Development of a R package to facilitate the learning of clustering techniques

This project explores the development of a tool, in the form of a R package, to ease the process of learning clustering techniques, how they work and what their pros and cons are. This tool should provide implementations for several different clustering techniques with explanations in order to allow the student to get familiar with the characteristics of each algorithm by testing them against several different datasets while deepening their understanding of them through the explanations. Additionally, these explanations should adapt to the input data, making the tool not only adept for self-regulated learning but for teaching too.Grado en Ingeniería Informátic

Robust evolutionary algorithms

Evolutionary Algorithms (EAs) have shown great potential to solve complex real world problems, but their dependence on problem specific configuration in order to obtain high quality performance prevents EAs from achieving widespread use. While it is widely accepted that statically configuring an EA is already a complex problem, dynamic configuration of an EA is a combinatorially harder problem. Evidence provided here supports the claim that EAs achieve the best results when using dynamic configurations. By designing methods that automatically configure parts of an EA or by changing how EAs work to avoid configurable aspects, EAs can be made more robust, allowing them better performance on a wider variety of problems with less requirements on the user. Two methods are presented in this thesis to increase the robustness of EAs. The first is a novel algorithm designed to automatically configure and dynamically update the recombination method which is used by the EA to exploit known information to create new solutions. The techniques used by this algorithm can likely be applied to other aspects of an EA in the future, leading to even more robust EAs. The second is an existing set of algorithms which only require a single configurable parameter. The analysis of the existing set led to the creation of a new variation, as well as a better understanding of how these algorithms work. Both methods are able to outperform more traditional EAs while also making both easier to apply to new problems. By building upon these methods, and perhaps combining them, EAs can become even more robust and become more widely used --Abstract, page iv

Consensus clustering and functional interpretation of gene-expression data

Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas

{Pairwise DNA Methylation Analysis

Üha süvenev huvi geneetikasse on kaasa toonud epigeneetiliste modifikatsioonide avastamise. Epigeneetilisteks modifikatsioonideks loetakse rakulisi tunnuseid mis pole põhjustatud DNA sekventsis esinevatest erinevustest. Sellistest modifikatsioonidest on enim uuritud DNA metülatsiooni, mis seisneb DNA nukleobaasile metüülrühma lisandumises. Vaatamata sellele, et metülatsiooni ennast on uuritud, vaadatakse metülatsiooni enamasti lugemite kaupa. See, kuidas metülatsiooni esinemisega seotud nukleobaasid teineteist mõjutavad, on suhteliselt tundmatu. Üheks põhjuseks, miks DNA paaride kaupa metülatsiooni piisavalt uuritud pole, võib pidada tööriistade vähesusest. Kahjuks on selle valdkonna kohta avaldatud vaid paar uurimustööd. Selle bakalaureusetöö eesmärgiks on anda lugejale ülevaade paaride kaupa DNA metülatsiooniga seotud eksperimendi läbiviimisest ja esitleda tööriista R paketi kujul, mis võimaldab koguda lugemitest andmeid paaride kaupa metülatsiooni kohta.The ever-increasing interest in genetics has lead to the discovery of epigenetic alterations - cellular trait variations not caused by the changes in the DNA sequence. DNA mehtylation is the most researched one of the alterations, being the addition of a methyl group to a DNA nucleobase. Even though methylation itself has been reseached, it is often considered on a per-read basis. The pairwise influence of methylation sites is still relatively unexplored. One of the reasons for this is that it is difficult to conduct such an experiment, mainly due to the lack of tools. The matters are worse with pairwise methylation, as only a few papers have been published about the topic. The aim of this thesis is to provide insight in conducting an analysis of pairwise DNA methylation, and to provide a tool as an R package for extracting pairwise methylation values