributes and topological features, a deep neural network classifier was used to predict unknown PBI pairs. Experimental results demonstrated that PTBGRP achieved the best performance on pathogen and PBI datasets.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Zhen-Hao Guo, Zhan-Heng Chen, Zhu-Hong You, Yan-Bin Wang, Hai-Cheng Yi, Mei-Neng Wang
Summary: The proposed model LDACE, which combines Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN), achieves remarkable performance in predicting potential lncRNA-disease associations. It constructs representation vectors by integrating multiple types of biological information and mines both local and global features using CNN. The model shows robustness and efficiency even in real environments, as demonstrated by case studies on lung cancer and endometrial cancer.
BMC BIOINFORMATICS
(2022)
Article
Biotechnology & Applied Microbiology
Yu-An Huang, Zhi-An Huang, Jian-Qiang Li, Zhu-Hong You, Lei Wang, Hai-Cheng Yi, Chang-Qing Yu
Summary: Recent evidences have shown the importance of host-microbiota interactions in the human body, and understanding these interactions can provide valuable insights into the pathological mechanisms of diseases. However, identifying disorder-specific microbes through wet-lab experiments is time-consuming and costly. This study aims to develop a computational prediction model to predict microbe-disease associations on a large scale. The proposed model shows reliable performance and has the potential to facilitate the identification of microbial biomarkers.
