Article
Biology
Ping Meng, Guohua Wang, Hongzhe Guo, Tao Jiang
Summary: Cancer development and progression are influenced by cancer driver genes. Identifying driver genes is important for effective cancer treatments. In this study, we present an algorithm based on two-stage random walk with restart to discover driver genes, which outperformed existing methods. Our approach is efficient in various cancer types and can identify possible driver genes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemical Research Methods
Chenye Wang, Junhan Shi, Jiansheng Cai, Yusen Zhang, Xiaoqi Zheng, Naiqian Zhang
Summary: DriverRWH is a random walk algorithm that utilizes co-mutation information to prioritize cancer driver genes across various cancer types, showing a better balance of precision and sensitivity compared to other tools.
BMC BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Qianqian Ma, Shao-Wu Zhang, Song-Yao Zhang
Summary: Researchers propose a novel network-based approach (m(6)Acancer-Net) to identify m(6)A-mediated driver genes and their associated networks in specific cancers. By integrating multiple data sources, this method can reliably identify functionally significant m(6)A-mediated driver genes in specific cancers, facilitating a deeper understanding of the regulatory and therapeutic mechanisms of cancer driver genes at the epitranscriptome level.
Article
Biochemical Research Methods
Chuang Liu, Yao Dai, Keping Yu, Zi-Ke Zhang
Summary: This study proposed a network-based classification method for identifying cancer driver genes by merging multiple biological information. The method constructs a cancer specific genetic network from the human protein-protein interactome (PPI) and combines biological information such as mutation frequency and differential expression of genes for accurate prediction of cancer driver genes. The algorithm achieves high prediction accuracy across seven different cancer types, surpassing existing advanced methods.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xianlai Chen, Mingyue Xu, Ying An
Summary: Multilayer network combined with random walk algorithm is an effective method for pre-screening vital target proteins related to prescriptions, improving the comprehensive understanding of drug action mechanisms.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Michael Hartung, Elisa Anastasi, Zeinab M. Mamdouh, Cristian Nogales, Harald H. H. W. Schmidt, Jan Baumbach, Olga Zolotareva, Markus List
Summary: Cancer is a heterogeneous disease and targeted therapy based on evidence-based selection of drugs is crucial. However, many cancer driver genes cannot be directly targeted, necessitating an indirect approach using functionally related targets in the gene interaction network. CADDIE is a web application that integrates databases and algorithms to guide clinical researchers in identifying drug targets and candidates.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Mathematics
Massimiliano Turchetto, Michele Bellingeri, Roberto Alfieri, Ngoc-Kim-Khanh Nguyen, Quang Nguyen, Davide Cassi
Summary: Investigating the network response to node removal and the efficacy of the node removal strategies is fundamental to network science. In this study, we propose four new measures of node centrality based on random walk and compare them with existing strategies for synthesizing and real-world networks. The results indicate that the degree nodes attack is the best strategy overall, and the new node removal strategies based on random walk show the highest efficacy in relation to specific network topology.
Article
Computer Science, Artificial Intelligence
Nahla Mohamed Ahmed, Ling Chen, Bin Li, Wei Liu, Caiyan Dai
Summary: This paper presents a random walk-based method named EPD-RW to identify essential proteins by integrating network topology and biological information. Experimental results demonstrate that EPD-RW can achieve the best performance among all tested methods on yeast PPI datasets. The biological features greatly enhance the performance of essential protein detection.
Article
Medicine, Research & Experimental
Feng Li, Han Li, Junliang Shang, Jin-Xing Liu, Lingyun Dai, Xikui Liu, Yan Li
Summary: Cancer is a major cause of human mortality with a significant impact on survival and health. Many computational methods have been developed to identify cancer driver genes, but they mainly focus on coding genes, disregarding the role of non-coding genes. In this study, we propose a network-based method called NMDGCC that can identify both coding and non-coding cancer driver genes. The method involves constructing a gene interaction network using mRNA and miRNA expression data, and then using node control centrality to identify cancer drivers. Testing on breast cancer datasets shows that NMDGCC outperforms existing methods and identifies several non-coding miRNA cancer drivers, particularly those related to the tumorigenesis of BRCA. Furthermore, NMDGCC successfully detects cancer drivers specific to different breast cancer subtypes.
EXPERIMENTAL BIOLOGY AND MEDICINE
(2023)
Article
Agronomy
Liu Zhu, Hongyan Zhang, Dan Cao, Yalan Xu, Lanzhi Li, Zilan Ning, Lei Zhu
Summary: This study introduces a random walk with restart algorithm (RWR) to identify potential drought stress-related genes in rice. By integrating protein-protein interaction data and gene coexpression network, a set of 13 genes were identified, 5 of which have been reported to be involved in drought stress resistance mechanisms.
