研究领域
主讲课程
科研项目
近年代表作
2025
1. Shen, Cong; Liu, Xiang; Luo, Jiawei*; Xia, Kelin*. Torsion Graph Neural Networks. IEEE Transactions ON Pattern Analysis and Machine Intelligence, 2025,47(4):2946-2956.
2. Shi, Wanwan; Long, Yahui; Luo, Jiawei*; Liu, Ying*; Xiong, Zehao; Wang, Bo; Xu, Zhongyuan. scGANCL: Bidirectional Generative Adversarial Network for Imputing scRNA-Seq Data With Contrastive Learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2025,22(2):661-671.
3. Zou, Zhiyi; Liu, Ying; Bai, Yuting; Luo, Jiawei*; Zhang, Zhaolei*. scTrans: Sparse attention powers fast and accurate cell type annotation in single-cell RNA-seq data. PLOS Computational Biology, 2025,21(4):e1012904.
4. Bo Wang, Yahui Long, Yuting Bai, Jiawei Luo*, Chee Keong Kwoh*. STCGAN: a novel cycle-consistent generative adversarial network for spatial transcriptomics cellular deconvolution. Briefings in Bioinformatics, 2025,26(1):bbae670.
5. Liu, Pei; Liang, Xiao; Li, Yue*; Luo, Jiawei*. ConvNTC: convolutional neural tensor completion for detecting “A–A–B” type biological triplets. Briefings in Bioinformatics, 2025,26(4), bbaf372.
6. Wanwan Shi, Bo Wang, Yahui Long, Ying Liu, Qiu Xiao, Yuting Bai, Xiaoyi Peng, Xiangtao Chen*, Jiawei Luo*. High-Frequency-Aware Graph Integration for Subcellular Spatial Transcriptomics. IEEE International Conference on Bioinformatics and Biomedicine,2025,550-555.
7. Xiao Liang, Pei Liu, Cong Shen, Wei Liu, Juping Li, Jiawei Luo*. SpaMCI-DL: A hybrid deep learning framework for integrated identification of domains and spatially variable genes in spatial transcriptomics. IEEE International Conference on Bioinformatics and Biomedicine,2025,1755-1760.
8. Hanwen Lv, Yuting Bai, Jiawei Luo*. Fine-Grained Cross-Attention Between Drug Structures and Genes for Perturbation Prediction.IEEE International Conference on Bioinformatics and Biomedicine,2025,1783-1786.
9. Juping Li, Xiao Liang, Meng Wang, Jie Cai, Qiu Xiao*, Jiawei Luo*. SpaMIX: a multi-modal fusion framework for spatial domains identification and spatially variable genes detection in spatial transcriptomics. The 23rd Asia Pacific Bioinformatics Conference, 2025,271-289.
10. Siyu Li, Zhiyi Zou, Meng Wang, Hao Wu, Qiu Xiao*, Jiawei Luo*.SpaAdapt: A Domain Adaptation Framework with Feature Disentanglement for Cell Type Annotation in Single-Cell Spatial Transcriptomics. The 23rd Asia Pacific Bioinformatics Conference, 2025,290-306.
2024
1. Liang, Xiao;Liu, Pei; Xue, Li; Chen, Baiyun; Liu, Wei; Shi, Wanwan; Wang, Yongwang; Chen, Xiangtao; Luo, Jiawei*.A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes. Bioinformatics, 2024,40(10): btae607.
2. Wei Liu, Bo Wang, Yuting Bai, Xiao Liang, Li Xue, Jiawei Luo*. SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning. Briefings in Bioinformatics, 2024,25(6):bbae578.
3. Cong Shen, Pingjian Ding, Junjie Wee, Jialin Bi, Jiawei Luo*, Kelin Xia. Curvature-enhanced graph convolutional network for biomolecularinteraction prediction. Computational and Structural Biotechnology Journal，2024,23:1016–1025.
4. Pei Liu,Ying Liu,Jiawei Luo*,Yue Li. MiRGraph: A hybrid deep learning approach toidentify microRNA-target interactions by integratingheterogeneous regulatory network and genomicsequences. IEEE International Conference on Bioinformatics and Biomedicine,2024,1028-1035.
5. Bo Wang, Wei Liu, Jiawei Luo, Xiangtao Chen, Chee Keong Kwoh. SMMGCL: a novel multi-level graph contrastivelearning framework for integrating spatialmulti-omics data.IEEE International Conference on Bioinformatics and Biomedicine,2024,1213-1218.
