Zhu-Hong You
Orcid: 0000-0003-1266-2696Affiliations:
- Shenzhen University, College of Computer Science and Software Engineering, China
According to our database1,
Zhu-Hong You
authored at least 201 papers
between 2007 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.
BMC Bioinform., December, 2024
MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.
IEEE J. Biomed. Health Informatics, March, 2024
GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.
IEEE J. Biomed. Health Informatics, March, 2024
SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.
J. Chem. Inf. Model., January, 2024
Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.
Briefings Bioinform., January, 2024
Fusing Higher and Lower-Order Biological Information for Drug Repositioning via Graph Representation Learning.
IEEE Trans. Emerg. Top. Comput., 2024
Dual-Channel Learning Framework for Drug-Drug Interaction Prediction via Relation-Aware Heterogeneous Graph Transformer.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
PDA-PRGCN: identification of Piwi-interacting RNA-disease associations through subgraph projection and residual scaling-based feature augmentation.
BMC Bioinform., December, 2023
GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm.
BMC Bioinform., December, 2023
An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.
Comput. Biol. Medicine, October, 2023
PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network.
Briefings Bioinform., September, 2023
BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path.
J. Chem. Inf. Model., August, 2023
iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.
Bioinform., August, 2023
Biomedical Knowledge Graph Embedding With Capsule Network for Multi-Label Drug-Drug Interaction Prediction.
IEEE Trans. Knowl. Data Eng., June, 2023
A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.
Briefings Bioinform., May, 2023
J. Frankl. Inst., March, 2023
Bioinform., February, 2023
SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs.
Briefings Bioinform., January, 2023
IEEE J. Biomed. Health Informatics, 2023
PPAEDTI: Personalized Propagation Auto-Encoder Model for Predicting Drug-Target Interactions.
IEEE J. Biomed. Health Informatics, 2023
IEEE Trans. Cybern., 2023
Predicting Mirna-Disease Associations Based on Neighbor Selection Graph Attention Networks.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
Combining K Nearest Neighbor With Nonnegative Matrix Factorization for Predicting Circrna-Disease Associations.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
Predicting MiRNA-Disease Associations by Graph Representation Learning Based on Jumping Knowledge Networks.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.
PLoS Comput. Biol., 2023
In silico prediction methods of self-interacting proteins: an empirical and academic survey.
Frontiers Comput. Sci., 2023
PEFNet: Position Enhancement Faster Network for Object Detection in Roadside Perception System.
IEEE Access, 2023
Multi-level Subgraph Representation Learning for Drug-Disease Association Prediction Over Heterogeneous Biological Information Network.
Proceedings of the Advanced Intelligent Computing Technology and Applications, 2023
Proceedings of the Advanced Intelligent Computing Technology and Applications, 2023
A Novel Graph Representation Learning Model for Drug Repositioning Using Graph Transition Probability Matrix Over Heterogenous Information Networks.
Proceedings of the Advanced Intelligent Computing Technology and Applications, 2023
Hypertension Risk Prediction Among Diabetic Patients Using Unconditional Multivariate Logistic Regression Model.
Proceedings of the 7th International Conference on Biomedical Engineering and Applications, 2023
Deep-USIpred: identifying substrates of ubiquitin protein ligases E3 and deubiquitinases with pretrained protein embedding and bayesian neural network.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
CAMPEOD: A Cross Attention-Based Multi-Scale Patch Embedding Organoid Detection Model.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
2022
IEEE J. Biomed. Health Informatics, 2022
Identifying Protein Complexes From Protein-Protein Interaction Networks Based on Fuzzy Clustering and GO Semantic Information.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction.
BMC Bioinform., 2022
Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.
BMC Bioinform., 2022
Predicting miRNA-disease associations based on graph random propagation network and attention network.
Briefings Bioinform., 2022
Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.
Briefings Bioinform., 2022
HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.