Review
Biochemistry & Molecular Biology
Rhys Gillman, Matt A. Field, Ulf Schmitz, Rozemary Karamatic, Lionel Hebbard
Summary: Cancer, a heterogeneous disease with a strong genetic component, can benefit from precision medicine approaches to identify molecular drivers. Single-tumour methods based on gene interaction networks have limitations and require further research for evaluation.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Medicine, General & Internal
Luxuan Qu, Zhiqiong Wang, Hao Zhang, Zhongyang Wang, Caigang Liu, Wei Qian, Junchang Xin
Summary: This study analyzed the protein-protein interaction network related to breast cancer, identified important genes, and validated their importance in clinical treatment.
Article
Physics, Multidisciplinary
Anthony Baptista, Aitor Gonzalez, Anais Baudot
Summary: The amount and variety of data have been increasing, requiring new methods to deal with the diversity and complexity of multilayer networks. The authors propose MultiXrank, a framework that uses random walks with restart to study multilayer networks, highlighting the important influence of bipartite networks.
COMMUNICATIONS PHYSICS
(2022)
Article
Oncology
Jinyuan Xu, Bo Pang, Yujia Lan, Renjie Dou, Shuai Wang, Shaobo Kang, Wanmei Zhang, Yuanyuan Liu, Yijing Zhang, Yanyan Ping
Summary: High heterogeneity in genome and phenotype of cancer populations makes it difficult to apply population-based common driver genes to diagnose and treat individual cancer patients. We proposed an integrative method to identify personalized driver gene sets for glioblastoma multiforme (GBM) patients by integrating gene expression and genetic alteration profiles. Our method identified driver gene sets for 99 GBM patients, and found that genomic alterations in one to seven driver genes could explain dysfunction of cancer hallmarks across GBM patients. Our method also identified MCM4 as a previously unknown oncogenic gene with rare genetic alterations, which was associated with poor prognosis in GBM. Functional experiments confirmed that MCM4 plays a role in GBM cell proliferation, invasion, migration, and clone formation. Our method could be valuable for developing targeted therapy and precision medicine.
MOLECULAR ONCOLOGY
(2023)
Article
Computer Science, Information Systems
Kamal Berahmand, Elahe Nasiri, Saman Forouzandeh, Yuefeng Li
Summary: This article proposes an improved method for local random walk by encouraging the movement towards nodes with stronger influence, resulting in higher prediction accuracy. A comparison with other similarity-based methods was conducted on 11 real-world networks, and the results demonstrated its superior performance in link prediction.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Biochemical Research Methods
Min Zeng, Yifan Wu, Chengqian Lu, Fuhao Zhang, Fang-Xiang Wu, Min Li
Summary: This study presents a deep learning framework, DeepLncLoc, for predicting the subcellular localization of lncRNAs. By introducing a novel subsequence embedding method, DeepLncLoc retains the order information of lncRNA sequences and utilizes a text convolutional neural network for high-level feature learning and prediction. Compared to traditional methods, DeepLncLoc shows improved performance.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Cheng Yan, Guihua Duan, Na Li, Lishen Zhang, Fang-Xiang Wu, Jianxin Wang
Summary: In this study, a new deep learning method called PDMDA is proposed to accurately predict deep-level miRNA-disease associations. By using graph neural networks (GNNs) and miRNA sequence features, PDMDA can extract valuable information from the feature representations of miRNAs and diseases, leading to efficient predictions.
Article
Biochemical Research Methods
Xingyi Li, Ju Xiang, Fang-Xiang Wu, Min Li
Summary: This study developed a multiplex network-based dual ranking framework for analyzing heterogeneous complex diseases. The results showed that the proposed method could identify biomarkers with small quantity, great prediction performance, and biological interpretability, and outperformed other competing methods in terms of diagnosis, prognosis, and classification.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Ali Akbar Jamali, Yuting Tan, Anthony Kusalik, Fang-Xiang Wu
Summary: Computational drug repositioning using tensor decomposition can accelerate drug discovery process. NTD-DR, a nonnegative tensor decomposition method, outperforms other methods in prediction performance and is validated in case studies.