6.唐勇轩,梁潇，骆嘉伟*.基于分类自动编码器的单细胞 RNA 测序数据降维方法 scAC.南京大学学报(自然科学），2024,60(6):920-929.
2023
1.Cong Shen, Jiawei Luo*, Kelin Xia. Molecular geometric deep learning. Cell Reports Methods, 2023,3(11):10621
2. Zehao Xiong , Jiawei Luo*, Wanwan Shi, Ying Liu, Zhongyuan Xu,Bo Wang. scGCL: an imputation method for scRNA-seq data basedon graph contrastive learning. Bioinformatics, 2023,39(3): btad098.
3. Bo Wang, Jiawei Luo*, Ying Liu, Wanwan Shi, Zehao Xiong, Cong Shen, Yahui Long. Spatial-MGCN: a novel multi-view graph convolutionalnetwork for identifying spatial domains with attentionmechanism.Briefings in Bioinformatics, 2023,24(5):bbad262.
4. Yezi He , Xiangtao Chen , Nguyen Hoang Tu , Jiawei Luo*. Deep Multi-Constraint Soft Clustering Analysisfor Single-Cell RNA-Seq Data via Zero-InflatedAutoencoder Embedding. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023,20(3): 2254-2265.
5. Zehao Xiong, Xiangtao Chen, Jiawei Luo*, Cong Shen, Zhongyuan Xu. scSAGAN: A scRNA-seq data imputation methodbased on Semi-Supervised Learning andProbabilistic Latent Semantic Analysis. IEEE International Conference on Bioinformatics and Biomedicine,2022,178-181
2022
1.Jiawei Luo, Yi Liu, Pei Liu, Zihan Lai, Hao Wu. Data Integration Using Tensor Decomposition for
the Prediction of miRNA-Disease Associations. IEEE Journal of Biomedical and Health Informatics, 2022,26(5):2370-2378.
2. Jiawei Luo*, Wenjue Ouyang, Cong Shen, Jie Cai. Multi-Relation Graph Embedding for Predicting
miRNA-Target Gene Interactions by Integrating
Gene Sequence Information. IEEE Journal of Biomedical and Health Informatics, 2022,26(8):4345-4353.
3. Zhongyuan Xu, Jiawei Luo*,Zehao Xiong. scSemiGAN: a single-cell semi-supervised annotation
and dimensionality reduction framework based on
generative adversarial network. Bioinformatics, 2022,38(22):5042–5048.
4. Yahui Long, Yu Zhang, Min Wu, Shaoliang Peng, Chee Keong Kwoh, Jiawei Luo*,
Xiaoli Li*. Heterogeneous graph attention networks for drug virus
association prediction. Methods, 2022, 198:11-18.
5. Ying Liu, Ruihui Li, Jiawei Luo*, Zhaolei Zhang*. Inferring RNA-binding protein target
preferences using adversarial domain
adaptation. PLOS Computational Biology, 2022, 18(2): e1009863.
6. Pei Liu, Jiawei Luo*, Xiangtao Chen. miRCom: Tensor Completion Integrating
Multi-View Information to Deduce the Potential
Disease-Related miRNA-miRNA Pairs. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022,19(3): 1747-1759.
7. Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Keong Kwoh,
Jinmiao Chen, Jiawei Luo* Xiaoli Li*. Pre-training graph neural networks for link prediction in
biomedical networks. Bioinformatics, 2022,38(8):2254–2262.
8. Qiu Xiao, Jianhua Dai, Jiawei Luo*. A survey of circular RNAs in complex diseases:
databases, tools and computational methods. Briefings in Bioinformatics, 2022,22(1):bbab444.
2021
1. Xinru Tang, Jiawei Luo*, Cong Shen, Zihan Lai. Multi-view Multichannel Attention Graph
Convolutional Network for miRNA–disease association prediction. Briefings in Bioinformatics, 2021,22(6):bbab174.
2. Yahui Long, Min Wu, Yong Liu, Jie Zheng, Chee-Keong Kwoh, Jiawei Luo*, Xiaoli Li. Graph Contextualized Attention Network for Predicting Synthetic Lethality in Human Cancers. Bioinformatics, 2021,37(16):2432–2440.