Briefings Bioinform., 2022
iGRLCDA: identifying circRNA-disease association based on graph representation learning.
Briefings Bioinform., 2022
Briefings Bioinform., 2022
A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction.
Briefings Bioinform., 2022
Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions.
Briefings Bioinform., 2022
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2.
Briefings Bioinform., 2022
A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.
Briefings Bioinform., 2022
MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.
Briefings Bioinform., 2022
A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.
Briefings Bioinform., 2022
GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.
Briefings Bioinform., 2022
Identification of miRNA-lncRNA Underlying Interactions Through Representation for Multiplex Heterogeneous Network.
Proceedings of the Intelligent Computing Theories and Application, 2022
MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction.
Proceedings of the Intelligent Computing Theories and Application, 2022
Predicting Drug-Disease Associations via Meta-path Representation Learning based on Heterogeneous Information Net works.
Proceedings of the Intelligent Computing Theories and Application, 2022
Predicting circRNA-disease associations using similarity assessing graph convolution from multi-source information networks.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022
2021
IEEE Trans. Netw. Sci. Eng., 2021
IMS-CDA: Prediction of CircRNA-Disease Associations From the Integration of Multisource Similarity Information With Deep Stacked Autoencoder Model.
IEEE Trans. Cybern., 2021
MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
Sci. Program., 2021
FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method.
Sci. Program., 2021
Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.
BMC Medical Informatics Decis. Mak., 2021
Learning from low-rank multimodal representations for predicting disease-drug associations.
BMC Medical Informatics Decis. Mak., 2021
LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization.
Neurocomputing, 2021
A computational approach for predicting drug-target interactions from protein sequence and drug substructure fingerprint information.
Int. J. Intell. Syst., 2021
In silico drug repositioning using deep learning and comprehensive similarity measures.
BMC Bioinform., 2021
A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.
BMC Bioinform., 2021
HiSCF: leveraging higher-order structures for clustering analysis in biological networks.
Bioinform., 2021
SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.
Briefings Bioinform., 2021
Briefings Bioinform., 2021
Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning.
Briefings Bioinform., 2021
Briefings Bioinform., 2021
MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm.
Briefings Bioinform., 2021
NMFCDA: Combining randomization-based neural network with non-negative matrix factorization for predicting CircRNA-disease association.
Appl. Soft Comput., 2021
SANE: A sequence combined attentive network embedding model for COVID-19 drug repositioning.
Appl. Soft Comput., 2021
Proceedings of the Intelligent Computing Theories and Application, 2021
CNNEMS: Using Convolutional Neural Networks to Predict Drug-Target Interactions by Combining Protein Evolution and Molecular Structures Information.
Proceedings of the Intelligent Computing Theories and Application, 2021
Weighted Nonnegative Matrix Factorization Based on Multi-source Fusion Information for Predicting CircRNA-Disease Associations.
Proceedings of the Intelligent Computing Theories and Application, 2021
Detection of Drug-Drug Interactions Through Knowledge Graph Integrating Multi-attention with Capsule Network.
Proceedings of the Intelligent Computing Theories and Application, 2021
Protein-Protein Interaction Prediction by Integrating Sequence Information and Heterogeneous Network Representation.
Proceedings of the Intelligent Computing Theories and Application, 2021
Computational Prediction of Protein-Protein Interactions in Plants Using Only Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2021
Predicting miRNA-Disease Associations via a New MeSH Headings Representation of Diseases and eXtreme Gradient Boosting.
Proceedings of the Intelligent Computing Theories and Application, 2021
2020
A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.
BMC Medical Informatics Decis. Mak., March, 2020
IEEE Trans. Comput. Soc. Syst., 2020
Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
Incorporating the Coevolving Information of Substrates in Predicting HIV-1 Protease Cleavage Sites.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
Learning Multimodal Networks From Heterogeneous Data for Prediction of lncRNA-miRNA Interactions.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.