Article
Engineering, Multidisciplinary
Xiangmao Meng, Wenkai Li, Ju Xiang, Hayat Dino Bedru, Wenkang Wang, Fang-Xiang Wu, Min Li
Summary: This study reexamines the essentiality of hub proteins in PPI networks by constructing temporal-spatial dynamic PPI networks and integrating gene expression data and subcellular localization information. The results show that integrating multiple data sources can improve the identification accuracy of essential proteins.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Biochemical Research Methods
Yulian Ding, Xiujuan Lei, Bo Liao, Fang-Xiang Wu
Summary: In this study, a factorization machine-based deep neural network with binary pairwise encoding (DFMbpe) is proposed to identify disease-related biomarkers. The DFMbpe model considers the interdependence of features and combines low-order and high-order feature interactions, leading to better performance compared to other biomarker identification models.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Engineering, Biomedical
Rayyan Azam Khan, Yigang Luo, Fang-Xiang Wu
Summary: In this study, a multilevel generative adversarial network (GAN) is proposed to enhance computed tomographic (CT) images for liver cancer diagnosis. The performance of the proposed method is investigated using three publicly available datasets, and it achieves good results in terms of performance metrics and computer-aided diagnosis. The effectiveness of the proposed multi-level GAN in producing enhanced biomedical images with preserved structural details and reduction in artifacts is demonstrated, and it shows consistently better performance among three datasets for computer-aided diagnosis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Information Systems
Minghan Fu, Meiyun Wang, Yaping Wu, Na Zhang, Yongfeng Yang, Haining Wang, Yun Zhou, Yue Shang, Fang-Xiang Wu, Hairong Zheng, Dong Liang, Zhanli Hu
Summary: A novel two-branch network architecture called SW-GCN is proposed to improve PET image quality. The network utilizes Swin Transformer units and graph convolution operation to handle different types of input information flow and enables better processing of long-range contextual information. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Chemistry, Analytical
Kawsar Ahmed, Francis M. Bui, Fang-Xiang Wu
Summary: To reduce the development time and effort of standard optical biosensors, machine learning approaches have been used to predict crucial parameters and evaluate the performance of the models based on performance indicators.
Article
Computer Science, Information Systems
Yuchen Zhang, Xiujuan Lei, Cai Dai, Yi Pan, Fang-Xiang Wu
Summary: More and more studies have shown that circRNAs can be used as disease markers due to their stability. Various computational methods, particularly those utilizing artificial intelligence, have been employed to predict circRNA-disease associations. However, these methods often use single, standard objective functions, leading to low prediction accuracy. This paper proposes a multiobjective evolutionary algorithm called ICDMOE to identify circRNA-disease associations, using matrix factorization and modularity of similarity networks to design four objective functions. Experimental results demonstrate that ICDMOE outperforms other prediction methods and can provide good candidates for biomedical experiments, as confirmed by existing studies, miRNA regulations, and expression profiles.
INFORMATION SCIENCES
(2023)
Article
Biochemical Research Methods
Xuhua Yan, Ruiqing Zheng, Fangxiang Wu, Min Li
Summary: CIAIRE is a novel contrastive learning-based batch correction framework that achieves a superior mix-heterogeneity trade-off. It proposes two complementary strategies, construction strategy and refinement strategy, to improve the appropriateness of positive pairs. CLAIRE outperforms existing methods in terms of mix-heterogeneity trade-off and achieves the best integration performance on six real datasets.
Article
Biochemical Research Methods
Yiming Li, Min Zeng, Fuhao Zhang, Fang-Xiang Wu, Min Li
Summary: In this study, DeepCellEss, a sequence-based interpretable deep learning framework, is proposed for cell line-specific essential protein predictions. By utilizing convolutional neural networks, bidirectional long short-term memory, and multi-head self-attention mechanism, DeepCellEss achieves effective prediction performance for different cell lines and outperforms existing methods and metrics.
Article
Computer Science, Information Systems
Ming Fang, Xiujuan Lei, Bo Liao, Fang-Xiang Wu
Summary: In this study, a deep learning model named DeepCELL was constructed to automatically classify cervical cytology images using multiple kernel feature representations. The experimental results showed that the proposed method achieved excellent performance on two datasets, indicating its promising performance in cervical cell image classification.
Article
Biochemical Research Methods
Liangliang Liu, Shaojie Tang, Fang-Xiang Wu, Yu-Ping Wang, Jianxin Wang
Summary: This paper proposes an ensemble hybrid features selection method for the classification of neuropsychiatric disorders, which improves the performance of classification methods. The importance of phenotypic features and image features in different classification tasks is analyzed.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Xiangmao Meng, Ju Xiang, Ruiqing Zheng, Fang-Xiang Wu, Min Li
Summary: Protein complexes play a crucial role in the biological functions of cells. This study proposes a method called DPCMNE to detect protein complexes using multi-level network embedding, which preserves both the local and global topological information of biological networks. Experimental results show that DPCMNE outperforms other existing methods in terms of F1 and F1+Acc, and the protein complexes detected by DPCMNE are biologically more significant.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
