2022 - Research.com Rising Star of Science Award
Leyi Wei mainly investigates Artificial intelligence, Machine learning, Data mining, Pattern recognition and Feature selection. Leyi Wei mostly deals with Upsampling in his studies of Artificial intelligence. As part of the same scientific family, Leyi Wei usually focuses on Machine learning, concentrating on Identification and intersecting with Computational biology.
His work carried out in the field of Data mining brings together such families of science as Binary classification, Divide and conquer algorithms, Robustness and Benchmark. In his study, Feature, Cross-validation and Field is inextricably linked to Deep learning, which falls within the broad field of Pattern recognition. His study focuses on the intersection of Feature selection and fields such as Discriminative model with connections in the field of Feature learning, Feature vector, Representation and Feature.
His primary scientific interests are in Artificial intelligence, Machine learning, Feature, Identification and Discriminative model. His Pattern recognition research extends to Artificial intelligence, which is thematically connected. His Machine learning research is multidisciplinary, incorporating perspectives in Data mining and Benchmark.
His work in Data mining covers topics such as Classifier which are related to areas like Feature extraction. In his research, Bioinformatics is intimately related to Feature vector, which falls under the overarching field of Feature. As a part of the same scientific study, Leyi Wei usually deals with the Identification, concentrating on Computational biology and frequently concerns with Ensemble learning.
The scientist’s investigation covers issues in Artificial intelligence, Feature, Discriminative model, Machine learning and Pattern recognition. Leyi Wei integrates Artificial intelligence and Correlation in his research. His research integrates issues of Image, Representation and Feature selection in his study of Feature.
His Representation research incorporates themes from Feature vector and Identification. His Discriminative model study incorporates themes from Inference, Feature learning, Support vector machine and Evolutionary information. His work on Segmentation as part of general Pattern recognition study is frequently linked to Generalization, Chemistry and Subcellular localization, bridging the gap between disciplines.
Leyi Wei focuses on Artificial intelligence, Web server, Context, Feature and Identification. The Artificial intelligence study combines topics in areas such as Predictive modelling and Machine learning. His work in the fields of Machine learning, such as Regression analysis, intersects with other areas such as Mechanism, Sensitivity and Genomics.
His Web server studies intersect with other disciplines such as Research community, Mixture model, Clinical therapy, Web application and Cell-penetrating peptide. The various areas that Leyi Wei examines in his Feature study include Decision problem, Data mining and Feature selection. He has included themes like Representation, Probabilistic logic, Information visualization and Pattern recognition in his Identification study.
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DUNet: A deformable network for retinal vessel segmentation
Qiangguo Jin;Zhaopeng Meng;Zhaopeng Meng;Tuan D. Pham;Qi Chen.
Knowledge Based Systems (2019)
Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA.
Quan Zou;Quan Zou;Pengwei Xing;Leyi Wei;Bin Liu.
ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.
Leyi Wei;Chen Zhou;Huangrong Chen;Jiangning Song.
Improved and promising identification of human MicroRNAs by incorporating a high-quality negative set
Leyi Wei;Minghong Liao;Yue Gao;Rongrong Ji.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2014)
Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information
Leyi Wei;Jijun Tang;Jijun Tang;Quan Zou.
Information Sciences (2017)
Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.
Leyi Wei;Pengwei Xing;Jiancang Zeng;JinXiu Chen.
Artificial Intelligence in Medicine (2017)
Prediction of human protein subcellular localization using deep learning
Leyi Wei;Leyi Wei;Yijie Ding;Ran Su;Ran Su;Jijun Tang.
Journal of Parallel and Distributed Computing (2017)
Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique
Leyi Wei;Pengwei Xing;Gaotao Shi;Zhiliang Ji.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019)
A novel hierarchical selective ensemble classifier with bioinformatics application
Leyi Wei;Shixiang Wan;Jiasheng Guo;Kelvin Kl Wong.
Artificial Intelligence in Medicine (2017)
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation.
Balachandran Manavalan;Shaherin Basith;Tae Hwan Shin;Leyi Wei.
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