What is he best known for?
The fields of study he is best known for:
His scientific interests lie mostly in Peptide sequence, Sequence analysis, Computational biology, Biochemistry and Protein sequencing.
His Peptide sequence study incorporates themes from Proteases, Cysteine and Matthews correlation coefficient.
His work deals with themes such as RNA splicing, Proteome, Autoimmunity and Data mining, which intersect with Computational biology.
The Protein sequencing study combines topics in areas such as Protein structure and Conserved sequence.
He has included themes like Biological system, Support vector machine, Protein secondary structure and Protein folding in his Protein structure study.
In Support vector machine, Jiangning Song works on issues like Feature selection, which are connected to Feature vector.
His most cited work include:
- PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites (187 citations)
- PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites (187 citations)
- iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. (172 citations)
What are the main themes of his work throughout his whole career to date?
Jiangning Song spends much of his time researching Computational biology, Artificial intelligence, Biochemistry, Protein structure and Bioinformatics.
His research integrates issues of Sequence analysis, Proteome, Protein sequencing and Support vector machine in his study of Computational biology.
His studies in Support vector machine integrate themes in fields like Genetics, Binding site, Ensemble forecasting and Encoding.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with his field of interest in Identification.
His Protein structure study integrates concerns from other disciplines, such as Protease, Target protein, Biological system and Protein secondary structure.
He combines subjects such as Proteases, Cleavage, In silico and Protein folding with his study of Peptide sequence.
He most often published in these fields:
- Computational biology (39.70%)
- Artificial intelligence (25.84%)
- Biochemistry (28.09%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (25.84%)
- Machine learning (15.73%)
- Computational biology (39.70%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Computational biology, Deep learning and Feature.
His Artificial intelligence study frequently draws connections to other fields, such as Identification.
In the subject of general Machine learning, his work in Support vector machine, Feature engineering, Ensemble learning and Dimensionality reduction is often linked to Set, thereby combining diverse domains of study.
His study in Computational biology is interdisciplinary in nature, drawing from both Cleavage, Function, Pathogenic organism, Human leukocyte antigen and Sequence analysis.
His work in Feature selection addresses issues such as Naive Bayes classifier, which are connected to fields such as Random forest.
Jiangning Song works mostly in the field of Protein secondary structure, limiting it down to topics relating to Intrinsically disordered proteins and, in certain cases, Biological system, as a part of the same area of interest.
Between 2019 and 2021, his most popular works were:
- iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data (83 citations)
- A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (42 citations)
- DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites (35 citations)
In his most recent research, the most cited papers focused on:
His primary scientific interests are in Computational biology, Artificial intelligence, Feature selection, Machine learning and Feature.
His Computational biology study combines topics in areas such as Cleave, Cleavage, Proteases, Proteolysis and Sequence analysis.
The concepts of his Cleave study are interwoven with issues in Proteome, Protein secondary structure, Protease, Peptide bond and Protein structure.
His Feature selection research includes themes of Model interpretation and Regulation of gene expression.
Within one scientific family, he focuses on topics pertaining to Encoding under Machine learning, and may sometimes address concerns connected to Artificial neural network, Robustness, Naive Bayes classifier, Support vector machine and Random forest.
His studies deal with areas such as Representation, Scale and Identification as well as Feature.
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