What is he best known for?
The fields of study he is best known for:
- Gene
- Artificial intelligence
- DNA
Jianlin Cheng spends much of his time researching Artificial intelligence, Protein structure prediction, Data mining, Artificial neural network and Protein structure.
His Artificial intelligence study frequently draws connections between related disciplines such as Machine learning.
He works mostly in the field of Machine learning, limiting it down to topics relating to Protein function prediction and, in certain cases, Computational biology and Genome.
Jianlin Cheng combines subjects such as Deep learning, Protein folding and Pattern recognition with his study of Protein structure prediction.
His research in Artificial neural network tackles topics such as Protein contact map which are related to areas like Convolutional neural network.
His work carried out in the field of Protein structure brings together such families of science as Server, Protein secondary structure and Bioinformatics.
His most cited work include:
- Genome sequence of the palaeopolyploid soybean (2910 citations)
- SCRATCH: a protein structure and structural feature prediction server (694 citations)
- A large-scale evaluation of computational protein function prediction (624 citations)
What are the main themes of his work throughout his whole career to date?
Jianlin Cheng mostly deals with Artificial intelligence, Protein structure prediction, Deep learning, Data mining and Machine learning.
Many of his research projects under Artificial intelligence are closely connected to Source code with Source code, tying the diverse disciplines of science together.
His Protein structure prediction research includes themes of Protein tertiary structure and Protein folding.
In the subject of general Machine learning, his work in Ranking and Collaborative filtering is often linked to Mechanism and Set, thereby combining diverse domains of study.
The various areas that he examines in his Protein structure study include Biological system, Server, Bioinformatics and Computational biology.
As a part of the same scientific family, Jianlin Cheng mostly works in the field of Bioinformatics, focusing on Gene regulatory network and, on occasion, Genome and Genomics.
He most often published in these fields:
- Artificial intelligence (41.60%)
- Protein structure prediction (34.45%)
- Deep learning (24.37%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (41.60%)
- Deep learning (24.37%)
- Protein structure prediction (34.45%)
In recent papers he was focusing on the following fields of study:
Jianlin Cheng focuses on Artificial intelligence, Deep learning, Protein structure prediction, Machine learning and Artificial neural network.
He interconnects CASP and Pattern recognition in the investigation of issues within Artificial intelligence.
His research in Deep learning intersects with topics in Algorithm, Residual, Inference and Protein secondary structure.
His Text mining research extends to Protein structure prediction, which is thematically connected.
Protein Data Bank is closely connected to Structure in his research, which is encompassed under the umbrella topic of Machine learning.
His Convolutional neural network research focuses on Protein structure and how it relates to Proteome, Nucleosome assembly and Protein folding.
Between 2019 and 2021, his most popular works were:
- Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps (27 citations)
- Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis. (13 citations)
- Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. (7 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Artificial intelligence
- DNA
His primary areas of study are Artificial intelligence, Deep learning, Protein structure prediction, Algorithm and Data mining.
His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Protein tertiary structure.
His studies deal with areas such as Protein structure, Artificial neural network and Convolutional neural network as well as Deep learning.
His Protein structure prediction research is multidisciplinary, relying on both A protein and Protein secondary structure.
His research integrates issues of Distance transform and Average mean square error in his study of Algorithm.
His Data mining study frequently intersects with other fields, such as Protein Data Bank.
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