What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Gene
Zhu-Hong You focuses on Artificial intelligence, Machine learning, Support vector machine, Cross-validation and Computational biology.
His study ties his expertise on Pattern recognition together with the subject of Artificial intelligence.
His Machine learning research includes themes of Drug target and Data mining.
The concepts of his Support vector machine study are interwoven with issues in Classifier, Topology, Protein–protein interaction, Protein sequencing and Gene regulatory network.
His Cross-validation research incorporates elements of Colon neoplasm, Similarity, Bioinformatics and Disease Association.
Biological network is closely connected to Disease in his research, which is encompassed under the umbrella topic of Computational biology.
His most cited work include:
- Long non-coding RNAs and complex diseases: from experimental results to computational models (296 citations)
- MicroRNAs and complex diseases: from experimental results to computational models. (214 citations)
- Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis (204 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Classifier, Support vector machine and Computational biology.
His Artificial intelligence research incorporates themes from Protein sequencing, Protein–protein interaction and Pattern recognition.
His Machine learning research is multidisciplinary, relying on both Graph and Representation.
His research in Classifier focuses on subjects like Drug target, which are connected to Enzyme.
His Support vector machine research includes themes of Autoencoder, Bioinformatics and Feature vector.
His Computational biology research incorporates elements of microRNA, Gene, Disease and Cross-validation.
He most often published in these fields:
- Artificial intelligence (62.86%)
- Machine learning (39.59%)
- Classifier (29.39%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (62.86%)
- Machine learning (39.59%)
- Random forest (11.02%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Random forest, Computational biology and Disease.
His biological study spans a wide range of topics, including Drug target and Pattern recognition.
His study in Machine learning is interdisciplinary in nature, drawing from both Graph, Semantic similarity and Drug discovery.
His Random forest study incorporates themes from Node, Network embedding, Representation and Computational model.
His research investigates the connection with Computational biology and areas like microRNA which intersect with concerns in Disease Association and Colon neoplasm.
He focuses mostly in the field of Disease, narrowing it down to topics relating to Embedding and, in certain cases, Data mining.
Between 2019 and 2021, his most popular works were:
- Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions (20 citations)
- An efficient approach based on multi-sources information to predict circRNA–disease associations using deep convolutional neural network (13 citations)
- iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation. (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Gene
Zhu-Hong You spends much of his time researching Artificial intelligence, Machine learning, Computational biology, Classifier and Disease.
With his scientific publications, his incorporates both Artificial intelligence and Stacking.
His Kernel study, which is part of a larger body of work in Machine learning, is frequently linked to Reductionism, bridging the gap between disciplines.
His Computational biology research is multidisciplinary, incorporating perspectives in Random forest and microRNA.
His biological study deals with issues like Semantic similarity, which deal with fields such as Autoencoder and Support vector machine.
His work deals with themes such as Circular RNA, Graph embedding and Source code, which intersect with Disease.
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