Article
Physics, Multidisciplinary
Zhiyu Tian, Yang Liu, Le Luo
Summary: This study explores topological edge states in non-Hermitian systems using the characteristics of diffusion coefficients, revealing unique features during topological phase transitions. Additionally, a direct correlation between the Shannon entropy of quantum walks and diffusion coefficients is discovered. This research presents a new avenue for studying topological states in non-Hermitian quantum walk systems.
Article
Physics, Multidisciplinary
Iddo Eliazar, Shlomi Reuveni
Summary: This paper is the first study to address the impact of restart on the Shannon entropy of completion time. It analyzes the effects of sharp restart on completion time with different timers and establishes closed-form results. The study also uses an information-geometric approach to determine the existence of timers that decrease or increase completion time entropy.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2023)
Article
Physics, Multidisciplinary
Nur Izzati Ishak, S. Muniandy, Wu Yi Chong
Summary: This study investigates the behavior of a one-dimensional quantum walk in the presence of flip-bit noise channel, revealing a wide range of probability distributions under different noise intensities. Surprisingly, the maximum Shannon entropy of the walk is achieved at a lower degree of decoherence rather than maximum decoherence.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Genetics & Heredity
Jia Qu, Chun-Chun Wang, Shu-Bin Cai, Wen-Di Zhao, Xiao-Long Cheng, Zhong Ming
Summary: The study developed a computing model for predicting miRNA-disease associations based on heterogeneous networks, with good performance demonstrated. Case studies showed that the model also performed well in prioritizing candidate miRNAs.
FRONTIERS IN GENETICS
(2021)
Article
Oncology
Tao Duan, Zhufang Kuang, Lei Deng
Summary: miRNA is a potential therapeutic target due to its complex gene regulation mechanism, and its abnormal expression can cause drug resistance, affecting the therapeutic effect of diseases. Developing computational methods to predict miRNA-drug resistance associations is of practical value for designing effective drugs or combinations.
FRONTIERS IN ONCOLOGY
(2022)
Article
Physics, Multidisciplinary
Anwar Zaman, Rashid Ahmad, Safia Bibi, Sajid Khan
Summary: This article introduces the generalization of the conditional shift operator in Discrete-time Quantum Walk (DTQW), including cases where the shift-size is greater than the unit size, the shift sizes in positive and negative directions are not equal, and the shift-size is randomly chosen at each step. The study shows that all three variants spread faster than the standard DTQW, but the probability becomes random and still follows some specific patterns in the case of random shift-size.
INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS
(2022)
Article
Biochemical Research Methods
Yiran Huang, Zhihong Wu, Wei Lan, Cheng Zhong
Summary: This study proposes a computational method called m(7)GDP-RW to predict m(7)G-disease associations using the random walk algorithm. The method incorporates feature information of m(7)G site and disease with known associations to compute similarity measures, and constructs a heterogeneous network to find novel associations. The experimental results demonstrate the effectiveness of m(7)GDP-RW in predicting m(7)G-disease associations.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Cagatay Dursun, Anne E. Kwitek, Serdar Bozdag
Summary: The article introduced a network propagation algorithm PhenoGeneRanker for multi-layer gene and phenotype networks, which performed well in ranking hypertension disease-related genes and strains. By utilizing multiplex networks, the algorithm achieved satisfactory results in gene and phenotype prioritization for hypertension research.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Physics, Multidisciplinary
Ling Zhan, Tao Jia
Summary: This article highlights the importance of Heterogeneous Information Network (HIN) embedding and proposes a new embedding method called CoarSAS2hvec. The method incorporates self-avoiding short sequence sampling and an optimized loss function to improve the performance of HIN structure embedding. CoarSAS2hvec outperforms other methods in node classification and community detection tasks, and effectively captures richer information compared to existing methods.
Article
Genetics & Heredity
Jianwei Li, Zhiguang Li, Yinfei Wang, Hongxin Lin, Baoqin Wu
Summary: Long non-coding RNAs (lncRNAs) are important in gene regulation, and dysfunction in lncRNA regulation can lead to complex human diseases. Gene set enrichment analysis is a widely used bioinformatic technique to detect the biological pathways and functional categories of genes that encode lncRNA. However, accurately performing this analysis for lncRNAs remains a challenge.
FRONTIERS IN GENETICS
(2023)
Article
Computer Science, Hardware & Architecture
Arvind Mewada, Rupesh Kumar Dewang
Summary: Online reviews significantly impact consumers' purchasing decisions, however, many sellers exploit this system by employing spammers to create fake reviews. Existing models overlook the importance of considering both structural and behavioral features in detecting organized spammer groups. To address this, we propose a Fake Reviewers Groups Detection Model that comprehensively considers network structure and reviewer behavior.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Green & Sustainable Science & Technology
Benjamin Cakir, Isabelle Schluep, Philipp Aerni, Isa Cakir
Summary: This article introduces the concept of Economic Complexity Index (ECI) to assess a country's resilience and contribution to sustainable change, using a novel methodological approach. The results show that ECI ranking may not always reflect the true internal economic complexity of a country, and the new approach aligns with the organic evolutionary character of complex economies.
Article
Engineering, Multidisciplinary
Pankaj Prasad Dwivedi, Dilip Kumar Sharma
Summary: The article discusses the implementation of sustainable development goals in different regions, with a focus on Indian union territories. The study shows that Chandigarh is the most successful in achieving sustainable development goals, while Dadra Nagar Haveli and Daman & Diu rank the lowest.
RESULTS IN ENGINEERING
(2022)
Article
Mathematical & Computational Biology
Saranya Muniyappan, Arockia Xavier Annie Rayan, Geetha Thekkumpurath Varrieth
Summary: In this study, a DTiGNN framework is proposed for identifying unknown drug-target pairs. The framework calculates the similarity between drugs and targets from multiple perspectives and learns the features of drugs and targets separately using an information entropy-based random walk method. Then, a multi-view convolutional neural network integrates the learned features into a single drug and target similarity network to construct a heterogeneous biological network. A novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Finally, a convolutional neural network infers new DTIs after balancing the sample using oversampling techniques. The results show better performance in terms of AUC and AUPR, with scores of 0.98 and 0.99, respectively, and a total of 23,739 newly predicted DTI pairs.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Biochemical Research Methods
Yuansheng Liu, Chaowang Lan, Michael Blumenstein, Jinyan Li
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2020)
Article
Physics, Multidisciplinary
Yuanlin Ma, Zuguo Yu, Runbin Tang, Xianhua Xie, Guosheng Han, Vo V. Anh
Article
Engineering, Civil
Xiaocai Zhang, Zhixun Zhao, Yi Zheng, Jinyan Li
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Biochemical Research Methods
Yuansheng Liu, Limsoon Wong, Jinyan Li
Editorial Material
Biochemical Research Methods
Jie Zheng, Jinyan Li, Yun Zheng
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2020)
Article
Biotechnology & Applied Microbiology
Zhixun Zhao, Xiaocai Zhang, Fang Chen, Liang Fang, Jinyan Li
Article
Engineering, Chemical
Hongping Guo, Zuguo Yu
Summary: Obesity and type II diabetes are associated with nonalcoholic fatty liver disease (NAFLD), and a multitrait association analysis identified 792 significant variants involving 100 pleiotripic genes. Pathway enrichment analysis revealed that the shared risk genes are enriched in cancer, diabetes, insulin secretion, and other related pathways, providing insights into the molecular mechanisms underlying comorbid NAFLD and metabolic disorders.
Article
Mathematics, Interdisciplinary Applications
Shan Jiang, Zu-Guo Yu, Vo V. Anh, Yu Zhou
Summary: This study utilizes MF-TWXDFA to investigate the cross-correlation between pollutants and meteorological factors, revealing the existence of multifractal cross-correlation between all pairs of pollutants and meteorological factors in both urban and rural areas. It also highlights the more obvious multifractal degree of cross-correlation between certain pollutants and meteorological factors in urban areas, contrary to previous research findings.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2021)
Article
Physics, Applied
Jian-Hui Li, Jin-Long Liu, Zu-Guo Yu, Bao-Gen Li, Da-Wen Huang
Summary: This paper studies the synchronizability of three types of dynamical weighted fractal networks and finds that an increase in certain features leads to stronger synchronizability.
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2021)
Article
Biochemistry & Molecular Biology
Dong-Ling Yu, Zu-Guo Yu, Guo-Sheng Han, Jinyan Li, Vo Anh
Summary: Abnormal miRNA functions play a significant role in various diseases, with up to five different association types identified. Predicting multiple association types is crucial for understanding disease mechanisms, and current methods lack exploiting nonlinear characteristics in the miRNA-disease association network to improve performance.
Article
Mathematics
Dongling Yu, Zuguo Yu
Summary: In this study, a novel approach called HWVoteRank is proposed for identifying cancer drivers through a network-based voting mechanism. Compared to other methods, it shows higher efficiency in identifying miRNA cancer drivers.Through literature research, the drivers identified by this approach are found to be of biological significance.
Article
Biochemical Research Methods
Yuan-Lin Ma, Dong-Ling Yu, Ya-Fei Liu, Zu-Guo Yu
Summary: In this study, a cascade combination method called CCRMDA is developed to explore disease-related miRNAs based on network topology. It utilizes a hybrid recommendation algorithm and a structural perturbation method to predict new miRNA-disease associations in the miRNA-disease heterogeneous network. Experimental analysis shows that CCRMDA achieves the best performance compared to other state-of-the-art algorithms and case studies further validate its effectiveness.
