What is he best known for?
The fields of study he is best known for:
Genetics, Computational biology, Cancer, Gene and Genetic diversity are his primary areas of study.
His Computational biology study combines topics from a wide range of disciplines, such as Pyrosequencing, Evolutionary dynamics, Single cell sequencing, Quasispecies model and Hidden Markov model.
Niko Beerenwinkel combines subjects such as Mutation and Cancer research with his study of Cancer.
The concepts of his Cancer research study are interwoven with issues in Cell, Benign tumor, Stem cell, CA15-3 and Colorectal cancer.
His Gene study frequently draws connections to other fields, such as Drug resistance.
His Metastasis study incorporates themes from Immunology and Point mutation.
His most cited work include:
- ROCR: visualizing classifier performance in R (2078 citations)
- Comparative lesion sequencing provides insights into tumor evolution (621 citations)
- Comparative lesion sequencing provides insights into tumor evolution (621 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computational biology, Genetics, Gene, Cancer and Mutation.
His research on Computational biology also deals with topics like
- Genomics which connect with Somatic evolution in cancer,
- Inference and related Data mining.
As part of his studies on Genetics, Niko Beerenwinkel often connects relevant areas like Genetic diversity.
His Cancer research is multidisciplinary, incorporating perspectives in Mutation and Cancer research.
His Mutation research integrates issues from Tree, Cell and Sequencing data.
His Virology research is multidisciplinary, relying on both Transcriptome and Drug resistance.
He most often published in these fields:
- Computational biology (49.14%)
- Genetics (30.81%)
- Gene (27.63%)
What were the highlights of his more recent work (between 2019-2021)?
- Computational biology (49.14%)
- Cancer (24.21%)
- Mutation (14.43%)
In recent papers he was focusing on the following fields of study:
Niko Beerenwinkel mostly deals with Computational biology, Cancer, Mutation, Gene and Somatic evolution in cancer.
His Computational biology research incorporates themes from Profiling, Cell, DNA sequencing and Genomics.
His biological study spans a wide range of topics, including Cancer research, Clear cell renal cell carcinoma and Oncology.
His Mutation research also works with subjects such as
- Non parametric clustering which connect with Treatment resistance and Tumor initiation,
- Bayesian probability together with Sampling and Data mining.
His Gene study frequently intersects with other fields, such as Inference.
His research in Somatic evolution in cancer intersects with topics in Haematopoiesis, Carcinogenesis, Point mutation, Evolutionary dynamics and Myeloid leukemia.
Between 2019 and 2021, his most popular works were:
- Eleven grand challenges in single-cell data science (187 citations)
- Eleven grand challenges in single-cell data science (187 citations)
- Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. (28 citations)
In his most recent research, the most cited papers focused on:
His main research concerns Computational biology, Severe acute respiratory syndrome coronavirus 2, Somatic evolution in cancer, Genomics and Coronavirus disease 2019.
The study incorporates disciplines such as Cancer cell, Cancer, Cell culture, Clear cell renal cell carcinoma and Genetic screen in addition to Computational biology.
The various areas that Niko Beerenwinkel examines in his Somatic evolution in cancer study include Hematopoietic stem cell transplantation, Myeloid, Myeloid leukemia, Immunology and DNA sequencing.
His Myeloid leukemia research is multidisciplinary, incorporating perspectives in Clonal selection, KRAS and Genetics.
His research in Genomics intersects with topics in Clinical decision support system, Mutation, Treatment response, Omics and Profiling.
His Evolutionary biology research is multidisciplinary, relying on both Genetic diversity, Virus, Gene, Pandemic and Coronavirus.
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