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Articles 1 - 3 of 3
Full-Text Articles in Theory and Algorithms
Incremental Phylogenetics By Repeated Insertions: An Evolutionary Tree Algorithm, Peter Revesz, Zhiqiang Li
Incremental Phylogenetics By Repeated Insertions: An Evolutionary Tree Algorithm, Peter Revesz, Zhiqiang Li
School of Computing: Faculty Publications
We introduce the idea of constructing hypothetical evolutionary trees using an incremental algorithm that inserts species one-by-one into the current evolutionary tree. The method of incremental phylogenetics by repeated insertions lead to an algorithm that can be used on DNA, RNA and amino acid sequences. According to experimental results on both synthetic and biological data, the new algorithm generates more accurate evolutionary trees than the UPGMA and the Neighbor Joining algorithms.
A Mitochondrial Dna-Based Computational Model Of The Spread Of Human Populations, Peter Revesz
A Mitochondrial Dna-Based Computational Model Of The Spread Of Human Populations, Peter Revesz
School of Computing: Faculty Publications
This paper presents a mitochondrial DNA-based computational model of the spread of human populations. The computation model is based on a new measure of the relatedness of two populations that may be both heterogeneous in terms of their set of mtDNA haplogroups. The measure gives an exponentially increasing weight for the similarity of two haplogroups with the number of levels shared in the mtDNA classification tree. In an experiment, the computational model is applied to the study of the relatedness of seven human populations ranging from the Neolithic through the Bronze Age to the present. The human populations included in …
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
COBRA Preprint Series
Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …