Mining Complex Hydrobiological Data with Galois Lattices

We have used Galois lattices for mining hydrobiological data. These data are about macrophytes, that are macroscopic plants living in water bodies. These plants are characterized by several biological traits, that own several modalities. Our aim is to cluster the plants according to their common traits and modalities and to find out the relations between traits. Galois lattices are efficient methods for such an aim, but apply on binary data. In this article, we detail a few approaches we used to transform complex hydrobiological data into binary data and compare the first results obtained thanks to Galois lattices

Mining Complex Hydrobiological Data with Galois Lattices

International audienceWe used Galois lattices for mining hydrobiological data about macrophytes, i.e. macroscopic plants living in water bodies. These plants are characterized by several biological traits, that are divided into several modalities. Our aim was to cluster the plants according to their common traits and modalities and to find out the relations between the traits. Galois lattices are efficient methods for such an aim, but apply to binary data. In this article, we detail a few of the approaches we used to turn complex hydrobiological data into binary data and compare the first results obtained thanks to Galois lattices

Formal Concept Analysis Applications in Bioinformatics

Bioinformatics is an important field that seeks to solve biological problems with the help of computation. One specific field in bioinformatics is that of genomics, the study of genes and their functions. Genomics can provide valuable analysis as to the interaction between how genes interact with their environment. One such way to measure the interaction is through gene expression data, which determines whether (and how much) a certain gene activates in a situation. Analyzing this data can be critical for predicting diseases or other biological reactions. One method used for analysis is Formal Concept Analysis (FCA), a computing technique based in partial orders that allows the user to examine the structural properties of binary data based on which subsets of the data set depend on each other. This thesis surveys, in breadth and depth, the current literature related to the use of FCA for bioinformatics, with particular focus on gene expression data. This includes descriptions of current data management techniques specific to FCA, such as lattice reduction, discretization, and variations of FCA to account for different data types. Advantages and shortcomings of using FCA for genomic investigations, as well as the feasibility of using FCA for this application are addressed. Finally, several areas for future doctoral research are proposed. Adviser: Jitender S. Deogu

URI Undergraduate Course Catalog 1988-1989

This is a digitized, downloadable version of the University of Rhode Island Undergraduate Course Catalog.https://digitalcommons.uri.edu/course-catalogs/1034/thumbnail.jp

URI Undergraduate Course Catalog 1987-1988

This is a digitized, downloadable version of the University of Rhode Island Undergraduate Course Catalog.https://digitalcommons.uri.edu/course-catalogs/1033/thumbnail.jp

URI Undergraduate Course Catalog 1986-1987

This is a digitized, downloadable version of the University of Rhode Island Undergraduate Course Catalog.https://digitalcommons.uri.edu/course-catalogs/1029/thumbnail.jp