What is he best known for?
The fields of study he is best known for:
Martin Weigert mainly focuses on Antibody, Molecular biology, Genetics, Receptor editing and Immunoglobulin light chain.
His Antibody research integrates issues from Mutation, Receptor and Antigen.
His Mutation research is multidisciplinary, incorporating perspectives in Amino acid and Cytoplasm.
His studies deal with areas such as Gene rearrangement, Transgene, Somatic cell, DNA and Epitope as well as Molecular biology.
Gene, Immunoglobulin heavy chain, Antibody Diversity, Immunoglobulin gene and Allelic exclusion are the core of his Genetics study.
His research in Receptor editing intersects with topics in Genetically modified mouse, Immune tolerance and Isotype.
His most cited work include:
- A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation (1105 citations)
- Receptor editing: an approach by autoreactive B cells to escape tolerance. (844 citations)
- The role of clonal selection and somatic mutation in autoimmunity. (615 citations)
What are the main themes of his work throughout his whole career to date?
Martin Weigert spends much of his time researching Molecular biology, Genetics, Antibody, Gene and Immunoglobulin light chain.
The various areas that Martin Weigert examines in his Molecular biology study include DNA, Receptor editing, Transgene and B cell.
Martin Weigert has included themes like Receptor and Gene rearrangement in his Receptor editing study.
The study of Antibody is intertwined with the study of Antigen in a number of ways.
His Antigen research includes themes of Marginal zone and Virology.
His Immunoglobulin light chain study integrates concerns from other disciplines, such as Lambda, Antibody Diversity and Inbred strain.
He most often published in these fields:
- Molecular biology (39.47%)
- Genetics (31.05%)
- Antibody (30.53%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (11.58%)
- Convolutional neural network (6.84%)
- Deep learning (5.79%)
In recent papers he was focusing on the following fields of study:
Martin Weigert focuses on Artificial intelligence, Convolutional neural network, Deep learning, Pattern recognition and Segmentation.
His Artificial intelligence research incorporates themes from Adaptive optics, Computer vision, Sample, Microscopy and Computational biology.
His Deep learning research includes elements of Ground truth and Cell.
His Pattern recognition study incorporates themes from Image processing, Face and Benchmark.
His research in Segmentation intersects with topics in Object detection and Feature.
Martin Weigert combines subjects such as Endothelial stem cell, Cell migration, B-cell receptor, B cell and Naive B cell with his study of Cell biology.
Between 2014 and 2021, his most popular works were:
- A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation (1105 citations)
- Content-aware image restoration: pushing the limits of fluorescence microscopy. (291 citations)
- Cell Detection with Star-convex Polygons (120 citations)
In his most recent research, the most cited papers focused on:
His primary areas of study are Microscope, Artificial intelligence, Optics, Microscopy and Convolutional neural network.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Deconvolution, Lateral resolution and Pattern recognition.
His work on Segmentation is typically connected to Polygon, Regular polygon and Star as part of general Pattern recognition study, connecting several disciplines of science.
His Image resolution study in the realm of Optics connects with subjects such as Fluorescence-lifetime imaging microscopy.
While the research belongs to areas of Image resolution, Martin Weigert spends his time largely on the problem of Resolution, intersecting his research to questions surrounding Image processing.
His work deals with themes such as Ground truth, Deep learning and Blind spot, which intersect with Convolutional neural network.
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