3. Weidun Xie, Jiawei Luo*, Chu Pan, Ying Liu. SG-LSTM-FRAME: a computational frame using sequence and geometrical information via LSTM to predict miRNA–gene associations. Briefings in Bioinformatics, 2021,22(2):2032–2042.
4. Yahui Long, Jiawei Luo*, Yu Zhang, Yan Xia. Predicting human microbe–disease associations via graph attention networks with inductive matrix completion. Briefings in Bioinformatics, 2021,22(3): bbaa146.
5. Qiu Xiao, Ning Zhang, Jiawei Luo*, Jianhua Dai, Xiwei Tang. Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs. Briefings in Bioinformatics, 2021,22(2):2043-2057.
6. Jiawei Luo, Cong Shen , Zihan Lai, Jie Cai, Pingjian Ding.Incorporating Clinical, Chemical and Biological Information for Predicting Small Molecule-microRNA Associations Based on Non-Negative Matrix Factorization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021,18(6): 2535-2545.
7. Pingjian Ding, Cheng Liang, Wenjue Ouyang, Guanghui Li, Qiu Xiao, Jiawei Luo*. Inferring Synergistic Drug Combinations Based
on Symmetric Meta-Path in a Novel
Heterogeneous Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021,18(4): 1562-1571.
8. Cheng Liang, Mingchao Shang, Jiawei Luo*. Cancer subtype identification by consensus guided
graph autoencoders. Bioinformatics, 37(24), 2021, 4779–4786.
9. Jiawei Luo, Zihan Lai, Cong Shen, Pei Liu, Heyuan Shi. Graph AttentionMechanism-based Deep Tensor Factorization for Predicting disease-associatedmiRNA-miRNA pairs. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021: 189-196.
2020
1.Yahui Long, Min Wu, Chee-Keong Kwoh, Jiawei Luo*, Xiaoli Li. Predicting Human Microbe-Drug Associations via Graph Convolutional Network with Conditional Random Field. Bioinformatics, 2020,36(19), 4918-4927.
2. Cong Shen, Jiawei Luo*, Wenjue Ouyang, Pingjian Ding, Xiangtao Chen. IDDkin: network-based influence deep diffusion model for enhancing prediction of kinase inhibitors. Bioinformatics, 2020, 36(22-23): 5481–5491.
3.Yahui Long, Min Wu, Yong Liu, Chee-Keong, Kwoh, Jiawei Luo*, Xiaoli Li. Ensembling graph attention networks for human microbe-drug association prediction. Bioinformatics, 2020,36: i779-i786.
4. Qiu Xiao, Jiawei Luo*, Cheng Liang, Guanghui Li, Jie Cai, Pingjian Ding, Ying Liu. Identifying lncRNA and mRNA Co-Expression Modules from Matched Expression Data in Ovarian Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020,17(2): 623-634.
5. Pingjian Ding, Wenjue Ouyang, Jiawei Luo*, Chee-Keong Kwoh. Heterogeneous information network and its application to human health and disease. Briefings in Bioinformatics, 2020, 21(4):1327-1346.
6. Cong Shen, Jiawei Luo*, Wenjue Ouyang, Pingjian Ding, Hao Wu. Identification of Small Molecule–miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks. Journal of Chemical Information and Modeling, 2020.60(12): 6709–672.
7. Chu Pan , Jiawei Luo*, Jiao Zhang. Computational Identification of RNA-Seq Based miRNA-Mediated Prognostic Modules in Cancer. IEEE Journal of Biomedical and Health Informatics, 2020, 24(2): 626-633.
8. Cong Shen, Jiawei Luo*, Zihan Lai, Pingjian Ding. Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge. Journal of Chemical Information and Modeling, 2020, 60(8): 4085-4097.
9. Chu Pan, Jiawei Luo*, Jiao Zhang, Xin Li. BiModule: Biclique Modularity Strategy for Identifying Transcription Factor and microRNA Co-Regulatory Modules. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020,17(1):321-326.
10. Yahui Long, Jiawei Luo*. Association mining to identify microbe drug interactions based on heterogeneous network embedding representation. IEEE Journal of Biomedical and Health Informatics, 2020,25(01): 266-275.
11. Ying Liu, Chu Pan, Dehan Kong, Jiawei Luo*, Zhaolei Zhang. A Survey of Regulatory Interactions Among RNA Binding Proteins and MicroRNAs in Cancer. Frontiers in Genetics, 2020, 11:515094.
