Article
Biochemical Research Methods
Saikat Biswas, Pabitra Mitra, Krothapalli Sreenivasa Rao
Summary: The presence of co-morbid diseases increases the mortality risk of patients, highlighting the importance of predicting co-morbid disease pairs for early intervention. This study introduces a complex-valued embedding approach based on biological knowledge graphs for predicting associations between prevalent diseases.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Qichang Zhao, Mengyun Yang, Zhongjian Cheng, Yaohang Li, Jianxin Wang
Summary: This study investigates and discusses the latest applications of deep learning techniques in CPR prediction, including datasets and feature engineering, deep learning methods, and prediction performance. A comprehensive comparison is made to demonstrate the prediction performance of different methods on classical datasets. The current state of the field, existing challenges, and proposed future directions are also discussed.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Giorgio Grani, Lorenzo Madeddu, Paola Velardi
Summary: This study aims to analyze the relationship between the categorical proximity of diseases in human-curated ontologies and the structural proximity of the related disease modules in the interactome. By proposing a method to induce a hierarchical structure from proximity relations between disease modules and comparing it with a human-curated disease taxonomy, the study demonstrates the systematic analysis of commonalities and differences in disease similarities, refining and extending human disease classification systems.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Biochemical Research Methods
Mohan Timilsina, Declan Patrick Mc Kernan, Haixuan Yang, Mathieu D'Aquin
Summary: Semi-Supervised Learning utilizes unlabeled data along with a small amount of labeled data for training in machine learning. In this study, computational methods were used to estimate the functional role of drugs from unlabeled data, demonstrating the effectiveness of integrating genetic embedding graphs with protein functional association networks in predicting drug labels.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Mathematical & Computational Biology
David N. Nicholson, Daniel S. Himmelstein, Casey S. Greene
Summary: This study aimed to accelerate the creation process of label functions and explore the reuse of label functions across different edge types. The results indicate that label functions describing similar edge types can improve performance when transferred.
Review
Biochemistry & Molecular Biology
Kok Zhi Lee, Juya Jeon, Bojing Jiang, Shri Venkatesh Subramani, Jingyao Li, Fuzhong Zhang
Summary: Protein hydrogels, with their biocompatibility, biodegradability, and versatility for modifications, have become attractive materials for various medical applications. Recent advancements in protein engineering, synthetic biology, and material science have allowed for precise control over protein sequences, hydrogel structures, and mechanical properties, expanding the range of biomedical applications for protein hydrogels. This review focuses on recent progress in protein hydrogels, particularly those produced by microorganisms, discussing different formation strategies, associated properties, and biomedical applications categorized by protein sequence origins. The current challenges and future opportunities in engineering protein-based hydrogels are also discussed, aiming to inspire material innovation and the development of advanced protein hydrogels with desired properties for diverse biomedical applications.
Review
Biochemistry & Molecular Biology
Olga K. Parfenova, Vladimir G. Kukes, Dmitry V. Grishin
Summary: Follistatin-like proteins, with both autocrine and paracrine activity, play important roles in various signaling pathways and biological processes, affecting cellular signaling directly or indirectly.
Review
Computer Science, Information Systems
Xiangmao Meng, Wenkai Li, Xiaoqing Peng, Yaohang Li, Min Li
Summary: This paper provides a comprehensive survey on the characteristics of protein interaction networks (PINs), including centrality, modularity, and dynamics, and discusses their applications in complex diseases as well as future research directions.
FRONTIERS OF COMPUTER SCIENCE
(2021)
Article
Biochemistry & Molecular Biology
Yinjie Guo, Qiuxin Li, Daili Ji, Lijin Tian, Joerg Meurer, Wei Chi
Summary: This study developed a ubiquitin-based module capable of modifying multiprotein complexes in plant chloroplasts. The researchers successfully modified the Photosystem II complex and discovered that the interaction between its components affects leaf development.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Computer Science, Information Systems
Ana Leon, Oscar Pastor
Summary: Understanding the human genome is a major research challenge that requires effective data management policies. Analyzing protein data can promote a shared understanding of the domain and facilitate the management of relevant genome data.
Article
Biochemistry & Molecular Biology
Jianfeng Sun, Arulsamy Kulandaisamy, Jacklyn Liu, Kai Hu, M. Michael Gromiha, Yuan Zhang
Summary: Membrane proteins play crucial roles in various biological processes, but their atomic-level structures are difficult to obtain. Computational tools utilizing intelligent algorithms and deep learning techniques have greatly advanced the prediction of membrane proteins. This review provides an overview of current computational strategies in membrane protein classification, topology identification, interaction site detection, and pathogenic effect prediction, as well as the prediction process itself.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Biochemical Research Methods
Lei Deng, Wenkai Li, Jingpu Zhang
Summary: Accumulating evidence suggests that dysfunctions of long non-coding RNAs (lncRNAs) play important roles in complex human diseases, but their relationships with diseases are largely unknown. To address this issue, a new computational method, LDAH2V, was developed to predict potential lncRNA-disease associations efficiently. By calculating meta-paths and feature vectors in a heterogeneous information network, LDAH2V outperforms existing methods and achieves satisfactory results in validation tests.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Computer Science, Information Systems
Thitipong Kawichai, Apichat Suratanee, Kitiporn Plaimas
Summary: The proposed method, MGP-DDA, utilizes gene ontology profiles and ensemble learning techniques to predict drug-disease associations. By incorporating meta-path based GO profiles and training classifiers, MGP-DDA outperforms existing methods with 88.6% precision. This approach demonstrates practicality in drug repositioning by identifying a significant number of new drug-disease associations with supporting evidence in ClinicalTrials.gov.
