What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Statistics
His primary areas of study are Artificial intelligence, Data mining, Machine learning, Genome and Pattern recognition.
His study in Random forest and Feature is carried out as part of his Artificial intelligence studies.
As a member of one scientific family, Jijun Tang mostly works in the field of Data mining, focusing on Support vector machine and, on occasion, Fingerprint.
His work carried out in the field of Machine learning brings together such families of science as Identification, Information integration and Benchmark.
His biological study spans a wide range of topics, including Organism and Computational biology.
His Convolutional neural network study in the realm of Pattern recognition interacts with subjects such as Data resolution.
His most cited work include:
- Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information (132 citations)
- Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information (132 citations)
- Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy. (117 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Genome, Computational biology, Algorithm and Pattern recognition.
His Artificial intelligence study frequently draws connections between adjacent fields such as Machine learning.
The Machine learning study combines topics in areas such as Field and Data mining, Identification.
Jijun Tang combines subjects such as Evolutionary biology, Phylogenetics and Inference with his study of Genome.
His Computational biology research integrates issues from Ancestral reconstruction, DNA methylation, Spectral clustering, DNA microarray and DNA sequencing.
His Pattern recognition study integrates concerns from other disciplines, such as Cross-validation, Filter and Kernel fusion.
He most often published in these fields:
- Artificial intelligence (49.37%)
- Genome (32.91%)
- Computational biology (37.97%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (49.37%)
- Computational biology (37.97%)
- Multiple kernel learning (15.61%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computational biology, Multiple kernel learning, Pattern recognition and Benchmark.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Identification.
His study looks at the relationship between Machine learning and topics such as Field, which overlap with Artificial neural network.
His studies deal with areas such as Cancer, Spectral clustering, Proteomics, Whole genome sequencing and CpG site as well as Computational biology.
Jijun Tang interconnects Protein structure, Probability distribution and Multivariate statistics in the investigation of issues within Pattern recognition.
He focuses mostly in the field of Benchmark, narrowing it down to matters related to ENCODE and, in some cases, Feature selection and Extrapolation.
Between 2018 and 2021, his most popular works were:
- Identification of drug-side effect association via multiple information integration with centered kernel alignment (76 citations)
- Identification of drug-side effect association via multiple information integration with centered kernel alignment (76 citations)
- Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC (51 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Statistics
Artificial intelligence, Multiple kernel learning, Benchmark, Machine learning and Kernel are his primary areas of study.
His work in Artificial intelligence covers topics such as Pattern recognition which are related to areas like Multivariate statistics.
Jijun Tang has included themes like Gene expression and Epigenetics in his Benchmark study.
His work on Multi-label classification as part of general Machine learning research is frequently linked to Bipartite graph, thereby connecting diverse disciplines of science.
His Kernel research also works with subjects such as
- Extrapolation and ENCODE most often made with reference to Precision and recall,
- Similarity which intersects with area such as Survival analysis, Spectral clustering and Cancer.
His study in Support vector machine is interdisciplinary in nature, drawing from both Histogram, Subcellular localization and Evolutionary information.
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