2019
1. Qiu Xiao, Jianhua Dai, Jiawei Luo*, Hamido Fujita. Multi-view manifold regularized learning-based method for prioritizing candidate disease miRNAs. Knowledge Based Systems, 2019, 175:118-129.
2. Cheng Liang, Shengpeng Yu, Jiawei Luo*. Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs. PLOS Computational Biology, 2019, 15(4): e1006931.
3. Pingjian Ding, Rui Yin, Jiawei Luo*, Chee Keong Kwoh. Ensemble Prediction of Synergistic Drug CombinationsIncorporating Biological, Chemical, Pharmacological and Network Knowledge. IEEE Journal of Biomedical and Health Informatics, 2019,23(3):1336-1345.
4. Jiawei Luo, Chu Pan, GenXiang, Ying,Yin. A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019,16(2):681-687.
5. Qiu Xiao, Jiawei Luo*, Cheng Liang, Jie Cai, Guanghui Li, Buwen Cao. CeModule: an integrative framework for discovering regulatorypatterns from genomic data in cancer. BMC Bioinformatics, 2019,20:67.
6. Ying Liu, Jiawei Luo*, PingjianDing. Inferring MicroRNA Targets Based on Restricted Boltzmann Machines. IEEE Journal of Biomedical and Health Informatics, 2019. 23(1): 427-436.
7. Yahui Long, Jiawei Luo*.WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network. BMC Bioinformatics, 2019,20:541.
2018
1. Qiu Xiao, Jiawei Luo*, ChengLiang, Jie Cai, Pingjian Ding. A graph regularized non-negative matrix factorizationmethod for identifying microRNA-disease associations. Bioinformatics. 2018. 34(2): 239-248.
2. Jiawei Luo*, Yahui Long. NTSHMDA:Prediction of Human Microbe-Disease Association based on Random Walk byIntegrating Network Topological Similarity. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2018. Early Access.
3. Qiu Xiao, Jiawei Luo*, Cheng Liang, Guanghui Li, Jie Cai, Pingjian Ding, Ying Liu. Identifying lncRNA andmRNA co-expression modules from matched expression data in ovarian Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2018. Early Access.
4. Jiawei Luo*, Pingjian Ding,Cheng Liang, Xiangtao Chen. Semi-supervised prediction of human miRNA-diseaseassociation based on graph regularization framework in heterogeneous networks. Neurocomputing.2018. 294(4): 29-38.
5. Pingjian Ding, Jiawei Luo*,Cheng Liang, Qiu Xiao, Buwen Cao, Guanghui Li. Discovering synergistic drugcombination from a computational perspective. Current topics in medicinalchemistry. 2018. 18(12): 965-974.
6. Jiawei Luo*, Ying Yin, Chu Pan,Gen Xiang, Nguyen Hoang Tu. Identifying functional modules in co-regulatorynetworks through overlapping spectral clustering. IEEE Transactions on nanobioscience. 2018. 17(2):134-144.
7. Pingjian Ding, Jiawei Luo*,Cheng Liang, Qiu Xiao, Buwen Cao. Human disease MiRNA inference by combiningtarget information based on heterogeneous manifolds. Journal of biomedical informatics. 2018. 80(6): 26-36.
8. Jiawei Luo*, Wei Huang, BuwenCao. A novel approach to identify the mirna-mrna causal regulatory modules incancer. IEEE/ACM transactions oncomputational biology and bioinformatics. 2018. 15(1): 309-315.
9. Jiawei Luo*, Lv Ding, ChengLiang, Nguyen Hoang Tu. An efficient network motif discovery approach forco-regulatory networks. IEEE Access 2018. 6:14151-14158.
10. Guanghui Li*, Jiawei Luo*, QiuXiao, Cheng Liang, Pingjian Ding. Prediction of microRNA–disease associationswith a Kronecker kernel matrix dimension reduction model. RSC Advances. 2018. 8(8):4377-4385.