Article
Computer Science, Information Systems
Wenkang Wang, Xiangmao Meng, Ju Xiang, Yunyan Shuai, Hayat Dino Bedru, Min Li
Summary: Protein complexes are essential in living cells and detecting them is crucial for understanding protein functions and treating complex diseases. This study proposes a novel method, CACO, to detect human protein complexes by integrating functional information from other species via protein ortholog relations. The method outperforms other state-of-the-art methods in terms of F-measure and Composite Score, demonstrating the effectiveness of integrating ortholog information and the proposed core-attachment algorithm in detecting protein complexes.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Biochemical Research Methods
Qingfeng Chen, Dehuan Lai, Wei Lan, Ximin Wu, Baoshan Chen, Jin Liu, Yi-Ping Phoebe Chen, Jianxin Wang
Summary: This article introduces a novel framework ILDMSF that combines lncRNA similarities and disease similarities for predicting potential lncRNA-disease relationships, utilizing support vector machine for identification and conducting leave-one-out cross validation to compare with other methods, showing prospective results in exploring correlations between lncRNA and disease.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Chengqian Lu, Lishen Zhang, Min Zeng, Wei Lan, Guihua Duan, Jianxin Wang
Summary: Emerging evidence suggests that circRNAs, with their covalently closed loop structures, can serve as promising biomarkers for diagnosis in pathogenic processes. Computational approaches provide a cost-effective way to identify circRNA-disease associations by aggregating multi-source pathogenesis data and inferring potential associations at the system level. The proposed CDHGNN model, based on edge-weighted graph attention and heterogeneous graph neural networks, outperforms state-of-the-art algorithms in predicting circRNA-disease associations and can identify specific molecular associations and investigate biomolecular regulatory relationships in pathogenesis.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Lishen Zhang, Chengqian Lu, Min Zeng, Yaohang Li, Jianxin Wang
Summary: In this study, we propose a method called CRMSS for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features. The CRMSS achieves superior performance over state-of-the-art methods in predicting circRNA-RBP binding.
BRIEFINGS IN BIOINFORMATICS
(2023)
Review
Biochemical Research Methods
Wei Lan, Yi Dong, Hongyu Zhang, Chunling Li, Qingfeng Chen, Jin Liu, Jianxin Wang, Yi-Ping Phoebe Chen
Summary: Accumulating evidence shows the importance of circular RNA (circRNA) in human diseases. Computational methods have been proposed to identify circRNA-disease associations, but there is a lack of comprehensive comparisons and summaries of these methods. This paper categorizes existing methods into three groups and introduces baseline methods for each category. It compares 14 representative methods using 5 different datasets and evaluates their effectiveness in identifying circRNA-disease associations in common cancers. The study also discusses the observations about method robustness and future directions and challenges.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Xiaoqing Peng, Wenjin Zhang, Wanxin Cui, Binrong Ding, Qingtong Lyu, Jianxin Wang
Summary: Alzheimer's disease (AD) is a common neurodegenerative disease, and DNA methylation is closely related to its pathological mechanism. A database named ADmeth has been designed to collect AD-related differential methylation data, containing 16,709 items identified from various brain regions and cell types in the blood, including 209 genes, 2,229 regions, and 14,271 CpG sites. The ADmeth database provides user-friendly functions for searching, submitting, and downloading data, aiming to facilitate research on the pathological mechanism of AD and non-invasive diagnosis using cell-free DNA.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Jiawei Huang, Wenlu Zhang, Yijun Li, Lin Li, Zhaoyi Li, Jin Ye, Jianxin Wang
Summary: Identifying heavy flows is crucial for network management, but it is challenging to detect heavy flow quickly and accurately in highly dynamic traffic and rapidly growing network capacity. Existing schemes trade-off efficiency, accuracy, and speed, requiring large memory for acceptable performance. To address this, ChainSketch is proposed, with advantages in memory efficiency, accuracy, and fast detection. ChainSketch utilizes selective replacement, hash chain, and compact structure, significantly improving F1-score compared to existing solutions, especially in small memory conditions.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Hardware & Architecture
Jingling Liu, Jiawei Huang, Weihe Li, Jianxin Wang, Tian He
Summary: Datacenter networks often face path asymmetry, leading to problems like packet reordering and under-utilization of multiple paths. In this paper, we propose a load balancing mechanism called AG that adaptively adjusts switching granularity based on the degree of topology asymmetry. We also design a switch-based scheme to measure the difference of one-way delay, allowing accurate detection of topology asymmetry. Experimental results show that AG outperforms existing load balancing schemes in terms of flow completion time.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Computer Science, Artificial Intelligence
Bolei Chen, Ping Zhong, Yongzheng Cui, Siyi Lu, Yixiong Liang, Yu Sheng
Summary: This paper proposes a novel Deep Reinforcement Learning (DRL) based autonomous exploration strategy, which efficiently reduces the unknown area of the workspace and provides accurate 2D map construction. The strategy utilizes the Generalized Voronoi Diagram (GVD) and Generalized Voronoi Networks (GVN) to design an autonomous exploration policy with spatial awareness and episodic memory. Invalid Action Masking (IAM) and a well-designed reward function are employed to cope with the expansion of the exploration range and guide the learning of policies. Extensive tests and experiments show the superiority of the strategy in terms of map quality and exploration speed.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Biochemical Research Methods
Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang
Summary: In this research, a novel method called DFHiC is proposed to generate high-resolution Hi-C matrix from low-resolution Hi-C matrix using the dilated convolutional neural network framework. DFHiC can reliably and accurately improve the resolution of Hi-C matrix, and the super-resolution Hi-C data enhanced by DFHiC is more similar to real high-resolution Hi-C data in terms of both chromatin significant interactions and identifying topologically associating domains.