PLoS Comput. Biol., 2020
GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.
PLoS Comput. Biol., 2020
GANCDA: a novel method for predicting circRNA-disease associations based on deep generative adversarial network.
Int. J. Data Min. Bioinform., 2020
A survey of current trends in computational predictions of protein-protein interactions.
Frontiers Comput. Sci., 2020
RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.
BMC Bioinform., 2020
NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information.
BMC Bioinform., 2020
An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convolutional neural network.
Bioinform., 2020
Bioinform., 2020
Prediction of Drug-Target Interactions by Ensemble Learning Method From Protein Sequence and Drug Fingerprint.
IEEE Access, 2020
GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions.
IEEE Access, 2020
Prediction of lncRNA-miRNA Interactions via an Embedding Learning Graph Factorize Over Heterogeneous Information Network.
Proceedings of the Intelligent Computing Theories and Application, 2020
Proceedings of the Intelligent Computing Theories and Application, 2020
Predicting Human Disease-Associated piRNAs Based on Multi-source Information and Random Forest.
Proceedings of the Intelligent Computing Theories and Application, 2020
Predicting LncRNA-miRNA Interactions via Network Embedding with Integrated Structure and Attribute Information.
Proceedings of the Intelligent Computing Theories and Application, 2020
A Novel Computational Method for Predicting LncRNA-Disease Associations from Heterogeneous Information Network with SDNE Embedding Model.
Proceedings of the Intelligent Computing Theories and Application, 2020
Predicting Protein-Protein Interactions from Protein Sequence Using Locality Preserving Projections and Rotation Forest.
Proceedings of the Intelligent Computing Theories and Application, 2020
A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder-Decoder Model.
Proceedings of the Intelligent Computing Theories and Application, 2020
DTIFS: A Novel Computational Approach for Predicting Drug-Target Interactions from Drug Structure and Protein Sequence.
Proceedings of the Intelligent Computing Theories and Application, 2020
A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions.
Proceedings of the Intelligent Computing Theories and Application, 2020
GCNSP: A Novel Prediction Method of Self-Interacting Proteins Based on Graph Convolutional Networks.
Proceedings of the Intelligent Computing Theories and Application, 2020
WGMFDDA: A Novel Weighted-Based Graph Regularized Matrix Factorization for Predicting Drug-Disease Associations.
Proceedings of the Intelligent Computing Methodologies - 16th International Conference, 2020
Embracing Disease Progression with a Learning System for Real World Evidence Discovery.
Proceedings of the Intelligent Computing Theories and Application, 2020
A Novel Computational Approach for Predicting Drug-Target Interactions via Network Representation Learning.
Proceedings of the Intelligent Computing Theories and Application, 2020
Prediction of lncRNA-Disease Associations from Heterogeneous Information Network Based on DeepWalk Embedding Model.
Proceedings of the Intelligent Computing Methodologies - 16th International Conference, 2020
Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform.
Proceedings of the Intelligent Computing Theories and Application, 2020
A Network Embedding-Based Method for Predicting miRNA-Disease Associations by Integrating Multiple Information.
Proceedings of the Intelligent Computing Methodologies - 16th International Conference, 2020
A Highly Efficient Biomolecular Network Representation Model for Predicting Drug-Disease Associations.
Proceedings of the Intelligent Computing Methodologies - 16th International Conference, 2020
A MapReduce-Based Parallel Random Forest Approach for Predicting Large-Scale Protein-Protein Interactions.
Proceedings of the Intelligent Computing Methodologies - 16th International Conference, 2020
Identification of Autistic Risk Genes Using Developmental Brain Gene Expression Data.
Proceedings of the Intelligent Computing Theories and Application, 2020
Inferring Drug-miRNA Associations by Integrating Drug SMILES and MiRNA Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2020
Predicting Drug-Target Interactions by Node2vec Node Embedding in Molecular Associations Network.
