What is he best known for?
The fields of study he is best known for:
- Gene
- Enzyme
- Operating system
Lennart Martens spends much of his time researching Proteomics, Proteome, Bioinformatics, Biochemistry and Computational biology.
His Proteomics research focuses on Proteomics Standards Initiative in particular.
The various areas that Lennart Martens examines in his Proteome study include Platelet, Integrin, Quantitative analysis, Mass spectrometry and Protein identification.
The concepts of his Bioinformatics study are interwoven with issues in Proteomics methods, Data science and Library science.
His work investigates the relationship between Data science and topics such as Human proteome project that intersect with problems in Standardization.
His Biochemistry study combines topics in areas such as Immunology and Pathogenesis.
His most cited work include:
- ProteomeXchange provides globally coordinated proteomics data submission and dissemination (1926 citations)
- The minimum information about a proteomics experiment (MIAPE) (608 citations)
- The minimum information about a proteomics experiment (MIAPE) (608 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Proteomics, Computational biology, Data science, Proteome and Bioinformatics.
His Proteomics research includes themes of Data mining, Identification, Peptide and Mass spectrometry.
His Identification study combines topics from a wide range of disciplines, such as Software and World Wide Web, Search engine.
Lennart Martens frequently studies issues relating to Metaproteomics and Computational biology.
Lennart Martens has researched Data science in several fields, including Standardization and Field.
His Proteome research is multidisciplinary, relying on both Protein identification, Shotgun proteomics and Human proteome project.
He most often published in these fields:
- Proteomics (34.12%)
- Computational biology (19.29%)
- Data science (17.80%)
What were the highlights of his more recent work (between 2019-2021)?
- Metaproteomics (6.23%)
- Workflow (8.31%)
- Proteomics (34.12%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Metaproteomics, Workflow, Proteomics, Proteogenomics and Computational biology.
His studies deal with areas such as Web application, Set and Semantic similarity as well as Metaproteomics.
His work in Workflow addresses subjects such as Machine learning, which are connected to disciplines such as Graphical user interface, Python and Atomic composition.
His work deals with themes such as Variant Call Format and Mass spectrometry, which intersect with Proteomics.
The Proteogenomics study combines topics in areas such as Label-free quantification, Software, Artificial intelligence and Identification.
Lennart Martens has included themes like Identifier, Proteome and Shotgun proteomics in his Computational biology study.
Between 2019 and 2021, his most popular works were:
- Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows. (38 citations)
- Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries. (12 citations)
- The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows. (10 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Enzyme
- Artificial intelligence
The scientist’s investigation covers issues in Workflow, Metaproteomics, Chromatography, Retention time and Field.
His Workflow research incorporates themes from Identification, Machine learning, Deep learning, Artificial intelligence and Ambiguity.
Lennart Martens interconnects Shotgun proteomics, Database search engine, Search engine and Component in the investigation of issues within Identification.
His Metaproteomics research incorporates elements of Pipeline, Web application, World Wide Web, Interface and InterPro.
His study in Retention time is interdisciplinary in nature, drawing from both Python, Graphical user interface, Atomic composition and Peptide.
His work carried out in the field of Field brings together such families of science as End-to-end principle, Functional annotation, Data processing and Data science.
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