2017
1. Jiawei Luo* ; Pingjian Ding; Cheng Liang; Buwen Cao; Xiangtao Chen, Collective prediction of disease-associated miRNAs based on transduction learning, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14(6): 1468~1475 (SCI)
2. Pingjian Ding ; Jiawei Luo* ; Cheng Liang; Jie Cai; Ying Liu; Xiangtao Chen, A Novel Group Wise-Based Method for Calculating Human miRNA Functional Similarity , IEEE ACCESS, 2017, 5: 2364~2372 (SCI)
3. Luo, Jiawei*; Xiao, Qiu, A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network , JOURNAL OF BIOMEDICAL INFORMATICS, 2017, 66: 194~203 (SCI)
4. Luo, Jiawei*; Xiang, Gen; Pan, Chu, Discovery of microRNAs and
Transcription Factors Co-Regulatory Modules by Integrating Multiple Types of Genomic Data , IEEE TRANSACTIONS ON NANOBIOSCIENCE, 2017, 16(1): 51~59 (SCI)
5. Zhiming Liu ; Jiawei Luo*, Genome-wide predicting disease-related protein complexes by walking on the heterogeneous network based on data integration and laplacian normalization , COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2017, 69: 41~47 (SCI)
6. Jiawei Luo*; Qiu Xiao; Cheng Liang; Pingjian Ding, Predicting MicroRNA-Disease Associations Using Kronecker Regularized Least Squares Based on Heterogeneous Omics Data , IEEE ACCESS, 2017, 5: 2503~2513 (SCI)
7. Zakouni, Amiyne ; Luo, Jiawei*; Kharroubi, Fouad* , Genetic algorithm and tabu search algorithm for solving the static manycast RWA problem in optical networks , JOURNAL OF COMBINATORIAL OPTIMIZATION, 2017, 33(2): 726~741 (SCI)
8. Luo, Jiawei* ; Liu, Chengchen, An Effective Method for Identifying Functional Modules in Dynamic PPI Networks , CURRENT BIOINFORMATICS, 2017, 12(1): 66~79 (SCI)
9. 骆嘉伟; 宋丹; 蔡洁; 王伟胜; 刘智明, 一种基于功能模块的疾病关联因子识别方法及系统（申请并进入实审）, 2017.1.18, 中国, N201710035109.2 (发明专利)
2016
1. Liang, Cheng ; Li, Yue; Luo, Jiawei* , A Novel Method to Detect Functional microRNA Regulatory Modules by Bicliques Merging , IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2016, 13(3): 549~556 (SCI)
2. Cao, Buwen ; Luo, Jiawei* ; Liang, Cheng; Wang, Shulin; Ding, Pingjian, PCE-FR: A Novel Method for Identifying Overlapping Protein Complexes in Weighted Protein-Protein Interaction Networks Using Pseudo-Clique Extension Based on Fuzzy Relation , IEEE TRANSACTIONS ON NANOBIOSCIENCE, 2016, 15(7): 728~738 (SCI)
3. Jiawei Luo*; Cong Huang; Pingjian Ding, A Meta-Path-Based Prediction Method for Human miRNA-Target Association. , Biomed Res Int, 2016, 2016:7460740 (SCI)
4. Luo, Jiawei*; Lin, Dingyu; Cao, Buwen, A cell-core-attachment approach for identifying protein complexes in yeast protein-protein interaction network , JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2016, 31(2): 967~978 (SCI)
5. Ding, Pingjian ; Luo, Jiawei*; Xiao, Qiu; Chen, Xiangtao, A path-based measurement for human miRNA functional similarities using miRNA-disease associations , SCIENTIFIC REPORTS, 2016, 6: 32533 (SCI)
6. Buwen Cao ; Jiawei Luo*; Cheng Liang; Shulin Wang, Detecting overlapping protein complexes in weighted protein-protein interaction networks using pseudo-clique extension based on fuzzy relation, International Joint Conference on Neural Networks, Vancouver, BC, 2016.7.24-2016.7.29
2015
1. Jiawei Luo*; Yi Qi, Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes , PLos One, 2015, 10(6): e0131418 (SCI)
2. Liang, Cheng ; Li, Yue ; Luo, Jiawei* ; Zhang, Zhaolei*, A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human , BIOINFORMATICS, 2015, 31(14): 2348~2355 (SCI IF=5.481)
3. Cao, Buwen ; Luo, Jiawei* ; Liang, Cheng; Wang, Shulin; Song, Dan, MOEPGA: A novel method to detect protein complexes in yeast protein-protein interaction networks based on Multi Objective Evolutionary Programming Genetic Algorithm , COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2015, 58: 173~181 (SCI)