Proceedings of the Intelligent Computing Theories and Application, 2020
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020
2019
Protein-Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine.
IEEE J. Biomed. Health Informatics, 2019
An Efficient Attribute-Based Encryption Scheme With Policy Update and File Update in Cloud Computing.
IEEE Trans. Ind. Informatics, 2019
An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information.
IEEE ACM Trans. Comput. Biol. Bioinform., 2019
LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.
PLoS Comput. Biol., 2019
Plant disease leaf image segmentation based on superpixel clustering and EM algorithm.
Neural Comput. Appl., 2019
Using discriminative vector machine model with 2DPCA to predict interactions among proteins.
BMC Bioinform., 2019
Briefings Bioinform., 2019
Improved Prediction of Protein-Protein Interactions Using Descriptors Derived From PSSM via Gray Level Co-Occurrence Matrix.
IEEE Access, 2019
CGMDA: An Approach to Predict and Validate MicroRNA-Disease Associations by Utilizing Chaos Game Representation and LightGBM.
IEEE Access, 2019
MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System.
Proceedings of the Intelligent Computing Methodologies - 15th International Conference, 2019
An Efficient LightGBM Model to Predict Protein Self-interacting Using Chebyshev Moments and Bi-gram.
Proceedings of the Intelligent Computing Theories and Application, 2019
In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2019
A Gated Recurrent Unit Model for Drug Repositioning by Combining Comprehensive Similarity Measures and Gaussian Interaction Profile Kernel.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins.
Proceedings of the Intelligent Computing Theories and Application, 2019
LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction.
Proceedings of the Intelligent Computing Theories and Application, 2019
Predicting of Drug-Disease Associations via Sparse Auto-Encoder-Based Rotation Forest.
Proceedings of the Intelligent Computing Methodologies - 15th International Conference, 2019
Proceedings of the Intelligent Computing Theories and Application, 2019
Learning from Deep Representations of Multiple Networks for Predicting Drug-Target Interactions.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining LSTM Network Model and Wavelet Transform for Predicting Self-interacting Proteins.
Proceedings of the Intelligent Computing Theories and Application, 2019
Predicting circRNA-disease associations using deep generative adversarial network based on multi-source fusion information.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019
2018
Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data.
IEEE Trans. Cybern., 2018
An improved efficient rotation forest algorithm to predict the interactions among proteins.
Soft Comput., 2018
HEMD: a highly efficient random forest-based malware detection framework for Android.
Neural Comput. Appl., 2018
A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.
J. Comput. Biol., 2018
DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model.
Neurocomputing, 2018
Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine.
Complex., 2018
Prediction of protein self-interactions using stacked long short-term memory from protein sequences information.
BMC Syst. Biol., 2018
Constructing prediction models from expression profiles for large scale lncRNA-miRNA interaction profiling.
Bioinform., 2018
Bioinform., 2018
A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases.
Bioinform., 2018
Efficient Framework for Predicting ncRNA-Protein Interactions Based on Sequence Information by Deep Learning.
Proceedings of the Intelligent Computing Theories and Application, 2018
Using Weighted Extreme Learning Machine Combined with Scale-Invariant Feature Transform to Predict Protein-Protein Interactions from Protein Evolutionary Information.
Proceedings of the Intelligent Computing Theories and Application, 2018
Discovering an Integrated Network in Heterogeneous Data for Predicting lncRNA-miRNA Interactions.
Proceedings of the Intelligent Computing Theories and Application, 2018
RP-FIRF: Prediction of Self-interacting Proteins Using Random Projection Classifier Combining with Finite Impulse Response Filter.
Proceedings of the Intelligent Computing Theories and Application, 2018
Learning Latent Patterns in Molecular Data for Explainable Drug Side Effects Prediction.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018
2017
IEEE Trans. Cybern., 2017
Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding.
