The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Feature vector and Deep learning. Xin Gao applies his multidisciplinary studies on Artificial intelligence and Field in his research. When carried out as part of a general Pattern recognition research project, his work on k-nearest neighbors algorithm is frequently linked to work in Vocabulary, therefore connecting diverse disciplines of study.
His studies deal with areas such as Segmentation and Image segmentation as well as Machine learning. His work carried out in the field of Deep learning brings together such families of science as Metagenomics, Molecular Sequence Annotation and Enzyme Commission number, Enzyme. His Graph research is multidisciplinary, incorporating elements of Graph theory and Sparse approximation, K-SVD.
His main research concerns Artificial intelligence, Algorithm, Computational biology, Pattern recognition and Machine learning. His Artificial intelligence research incorporates elements of Data mining and Natural language processing. Xin Gao has researched Natural language processing in several fields, including Annotation and Ontology.
His research in Algorithm intersects with topics in Protein structure, Nanopore sequencing and Benchmark. Computational biology is closely attributed to Genome in his work. His Pattern recognition study combines topics from a wide range of disciplines, such as Graph, Iterative method and Non-negative matrix factorization.
Xin Gao mainly focuses on Artificial intelligence, Deep learning, Computational biology, Artificial neural network and Algorithm. His Artificial intelligence study incorporates themes from Natural language processing, Machine learning and Pattern recognition. The various areas that he examines in his Pattern recognition study include Breast cancer and Data set.
His studies in Deep learning integrate themes in fields like Transfer of learning, Visualization, Key and Inference. His Computational biology study also includes
Xin Gao mainly investigates Artificial intelligence, Deep learning, Artificial neural network, Algorithm and Machine learning. His work on Biological database expands to the thematically related Artificial intelligence. The concepts of his Deep learning study are interwoven with issues in Component, World Wide Web, MEDLINE and Flash evaporation.
His Artificial neural network research incorporates themes from Ground truth and Neuroimaging. In his research, Programming algorithm and RNA is intimately related to Inference, which falls under the overarching field of Algorithm. His work in the fields of Transfer of learning overlaps with other areas such as Fully automated.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
SARS-CoV-2 induced diarrhoea as onset symptom in patient with COVID-19.
Y Song;P Liu;X L Shi;Y L Chu.
Bioinformatics clouds for big data manipulation.
Lin Dai;Xin Gao;Yan Guo;Jingfa Xiao.
Biology Direct (2012)
Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.
Yu Li;Chao Huang;Lizhong Ding;Zhongxiao Li.
DEEPre: sequence-based enzyme EC number prediction by deep learning
Yu Li;Sheng Wang;Ramzan Umarov;Bingqing Xie.
Non-negative matrix factorization by maximizing correntropy for cancer clustering
Jim Jing-Yan Wang;Xiaolei Wang;Xin Gao.
BMC Bioinformatics (2013)
Hypoxia promotes glioma-associated macrophage infiltration via periostin and subsequent M2 polarization by upregulating TGF-beta and M-CSFR.
Guo X;Xue H;Shao Q;Wang J;Wang J.
Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis
Abdul-Hamid M. Emwas;Raja Roy;Ryan T. McKay;Danielle Ryan.
Journal of Proteome Research (2016)
Multiple graph regularized nonnegative matrix factorization
Jim Jing-Yan Wang;Halima Bensmail;Xin Gao.
Pattern Recognition (2013)
Learning from Weak and Noisy Labels for Semantic Segmentation
Zhiwu Lu;Zhenyong Fu;Tao Xiang;Peng Han.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis
Longxi Zhou;Zhongxiao Li;Juexiao Zhou;Haoyang Li.
IEEE Transactions on Medical Imaging (2020)
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