4. Jiawei Luo*; Xiaoshuang Liang, Discovering co-regulated modules based on protein interaction and transcriptional regulatory networks , Journal of Computational Information Systems, 2015, 11(8): 3041~3049 (SCI)
5. Jiawei Luo* ; Juan Wu, A new algorithm for essential proteins identification based on the integration of protein complex co-expression information and edge clustering coefficient , International Journal of Data Mining and Bioinformatics, 2015, 12(3): 257~274 (SCI)
6. Luo, Jiawei*; Liang, Shiyu, Prioritization of potential candidate disease genes by topological similarity of protein-protein interaction network and phenotype data , JOURNAL OF BIOMEDICAL INFORMATICS, 2015, 53: 229~236 (SCI)
7. Luo, Jiawei*; Liu, Chengchen; Hoang Tu Nguyen, A Core-Attach Based Method for Identifying Protein Complexes in Dynamic PPI Networks , 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), John Neumann Inst, Ho Chi Minh City, 2015.5.19-2015.5.22
8. Hisham Albukhaiti ; Jiawei Luo* , A Feature Selection Approach for Classifying Diseases Based on Medical Data, 2015 Global Conference on Biological Engineering and Biomedical, Shanghai, Shanghai, 2015.1.17-2015.1.18
2014
1. Jiawei Luo* ; Guanghui Li; Dan Song; Cheng Liang, CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks , Physica A: Statistical Mechanics and Its Applications, 2014, 416: 309~320 (SCI)
2. Lin, Hongli*; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong, Development of a Personalized Training System Using the Lung Image Database Consortium and Image Database Resource Initiative Database , ACADEMIC RADIOLOGY, 2014, 21 (12): 1614~1622 (SCI)
3. Li, Yue* ; Liang, Cheng ; Easterbrook, Steve; Luo, Jiawei ; Zhang, Zhaolei, Investigating the functional implications of reinforcing feedback loops in transcriptional regulatory networks , MOLECULAR BIOSYSTEMS, 2014, 10(12): 3238~3248 (SCI)
4. Li, Yue* ; Liang, Cheng ; Wong, Ka-Chun; Luo, Jiawei ; Zhang, Zhaolei*, Mirsynergy: detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion , BIOINFORMATICS, 2014, 30(18): 2627~2635 (SCI IF=5.481)
5. Liang, Cheng ; Jiawei, Luo* ; Song, Dan, Network simulation reveals significant contribution of network motifs to the age-dependency of yeast protein-protein interaction networks , MOLECULAR BIOSYSTEMS, 2014, 10(9): 2277~2288 (SCI)
6. Luo, Jiawei* ; Wei, Miao, An accelerated network Motif detection algorithm using the structure of basic symmetric subgraph , Journal of Computational Information Systems, 2014, 10(17): 7315~7322 (SCI)
7. Luo, Jiawei* ; Zhang, Nan, PREDICTION OF ESSENTIAL PROTEINS BASED ON EDGE CLUSTERING COEFFICIENT AND GENE ONTOLOGY INFORMATION , JOURNAL OF BIOLOGICAL SYSTEMS, 2014, 22(3): 1~13 (SCI)
8. Luo Jiawei* ; Liu Shunmin, A Novel Essential Protein Identification Algorithm Based on the Integration of Local Network Topology and Gene Ontology , JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE, 2014, 11(3): 619~624 (SCI)
9. Luo, Jiawei* ; Li, Guanghui; Song, Dan; Liang, Cheng, Integrating Functional and Topological Properties to Identify Biological Network Motif in Protein Interaction Networks , JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE, 2014, 11(3): 744~750 (SCI)
10. Lin, Hongli* ; Yang, Xuedong ; Wang, Weisheng; Luo, Jiawei , A Performance Weighted Collaborative Filtering algorithm for personalized radiology education , JOURNAL OF BIOMEDICAL INFORMATICS, 2014, 51: 107~113 (SCI)
11. Luo, Jiawei* ; Kuang, Ling, A new method for predicting essential proteins based on dynamic network topology and complex information , COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2014, 52: 34~42 (SCI)
12. Thi-Thiet Pham ; Luo, Jiawei* ; Tzung-Pei Hong; Bay Vo , An efficient method for mining non-redundant sequential rules using attributed prefix-trees , ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2014, 32: 88~99 (SCI)