CURRENT BIOINFORMATICS
(2023)
Review
Biochemical Research Methods
Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S. Yu, Xiangxiang Zeng
Summary: Recent advances in AI and deep learning models have proven their usefulness in biomedical applications, specifically in predicting drug-drug interactions (DDIs). Traditional clinical trials and experiments for DDIs prediction are time-consuming and expensive. The application of AI and deep learning in this field faces challenges such as data availability and encoding, as well as computational method design. This review summarizes various methods for DDIs prediction and provides a comprehensive guide for researchers and developers.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Jian-Hui Li, Zu-Guo Yu, Vo V. V. Anh, Jin-Long Liu, An-Qi Peng
Summary: In this paper, a new random rewiring method is proposed to transform fractal networks into small-world networks. The proposed method is proven to retain the degree and connectivity of the network, as well as the tree structure of tree graphs. It demonstrates the generality of small-world networks and can be applied to various types of complex networks. This rewiring method has a broader range of applications in network analysis.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2023)
Article
Physics, Fluids & Plasmas
Yuemin Ding, Jin-Long Liu, Xiaohui Li, Yu-Chu Tian, Zu-Guo Yu
Summary: A computationally efficient sandbox algorithm (CESA) is proposed in this paper for multifractal analysis (MFA) of large-scale networks, which reduces the time complexity from cubic to quadratic and the space complexity from quadratic to linear compared to the existing sandbox algorithm. Experimental results show that CESA is effective, efficient, and feasible for MFA of networks with about 11 million nodes, and increasing the size of the theoretical network does improve the accuracy of MFA results. Additionally, CESA is demonstrated to be applicable to real-world networks of large scale.
Article
Biology
Kunal Bhattacharya, Shikha Mahato, Satyendra Deka, Nongmaithem Randhoni Chanu, Amit Kumar Shrivastava, Pukar Khanal
Summary: Chemoresistance, a major challenge in cancer treatment, is associated with the cellular glutathione-related detoxification system. A study has identified GSTP1 enzyme as critical in the inactivation of anticancer drugs and suggests the need for GSTP1 inhibitors to combat chemoresistance. Through molecular docking and simulations, the study found that quercetin 7-O-beta-D-glucoside showed promise as a potential candidate for addressing chemoresistance in cancer patients.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Manwi Shankar, Majji Sai Sudha Rani, Priyanka Gopi, P. Arsha, Prateek Pandya
Summary: This study investigates the interaction between the food dye BBY and the serum protein BSA. The results show that BBY binds to a specific site on BSA through hydrophobic interactions, affecting the structural stability of the protein. These findings enhance our understanding of the molecular-level interactions between BBY and BSA.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Chi Zhang, Qian Gao, Ming Li, Tianfei Yu
Summary: In this study, we propose a graph neural network-based autoencoder model, AGraphSAGE, that effectively predicts protein-protein interactions across diverse biological species by integrating gene ontology.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kangjie Wu, Liqian Xu, Xinxiang Li, Youhua Zhang, Zhenyu Yue, Yujia Gao, Yiqiong Chen
Summary: Named Entity Recognition (NER) is a crucial task in natural language processing (NLP) and big data analysis, with wide application range. This paper proposes an improved neural network method for NER of rice genes and phenotypes, which can learn semantic information in the context without feature engineering. Experimental results show that the proposed model outperforms other models.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Suman Hait, Sudip Kundu
Summary: Interactions between amino acids in proteins are crucial for stability and structural integrity. Thermophiles have more and more stable interactions to survive in extreme environments. Different types of interactions are enriched in different structural regions.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kountay Dwivedi, Ankit Rajpal, Sheetal Rajpal, Virendra Kumar, Manoj Agarwal, Naveen Kumar
Summary: This study aims to identify biomarkers for non-small cell lung cancer (NSCLC) using copy number variation (CNV) data. A novel deep learning architecture, XL1R-Net, is proposed to improve the classification accuracy for NSCLC subtyping. Twenty NSCLC-relevant biomarkers are uncovered using explainable AI (XAI)-based feature identification. The results show that the identified biomarkers have high classification performance and clinical relevance. Additionally, twelve of the biomarkers are potentially druggable and eighteen of them have a high probability of predicting NSCLC patients' survival likelihood according to the Drug-Gene Interaction Database and the K-M Plotter tool, respectively. This research suggests that investigating these seven novel biomarkers can contribute to NSCLC therapy, and the integration of multiomics data and other sources will help better understand NSCLC heterogeneity.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Pengli Lu, Wenqi Zhang, Jinkai Wu
Summary: Researchers have developed a computational method, AMPCDA, to predict circRNA-disease associations using predefined metapaths, achieving high predictive accuracy. This method effectively combines node embeddings with higher-order neighborhood representations and provides valuable guidance for revealing new disease mechanisms in biological research.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)