IEEE ACM Trans. Comput. Biol. Bioinform., 2017
PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning.
IEEE ACM Trans. Comput. Biol. Bioinform., 2017
PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
PLoS Comput. Biol., 2017
An improved sequence-based prediction protocol for protein-protein interactions using amino acids substitution matrix and rotation forest ensemble classifiers.
Neurocomputing, 2017
Comput. Intell. Neurosci., 2017
Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases.
Comput. Electron. Agric., 2017
Leaf image based cucumber disease recognition using sparse representation classification.
Comput. Electron. Agric., 2017
NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations.
Database J. Biol. Databases Curation, 2017
Long non-coding RNAs and complex diseases: from experimental results to computational models.
Briefings Bioinform., 2017
Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network.
Proceedings of the Bioinformatics Research and Applications - 13th International Symposium, 2017
2016
A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method.
IEEE Trans. Neural Networks Learn. Syst., 2016
IEEE Trans. Cybern., 2016
An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering.
IEEE Trans Autom. Sci. Eng., 2016
Construction of reliable protein-protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation features.
Neurocomputing, 2016
Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.
BMC Syst. Biol., 2016
Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.
BMC Bioinform., 2016
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
2015
An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems.
IEEE Trans. Ind. Informatics, 2015
Improving network topology-based protein interactome mapping via collaborative filtering.
Knowl. Based Syst., 2015
Detection of Protein-Protein Interactions from Amino Acid Sequences Using a Rotation Forest Model with a Novel PR-LPQ Descriptor.
Proceedings of the Advanced Intelligent Computing Theories and Applications, 2015
Predicting Protein-Protein Interactions from Amino Acid Sequences Using SaE-ELM Combined with Continuous Wavelet Descriptor and PseAA Composition.
Proceedings of the Intelligent Computing Theories and Methodologies, 2015
2014
A MapReduce based parallel SVM for large-scale predicting protein-protein interactions.
Neurocomputing, 2014
Predicting dynamic deformation of retaining structure by LSSVR-based time series method.
Neurocomputing, 2014
Comput. Vis. Image Underst., 2014
Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set.
BMC Bioinform., 2014
Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding.
Proceedings of the Bioinformatics Research and Applications - 10th International Symposium, 2014
Using Chou's amphiphilic Pseudo-Amino Acid Composition and Extreme Learning Machine for prediction of Protein-protein interactions.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014
2013
Pattern Recognit. Lett., 2013
Increasing the reliability of protein-protein interaction networks via non-convex semantic embedding.
Neurocomputing, 2013
Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis.
BMC Bioinform., 2013
Prediction of protein-protein interactions from amino acid sequences using extreme learning machine combined with auto covariance descriptor.
Proceedings of the 2013 IEEE Workshop on Memetic Computing, 2013
A SVM-Based System for Predicting Protein-Protein Interactions Using a Novel Representation of Protein Sequences.
Proceedings of the Intelligent Computing Theories - 9th International Conference, 2013
Research on Signaling Pathways Reconstruction by Integrating High Content RNAi Screening and Functional Gene Network.
Proceedings of the Intelligent Computing Theories and Technology, 2013
2012
Assessing and predicting protein interactions by combining manifold embedding with multiple information integration.
BMC Bioinform., 2012
Proceedings of the Advances in Swarm Intelligence - Third International Conference, 2012
2010
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.
BMC Bioinform., 2010
Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data.
Bioinform., 2010
Proceedings of the Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 2010
Increasing Reliability of Protein Interactome by Combining Heterogeneous Data Sources with Weighted Network Topological Metrics.
Proceedings of the Advanced Intelligent Computing Theories and Applications, 2010
2009
Integration of Genomic and Proteomic Data to Predict Synthetic Genetic Interactions Using Semi-supervised Learning.
Proceedings of the Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, 2009
2008
Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008
Proceedings of the Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 2008
2007
Int. J. Inf. Acquis., 2007
Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2007