What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Machine learning
Sungroh Yoon focuses on Artificial intelligence, Machine learning, Deep learning, Data mining and microRNA.
His work carried out in the field of Artificial intelligence brings together such families of science as Computer security and Heartbeat.
His Machine learning research is multidisciplinary, relying on both Language model, Pipeline, Online community, World Wide Web and Nucleic acid secondary structure.
Sungroh Yoon has researched Deep learning in several fields, including Recurrent neural network, Convolutional neural network, Bioinformatics and Indel.
His Data mining research incorporates elements of Noise reduction, Key, Nucleotide and DNA sequencing.
His research integrates issues of Domain, Guide RNA, Categorization, CRISPR/Cpf1 and Big data in his study of Artificial neural network.
His most cited work include:
- Deep learning in bioinformatics (617 citations)
- RNA design rules from a massive open laboratory (174 citations)
- Prediction of regulatory modules comprising microRNAs and target genes (144 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Deep learning.
His research in Convolutional neural network, Inference, Classifier, Recurrent neural network and Spiking neural network are components of Artificial intelligence.
His Machine learning study combines topics in areas such as Adversarial system, Feature extraction and microRNA.
His work in Adversarial system is not limited to one particular discipline; it also encompasses Generative grammar.
The study incorporates disciplines such as Contextual image classification, Image, Object detection and Feature in addition to Pattern recognition.
His research links Efficient energy use with Artificial neural network.
He most often published in these fields:
- Artificial intelligence (44.27%)
- Machine learning (20.43%)
- Pattern recognition (15.48%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (44.27%)
- Artificial neural network (16.72%)
- Pattern recognition (15.48%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Artificial neural network, Pattern recognition, Deep learning and Machine learning.
Artificial intelligence is closely attributed to Natural language processing in his study.
The Artificial neural network study combines topics in areas such as Object detection, Inference, Data mining and Robustness.
His Pattern recognition study integrates concerns from other disciplines, such as Pixel, Regularization, Blood pressure, Interpretability and Image.
The concepts of his Deep learning study are interwoven with issues in Visualization, Anomaly detection, Theoretical computer science and Graph similarity.
His study in the field of Convolutional neural network is also linked to topics like Chemical property.
Between 2019 and 2021, his most popular works were:
- Predicting the efficiency of prime editing guide RNAs in human cells. (27 citations)
- High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells. (25 citations)
- Prediction of the sequence-specific cleavage activity of Cas9 variants (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Machine learning
His main research concerns Artificial intelligence, Cas9, Computational biology, Artificial neural network and Pattern recognition.
His work on Deep learning and Sentence as part of his general Artificial intelligence study is frequently connected to Property, thereby bridging the divide between different branches of science.
His research in Cas9 intersects with topics in Timer, Biological system and Indel.
Sungroh Yoon has included themes like Genome editing and Genetic Change in his Computational biology study.
His Artificial neural network study contributes to a more complete understanding of Machine learning.
Sungroh Yoon combines subjects such as Deconvolution, Contextual image classification, Image, Binary decision diagram and Spiking neural network with his study of Pattern recognition.
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