H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,355 160 World Ranking 8232 National Ranking 21

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Gene
  • Machine learning

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 most cited work include:

  • Bioinformatics clouds for big data manipulation. (126 citations)
  • Non-negative matrix factorization by maximizing correntropy for cancer clustering (102 citations)
  • DEEPre: sequence-based enzyme EC number prediction by deep learning (95 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (37.27%)
  • Algorithm (14.55%)
  • Computational biology (13.94%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (37.27%)
  • Deep learning (12.73%)
  • Computational biology (13.94%)

In recent papers he was focusing on the following fields of study:

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

  • Genome which connect with Graph,
  • Cell and related Cell fate determination and Drug discovery. His Algorithm research is multidisciplinary, incorporating perspectives in Electrical current and Nanopore sequencing.

Between 2019 and 2021, his most popular works were:

  • A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis (31 citations)
  • Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets (17 citations)
  • Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data (14 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Gene
  • Machine learning

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.

Best Publications

SARS-CoV-2 induced diarrhoea as onset symptom in patient with COVID-19.

Y Song;P Liu;X L Shi;Y L Chu.
Gut (2020)

238 Citations

Bioinformatics clouds for big data manipulation.

Lin Dai;Xin Gao;Yan Guo;Jingfa Xiao.
Biology Direct (2012)

180 Citations

DEEPre: sequence-based enzyme EC number prediction by deep learning

Yu Li;Sheng Wang;Ramzan Umarov;Bingqing Xie.
Bioinformatics (2018)

146 Citations

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.

Yu Li;Chao Huang;Lizhong Ding;Zhongxiao Li.
Methods (2019)

140 Citations

Non-negative matrix factorization by maximizing correntropy for cancer clustering

Jim Jing-Yan Wang;Xiaolei Wang;Xin Gao.
BMC Bioinformatics (2013)

130 Citations

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.
Oncotarget (2016)

124 Citations

Multiple graph regularized nonnegative matrix factorization

Jim Jing-Yan Wang;Halima Bensmail;Xin Gao.
Pattern Recognition (2013)

115 Citations

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)

104 Citations

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)

103 Citations

Notice of Violation of IEEE Publication Principles Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting

Jingyan Wang;Yongping Li;Ying Zhang;Chao Wang.
IEEE Transactions on Medical Imaging (2011)

89 Citations

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