What is he best known for?
The fields of study he is best known for:
His primary areas of investigation include Genetics, Computational biology, Cluster analysis, Combinatorics and Data mining.
His Genetics study is mostly concerned with Genome, Gene, Human genome, Transcription factor and Induced pluripotent stem cell.
The Computational biology study which covers Gene expression profiling that intersects with Regulation of gene expression.
The various areas that Ron Shamir examines in his Cluster analysis study include DNA microarray and Gene expression.
His work in Combinatorics covers topics such as Discrete mathematics which are related to areas like Interval, Approximation algorithm, Point and Partition.
When carried out as part of a general Data mining research project, his work on Identification is frequently linked to work in Expression and Throughput, therefore connecting diverse disciplines of study.
His most cited work include:
- Clustering gene expression patterns. (1109 citations)
- Modelling and analysis of gene regulatory networks (808 citations)
- Network-based prediction of protein function (804 citations)
What are the main themes of his work throughout his whole career to date?
Genetics, Computational biology, Combinatorics, Algorithm and Gene are his primary areas of study.
His works in Genome, Transcription factor, Regulation of gene expression, Promoter and Gene expression profiling are all subjects of inquiry into Genetics.
His studies deal with areas such as Enhancer, DNA, Bioinformatics and Genomics as well as Computational biology.
In his study, Interval is inextricably linked to Discrete mathematics, which falls within the broad field of Combinatorics.
His work in Gene expression and Gene regulatory network is related to Gene.
As a member of one scientific family, Ron Shamir mostly works in the field of Gene expression, focusing on Cluster analysis and, on occasion, DNA microarray.
He most often published in these fields:
- Genetics (25.07%)
- Computational biology (22.45%)
- Combinatorics (17.20%)
What were the highlights of his more recent work (between 2014-2021)?
- Computational biology (22.45%)
- Genome (12.54%)
- Gene (14.29%)
In recent papers he was focusing on the following fields of study:
Ron Shamir mainly investigates Computational biology, Genome, Gene, Genetics and Algorithm.
His studies in Computational biology integrate themes in fields like Enhancer, Transcription factor, DNA, Genomics and Regulation of gene expression.
His work on Genome rearrangement as part of his general Genome study is frequently connected to Joins, thereby bridging the divide between different branches of science.
His biological study spans a wide range of topics, including Retinal and Cell adhesion.
His Algorithm research includes elements of Chromosome and Partition.
His study focuses on the intersection of Gene expression and fields such as Cohort with connections in the field of Cluster analysis.
Between 2014 and 2021, his most popular works were:
- Multi-omic and multi-view clustering algorithms: review and cancer benchmark (109 citations)
- Recycler: an algorithm for detecting plasmids from de novo assembly graphs. (81 citations)
- Transcription factor family‐specific DNA shape readout revealed by quantitative specificity models (61 citations)
In his most recent research, the most cited papers focused on:
Ron Shamir spends much of his time researching Computational biology, Algorithm, Genetics, Gene and Gene expression profiling.
The study incorporates disciplines such as Enhancer, Expression quantitative trait loci, Inference and DNA in addition to Computational biology.
His Algorithm study incorporates themes from Upper and lower bounds, Chromosome and Heuristics.
Much of his study explores Genetics relationship to Element.
His work on Gene expression and Gene regulatory network as part of general Gene research is frequently linked to Constant, thereby connecting diverse disciplines of science.
His Gene expression profiling research is multidisciplinary, incorporating perspectives in Internal medicine, Disease, Oncology and Bioinformatics.
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