What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Enzyme
- Gene
His main research concerns Molecular dynamics, Crystallography, Normal mode, Nanotechnology and ATPase.
The Molecular dynamics study combines topics in areas such as Chemical physics, Nanotube membrane, Conformational change and Physiological condition.
His Crystallography research is multidisciplinary, relying on both Protein structure, Biophysics, Bilayer and Protein folding.
The Carbon nanotube research Jianpeng Ma does as part of his general Nanotechnology study is frequently linked to other disciplines of science, such as Extramural and Protonation, therefore creating a link between diverse domains of science.
In general Carbon nanotube, his work in Optical properties of carbon nanotubes is often linked to Micelle linking many areas of study.
His ATPase research includes elements of Molecular motor, Stereochemistry, ATP synthase and Nucleotide.
His most cited work include:
- CHARMM: the biomolecular simulation program. (4881 citations)
- Band gap fluorescence from individual single-walled carbon nanotubes. (2981 citations)
- Band gap fluorescence from individual single-walled carbon nanotubes. (2981 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Crystallography, Protein structure, Normal mode, Molecular dynamics and Algorithm.
The various areas that Jianpeng Ma examines in his Crystallography study include Biophysics, Cryo-electron microscopy, Lipid bilayer fusion and Protein folding.
His Protein structure study also includes fields such as
- Transmembrane domain which is related to area like ATP-binding cassette transporter,
- Biological system together with Computational chemistry and Feature.
His study looks at the relationship between Normal mode and fields such as Resolution, as well as how they intersect with chemical problems.
His work is dedicated to discovering how Molecular dynamics, Stereochemistry are connected with Molecular motor, Protein subunit and Binding site and other disciplines.
His research investigates the link between Algorithm and topics such as Protein structure prediction that cross with problems in Dihedral angle.
He most often published in these fields:
- Crystallography (48.19%)
- Protein structure (36.79%)
- Normal mode (29.53%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (33.68%)
- Protein structure prediction (20.21%)
- Protein folding (20.73%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, Protein structure prediction, Protein folding, Protein structure and Dihedral angle.
He has included themes like Artificial neural network, Structure and Protein secondary structure in his Algorithm study.
The study incorporates disciplines such as Recurrent neural network and Deep learning, Inference, Artificial intelligence in addition to Protein secondary structure.
Jianpeng Ma connects Protein folding with Folding in his research.
His research links Model selection with Protein structure.
His biological study deals with issues like Correctness, which deal with fields such as Function.
Between 2017 and 2021, his most popular works were:
- Atomistic simulations indicate the functional loop-to-coiled-coil transition in influenza hemagglutinin is not downhill (7 citations)
- Atomistic simulations indicate the functional loop-to-coiled-coil transition in influenza hemagglutinin is not downhill (7 citations)
- Three classes of response elements for human PRC2 and MLL1/2-Trithorax complexes. (6 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Enzyme
- Gene
His primary scientific interests are in Protein structure prediction, Algorithm, Artificial neural network, Inference and Deep learning.
His work carried out in the field of Protein structure prediction brings together such families of science as Geometry and A protein.
The Algorithm study combines topics in areas such as Function, Protein structure modeling and Protein Data Bank.
Jianpeng Ma interconnects Protein structure and Protein secondary structure in the investigation of issues within Artificial neural network.
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