Bioinformatics: Sequence and Genome Analysis
By D. Mount. 2001
1,126 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/14d46d1d2f7cd38f68f9fc68726b45db57e327b9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Douglas B. Kell
- Julia Handl
- Joshua D. Knowles
- Bin Liu
- Junjie Chen
- Xiaowei Wang
- Brian Seed
- Mohammed J. Zaki
Citations in the corpus
- A PCR primer bank for quantitative gene expression analysis. (2003, background): “Although the prediction of primer secondary structure is still challenging at present, secondary structure is most likely to occur in regions of self-complementarity (28).”
- Efficiently mining frequent trees in a forest: algorithms and applications (2005, methodology): “This tree was produced using a parsimonybased phylogenetic tree construction method [24]; using different algorithms may produce several variants of the evolutionary relationships.”
- Artificial error generation for translation-based grammatical error correction (2016, background): “Three-way alignments are a special case of multiple sequence alignment, a well-known string matching problem in computational biology (Mount, 2004).”
- Multiobjective Optimization in Bioinformatics and Computational Biology (2007, background): “For this purpose, the sequences are set down one upon the other and are padded with gaps so that a scoring model which assesses the quality of an alignment as a function of the number/type of mismatches, matches, and gaps is optimized [90].”
- Enhanced Sequence Matching for Action Recognition from 3D Skeletal Data (2014, positive): “Inspired by DNA sequence alignment approach used in bioinformatics [2–5], we construct action sequence from a set of elementary Moving Poses (eMPs) and match two sequences using a new scoring function.”
- A comprehensive review and comparison of different computational methods for protein remote homology detection (2018, methodology): “These methods significantly reduce the computational cost, especially for the sequences with overlapping regions [52, 53].”