What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Gene
Tak-Wah Lam mostly deals with Genome, Parallel computing, DNA sequencing, Computational biology and Sequence assembly.
Genome is often connected to Data mining in his work.
His work on Parallel algorithm as part of general Parallel computing research is frequently linked to Ultra fast, thereby connecting diverse disciplines of science.
His Computational biology research integrates issues from Whole genome sequencing and Sequence.
His studies in Sequence assembly integrate themes in fields like Contig and Genomics.
His biological study spans a wide range of topics, including Algorithm design and Error detection and correction.
His most cited work include:
- SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler (3123 citations)
- SOAP2: an improved ultrafast tool for short read alignment. (2900 citations)
- MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph (1444 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Combinatorics, Algorithm, Competitive analysis, Time complexity and Online algorithm.
His Combinatorics research is multidisciplinary, relying on both Matching, Discrete mathematics, Data structure and Pattern matching.
The concepts of his Algorithm study are interwoven with issues in Sequence and Speedup.
His work investigates the relationship between Competitive analysis and topics such as Distributed computing that intersect with problems in Dynamic priority scheduling.
In his work, Theoretical computer science and Space is strongly intertwined with Set, which is a subfield of Time complexity.
His Online algorithm course of study focuses on Parallel computing and Multiprocessor scheduling.
He most often published in these fields:
- Combinatorics (24.81%)
- Algorithm (21.48%)
- Competitive analysis (15.56%)
What were the highlights of his more recent work (between 2014-2021)?
- Computational biology (11.11%)
- Artificial intelligence (6.30%)
- Genomics (5.19%)
In recent papers he was focusing on the following fields of study:
Tak-Wah Lam focuses on Computational biology, Artificial intelligence, Genomics, Nanopore sequencing and Data mining.
His studies deal with areas such as Regulation of gene expression, DNA sequencing, Gene, Reference genome and Signal transduction as well as Computational biology.
His Artificial intelligence study combines topics in areas such as Natural language processing, Source code, Sequence assembly and Pattern recognition.
His research on Genomics concerns the broader Genome.
His research integrates issues of Effective method and Computer engineering in his study of Genome.
Tak-Wah Lam has included themes like Construct, Software and Multiple sequence alignment in his Data mining study.
Between 2014 and 2021, his most popular works were:
- MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph (1444 citations)
- MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices (397 citations)
- Erratum to "SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler" [GigaScience, (2012), 1, 18] (116 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Gene
His primary areas of study are Genomics, Artificial neural network, Computational biology, Parallel computing and Nanopore sequencing.
His Genomics study deals with the bigger picture of Genome.
The Computational biology study which covers DNA sequencing that intersects with Task, Allele and Zygosity.
His study in the field of Speedup also crosses realms of Single node.
His Nanopore sequencing research incorporates themes from Germline, Sequence assembly, Limit, Machine learning and Central processing unit.
His study looks at the intersection of Software and topics like Scalability with Parallel algorithm, Memory footprint and De Bruijn sequence.
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