Article
Biochemical Research Methods
Neng Huang, Minghua Xu, Fan Nie, Peng Ni, Chuan-Le Xiao, Feng Luo, Jianxin Wang
Summary: We developed a deep learning-based method called NanoSNP for identifying SNP sites in low-coverage Nanopore sequencing data. NanoSNP uses a multi-step, multi-scale, and haplotype-aware pipeline to detect SNP sites and predict genotypes. Comparison with other methods showed that NanoSNP outperformed Clair, Pepper-DeepVariant, and NanoCaller in identifying SNPs, especially in difficult-to-map regions and the major histocompatibility complex regions of the human genome. NanoSNP performed comparably to Clair3 when coverage exceeded 16x.
Article
Computer Science, Information Systems
Weihe Li, Jiawei Huang, Wenjun Lyu, Baoshen Guo, Wanchun Jiang, Jianxin Wang
Summary: Current ABR algorithms do not pay enough attention to audio bitrate selection, assuming it has minimal impact on video selection. However, with the advancement of audio technologies, audio bitrate can significantly affect video selection and viewing experience. To address this issue, we propose a deep reinforcement learning-based ABR algorithm that considers both audio and video quality, achieving significant improvements in overall viewing quality.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Information Systems
Shigeng Zhang, Zijing Ma, Kaixuan Lu, Xuan Liu, Jia Liu, Song Guo, Albert Y. Zomaya, Jian Zhang, Jianxin Wang
Summary: This paper presents HearMe, an accurate and real-time lip-reading system built on commercial RFID devices. HearMe can help people with speech disorders communicate and interact with the world effectively. By utilizing effective data collection, signal pattern extraction, and feature extraction techniques, HearMe achieves high accuracy in mouth motion recognition and word-level recognition. The use of transfer learning enhances the model's robustness in different environments.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Shigeng Zhang, Zijing Ma, Chengwei Yang, Xiaoyan Kui, Xuan Liu, Weiping Wang, Jianxin Wang, Song Guo
Summary: This article introduces a real-time and accurate gesture recognition system called ReActor based on RFID. ReActor combines time-domain statistical features and frequency-domain features to represent the signal profile corresponding to different gestures accurately, and uses signal preprocessing and classifier training to maintain high accuracy in different environments.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Wanchun Jiang, Haoyang Li, Yulong Yan, Fa Ji, Jiawei Huang, Jianxin Wang, Tong Zhang
Summary: This paper investigates the scheduling problem of key-value access operations in distributed key-value stores and proposes a distributed adaptive scheduler (DAS). Theoretical analysis and extensive simulations show that DAS can adapt to varying traffic and server performance, achieving consistent low latency and shorter request completion time.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jiawei Huang, Jingling Liu, Ning Jiang, Sen Liu, Jinbin Hu, Jianxin Wang
Summary: This study proposes a datacenter TCP based on Differential Explicit Congestion Notification (DECN) to achieve fast convergence without any additional feedback overhead. By feeding the rate difference between the target and current rate back to the source using multiple consecutive packets, DECN and its enhanced version DECN* achieve comparable fast convergence as existing explicit feedback-based TCPs without incurring any extra traffic or feedback overhead. Experimental results show that they reduce the flow completion time by up to 34% in typical data center applications.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Engineering, Civil
Aikun Xu, Ping Zhong, Yilin Kang, Jiongqiang Duan, Anning Wang, Mingming Lu, Chuan Shi
Summary: This paper presents a multi-modal transportation recommendation algorithm based on a carefully constructed Heterogeneous graph Attention Network (THAN). The algorithm utilizes a novel graph embedding method, constructs a heterogeneous graph from large-scale data, and employs a hierarchical attention mechanism and a fusion neural layer for node embedding and transport mode prediction.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
