What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Machine learning
His primary areas of investigation include Computational biology, Pseudo amino acid composition, Bioinformatics, Classifier and Subcellular localization.
His studies deal with areas such as Chromatin, Biochemistry, Bacterial protein and Nuclear pore as well as Computational biology.
The concepts of his Pseudo amino acid composition study are interwoven with issues in Proteomics, Membrane protein and Protein methods.
His work in Bioinformatics tackles topics such as Peptide sequence which are related to areas like Cleavage.
Hong-Bin Shen has included themes like Protein structure, Drug discovery and Homology in his Classifier study.
While the research belongs to areas of Protein structure, Hong-Bin Shen spends his time largely on the problem of Protein secondary structure, intersecting his research to questions surrounding Machine learning and Artificial intelligence.
His most cited work include:
- Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. (679 citations)
- Recent progress in protein subcellular location prediction (669 citations)
- Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization (429 citations)
What are the main themes of his work throughout his whole career to date?
Hong-Bin Shen mainly investigates Artificial intelligence, Computational biology, Pattern recognition, Machine learning and Data mining.
As part of the same scientific family, Hong-Bin Shen usually focuses on Artificial intelligence, concentrating on Binding site and intersecting with Protein secondary structure, DNA microarray and Protein ligand.
His work deals with themes such as Bioinformatics, Classifier, Pseudo amino acid composition, Peptide sequence and Subcellular localization, which intersect with Computational biology.
His study in Pseudo amino acid composition is interdisciplinary in nature, drawing from both Proteomics, Protein sequencing and Membrane protein.
His Pattern recognition study integrates concerns from other disciplines, such as Artificial neural network, Feature and Feature.
His Machine learning research includes elements of Algorithm and Benchmark.
He most often published in these fields:
- Artificial intelligence (57.21%)
- Computational biology (40.17%)
- Pattern recognition (29.26%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (57.21%)
- Computational biology (40.17%)
- Deep learning (11.35%)
In recent papers he was focusing on the following fields of study:
Hong-Bin Shen focuses on Artificial intelligence, Computational biology, Deep learning, Pattern recognition and Convolutional neural network.
His work on Feature extraction, Image processing and Cluster analysis as part of general Artificial intelligence study is frequently linked to Expression and Set, therefore connecting diverse disciplines of science.
His Computational biology research integrates issues from Protein secondary structure, Peptide sequence, microRNA and Subcellular localization, Protein subcellular location.
His Peptide sequence research is multidisciplinary, incorporating elements of Accessible surface area and Transmembrane domain.
His Deep learning study incorporates themes from Recurrent neural network, RNA, RNA-binding protein, Rna protein and Regulation of gene expression.
His work carried out in the field of Pattern recognition brings together such families of science as Image quality, Feature, Artificial neural network, Structure and Robustness.
Between 2017 and 2021, his most popular works were:
- The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier. (108 citations)
- Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. (91 citations)
- Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks. (75 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Machine learning
Hong-Bin Shen mostly deals with Computational biology, Artificial intelligence, Deep learning, Subcellular localization and Convolutional neural network.
Hong-Bin Shen combines Computational biology and Protein translocation in his studies.
He combines subjects such as RNA, RNA-binding protein, Regulation of gene expression and Binding site with his study of Artificial intelligence.
The Regulation of gene expression study combines topics in areas such as Artificial neural network, Inference, Systems biology and Pattern recognition.
His Subcellular localization study combines topics in areas such as Classification methods, Field, Data mining and Bioimage informatics.
His research in Convolutional neural network intersects with topics in Feature, Encoding and Rna protein.
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