H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 36 Citations 4,673 107 World Ranking 5503 National Ranking 522

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Gene
  • Algorithm

His primary areas of investigation include Disease, Artificial intelligence, microRNA, Machine learning and Data mining. His studies in Disease integrate themes in fields like Systems biology, Similarity computation and Data science. The study incorporates disciplines such as Biological network, Membrane computing and Drug discovery in addition to Artificial intelligence.

His work deals with themes such as Similarity, Computational biology and Biological database, which intersect with microRNA. His Similarity research integrates issues from Disease gene and Identification. His work on Support vector machine, Autoencoder and Deep learning as part of general Machine learning research is often related to Repurposing and Drug repositioning, thus linking different fields of science.

His most cited work include:

  • Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks (233 citations)
  • Inferring MicroRNA-Disease Associations by Random Walk on a Heterogeneous Network with Multiple Data Sources (192 citations)
  • A comprehensive overview and evaluation of circular RNA detection tools. (178 citations)

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

Xiangxiang Zeng spends much of his time researching Artificial intelligence, Machine learning, Theoretical computer science, Algorithm and Computational biology. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Pattern recognition and Identification. His biological study spans a wide range of topics, including Representation and Biological network.

His Theoretical computer science research includes elements of P system and Turing machine. His work on Computation as part of general Algorithm research is frequently linked to Spike, bridging the gap between disciplines. Xiangxiang Zeng works mostly in the field of Computational biology, limiting it down to topics relating to Disease and, in certain cases, Similarity, as a part of the same area of interest.

He most often published in these fields:

  • Artificial intelligence (32.69%)
  • Machine learning (21.79%)
  • Theoretical computer science (18.59%)

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

  • Artificial intelligence (32.69%)
  • Machine learning (21.79%)
  • Deep learning (7.69%)

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

Xiangxiang Zeng focuses on Artificial intelligence, Machine learning, Deep learning, Artificial neural network and Representation. His work deals with themes such as Enhancer and Identification, which intersect with Artificial intelligence. The Machine learning study combines topics in areas such as Biological network, Community structure, Maximization and Benchmark.

His Benchmark research incorporates elements of Learning methods and Disease. He has researched Deep learning in several fields, including Convolutional neural network, Data mining and Hierarchical network model. His Artificial neural network research incorporates themes from Graph, Membrane computing and Open problem.

Between 2019 and 2021, his most popular works were:

  • Target identification among known drugs by deep learning from heterogeneous networks (39 citations)
  • Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods. (38 citations)
  • Computational methods for identifying the critical nodes in biological networks. (30 citations)

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

  • Artificial intelligence
  • Gene
  • Algorithm

Xiangxiang Zeng mainly investigates Artificial intelligence, Machine learning, Distributed computing, Biological network and Disease. His Artificial intelligence study frequently draws parallels with other fields, such as Identification. His Identification research is multidisciplinary, relying on both Deep learning, Computational biology and Small molecule.

His studies in Machine learning integrate themes in fields like Community structure and Maximization. His biological study spans a wide range of topics, including Optimization problem and State. Xiangxiang Zeng combines subjects such as Learning methods and Benchmark with his study of Disease.

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

Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks

Xiangxiang Zeng;Xuan Zhang;Quan Zou.
Briefings in Bioinformatics (2016)

290 Citations

Inferring MicroRNA-Disease Associations by Random Walk on a Heterogeneous Network with Multiple Data Sources

Yuansheng Liu;Xiangxiang Zeng;Zengyou He;Quan Zou.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017)

237 Citations

Similarity computation strategies in the microRNA-disease network: a survey

Quan Zou;Jinjin Li;Li Song;Xiangxiang Zeng.
Briefings in Functional Genomics (2015)

224 Citations

A comprehensive overview and evaluation of circular RNA detection tools.

Xiangxiang Zeng;Wei Lin;Maozu Guo;Quan Zou.
PLOS Computational Biology (2017)

224 Citations

Prediction and Validation of Disease Genes Using HeteSim Scores

Xiangxiang Zeng;Yuanlu Liao;Yuansheng Liu;Quan Zou.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017)

176 Citations

nDNA-prot: Identification of DNA-binding proteins based on unbalanced classification

Li Song;Dapeng Li;Xiangxiang Zeng;Yunfeng Wu.
BMC Bioinformatics (2014)

167 Citations

Prediction of potential disease-associated microRNAs using structural perturbation method.

Xiangxiang Zeng;Xiangxiang Zeng;Li Liu;Linyuan Lü;Linyuan Lü;Quan Zou.
Bioinformatics (2018)

146 Citations

Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.

Quan Zou;Shixiang Wan;Shixiang Wan;Ying Ju;Jijun Tang;Jijun Tang.
BMC Systems Biology (2016)

143 Citations

Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources

Tseren-Onolt Ishdorj;Alberto Leporati;Linqiang Pan;Xiangxiang Zeng.
Theoretical Computer Science (2010)

142 Citations

deepDR: a network-based deep learning approach to in silico drug repositioning.

Xiangxiang Zeng;Siyi Zhu;Xiangrong Liu;Yadi Zhou.
Bioinformatics (2019)

126 Citations

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Best Scientists Citing Xiangxiang Zeng

Quan Zou

Quan Zou

University of Electronic Science and Technology of China

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