What is he best known for?
The fields of study he is best known for:
- Gene
- Enzyme
- Artificial intelligence
Andreas Bender mostly deals with Computational biology, Artificial intelligence, Virtual screening, Similarity and Data mining.
His Computational biology research incorporates elements of Nanotechnology, Chemical database, Bioinformatics, Drug discovery and Small molecule.
His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His studies deal with areas such as Principal component analysis, Similarity measure, ADME and Protein target as well as Virtual screening.
His work in Similarity addresses issues such as Molecular descriptor, which are connected to fields such as Euclidean distance measure, Simple Features and Margin.
Andreas Bender has included themes like Chemical space and Data set in his Data mining study.
His most cited work include:
- Molecular similarity: a key technique in molecular informatics. (385 citations)
- SPT-3G: A Next-Generation Cosmic Microwave Background Polarization Experiment on the South Pole Telescope (278 citations)
- Integrating high-content screening and ligand-target prediction to identify mechanism of action (273 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Computational biology, Artificial intelligence, Data mining, Drug discovery and In silico.
His research in Computational biology tackles topics such as Bioinformatics which are related to areas like Drug.
Andreas Bender interconnects Machine learning, Virtual screening and Pattern recognition in the investigation of issues within Artificial intelligence.
His Virtual screening study integrates concerns from other disciplines, such as Pharmacophore and Similarity.
His Data mining research integrates issues from Applicability domain and Cheminformatics.
His In silico study combines topics from a wide range of disciplines, such as Mode of action, Pharmacology and Small molecule.
He most often published in these fields:
- Computational biology (25.14%)
- Artificial intelligence (21.27%)
- Data mining (17.40%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (21.27%)
- Computational biology (25.14%)
- Random forest (5.80%)
In recent papers he was focusing on the following fields of study:
Andreas Bender mainly investigates Artificial intelligence, Computational biology, Random forest, Drug discovery and Drug.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and chEMBL.
Andreas Bender studied Machine learning and Virtual screening that intersect with Structure–activity relationship.
The various areas that Andreas Bender examines in his Computational biology study include Precision medicine, Gene expression, Cheminformatics and Genomics.
His research in Random forest intersects with topics in Interpretability, Data mining, Test set and Pattern recognition.
The concepts of his Drug discovery study are interwoven with issues in Illusion and Field.
Between 2017 and 2021, his most popular works were:
- DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. (124 citations)
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks (33 citations)
- Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data. (32 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Enzyme
- Artificial intelligence
His primary areas of investigation include Artificial intelligence, Random forest, Machine learning, chEMBL and Drug discovery.
His study on Deep learning and Cross-validation is often connected to Active compound, Random event and Treatment modality as part of broader study in Artificial intelligence.
The study incorporates disciplines such as Data mining and Pattern recognition in addition to Random forest.
His research in chEMBL intersects with topics in Similarity, Virtual screening and Quantitative structure–activity relationship.
Andreas Bender has included themes like Scaffold hopping, In silico and Fingerprint in his Similarity study.
His Drug discovery research is multidisciplinary, incorporating elements of Illusion, Clinical trial and Computational biology.
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