What is he best known for?
The fields of study he is best known for:
Michael S. Waterman mainly investigates Genetics, Computational biology, Combinatorics, Algorithm and Genome.
His Computational biology research is multidisciplinary, incorporating elements of Consensus sequence, Alignment-free sequence analysis and Sequence analysis.
Michael S. Waterman has researched Alignment-free sequence analysis in several fields, including Multiple sequence alignment, Set and BLOSUM.
His Multiple sequence alignment research incorporates themes from Gap penalty, Conserved Domain Database, Substitution matrix and Structural alignment.
The concepts of his Combinatorics study are interwoven with issues in Discrete mathematics, Sequence alignment, Metric and Bioinformatics.
The study incorporates disciplines such as Sequence comparison, Poisson distribution, Similarity, Distance measures and Homology in addition to Algorithm.
His most cited work include:
- Identification of common molecular subsequences. (8185 citations)
- The B73 Maize Genome: Complexity, Diversity, and Dynamics (3021 citations)
- Comparison of biosequences (1406 citations)
What are the main themes of his work throughout his whole career to date?
Algorithm, Combinatorics, Genetics, Computational biology and Genome are his primary areas of study.
Michael S. Waterman has included themes like Restriction map, Optical mapping and Sequence alignment in his Algorithm study.
In his study, Poisson distribution, Sequence comparison and Artificial intelligence is inextricably linked to Sequence, which falls within the broad field of Combinatorics.
The Computational biology study combines topics in areas such as Sequence analysis, Shotgun sequencing and Sequence.
Michael S. Waterman combines subjects such as Data mining, DNA sequencing and Metagenomics with his study of Genome.
In most of his Multiple sequence alignment studies, his work intersects topics such as Alignment-free sequence analysis.
He most often published in these fields:
- Algorithm (25.40%)
- Combinatorics (19.35%)
- Genetics (19.76%)
What were the highlights of his more recent work (between 2008-2019)?
- Genome (17.74%)
- Artificial intelligence (11.29%)
- Sequence comparison (9.27%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Genome, Artificial intelligence, Sequence comparison, Statistic and Computational biology.
Genome is a subfield of Genetics that he investigates.
His study in the field of Copy-number variation and Gene mapping also crosses realms of Word length.
His research in Sequence comparison intersects with topics in Normal approximation, Sequence, Completeness and Combinatorics.
His studies in Statistic integrate themes in fields like Algorithm, Statistical power, Markov chain and Data sequences.
His Computational biology study incorporates themes from Phenotype, Multiple comparisons problem and Genomics.
Between 2008 and 2019, his most popular works were:
- The B73 Maize Genome: Complexity, Diversity, and Dynamics (3021 citations)
- High-resolution human genome structure by single-molecule analysis (164 citations)
- Alignment-free sequence comparison (I): statistics and power. (162 citations)
In his most recent research, the most cited papers focused on:
His primary areas of study are Genome, Sequence comparison, Software, Genetics and Data mining.
His biological study spans a wide range of topics, including Alignment-free sequence analysis, Sequence analysis and Metagenomics.
His Sequence comparison research integrates issues from Data type, Normal approximation, Web service, Combinatorics and Machine learning.
Michael S. Waterman interconnects Sequence and Inference, Artificial intelligence, Benchmark in the investigation of issues within Software.
His Genetics study frequently draws connections between adjacent fields such as Computational biology.
His Data mining research incorporates elements of Biological network, Network motif, Pairwise comparison and DNA sequencing.
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