The availability of new technologies supplied life scientists with large amounts of experimental data. The data sets are large not only in terms of the number of observations, but also in terms of the number of recorded features. One of the aims of modeling is to explain a given phenomenon in possibly the simplest way, hence the need for selection of suitable features. We extended a Monte Carlo-based approach to selecting statistically significant features with discovery of feature interdependencies and used it in modeling sequence-function relationships in proteins. Our approach led to compact and easy-to-interpret predictive models. First, we represented protein sequences in terms of their physicochemical properties. This was followed by our feature selection and discovery of feature interdependencies. Finally, predictive models based on e.g., decision trees or rough sets were constructed. We applied the method to model two important biological problems: 1) HIV-1 resistance to reverse transcriptase-targeted drugs and 2) post-translational modifications of proteins. In the case of HIV resistance, we were not only able to predict whether the mutated protein is resistant to a drug or not, but we also suggested some new, previously neglected, mutations that possibly contribute to drug resistance. For all these mutations we proposed probable molecular mechanisms of action using literature and 3D structure studies. In the case of predicting PTMs, we built high accuracy models of modifications. In comparison to other methods, we were able to resolve whether the closest neighborhood of a residue (the nanomer) is sufficient to determine its modification status. Importantly, the application of our method yields networks of interdependent physicochemical properties of amino acids that show how these properties collaborate in establishing a given modification. We believe that the presented methods will help researchers to analyze a large class of important biological problems and will guide them in their research.

The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties &ldquo / low&rdquo / , &ldquo / medium&rdquo / and &ldquo / high&rdquo / . In the rough fuzzy MLP, initial weights and near optimal number of hidden nodes are estimated using rough dependency rules. A rough fuzzy RBF structure similar to the rough fuzzy MLP is proposed. The rough fuzzy RBF was inspected whether dependencies like the ones in rough fuzzy MLP can be concluded.

Avec le développement du cloud computing, de nouveaux services voient le jour et il devient primordial que les utilisateurs aient les outils nécessaires pour choisir parmi ses services. La théorie des ensembles approximatifs représente un bon outil de traitement de données incertaines. Elle peut exploiter les connaissances cachées ou appliquer des règles sur des ensembles de données. Le but principal de cette thèse est d'utiliser la théorie des ensembles approximatifs pour aider les utilisateurs de cloud computing à prendre des décisions. Dans ce travail, nous avons, d'une part, proposé un cadre utilisant la théorie des ensembles approximatifs pour la sélection de services cloud et nous avons donné un exemple en utilisant les ensembles approximatifs dans la sélection de services cloud pour illustrer la pratique et analyser la faisabilité de cette approche. Deuxièmement, l'approche proposée de sélection des services cloud permet d’évaluer l’importance des paramètres en fonction des préférences de l'utilisateur à l'aide de la théorie des ensembles approximatifs. Enfin, nous avons effectué des validations par simulation de l’algorithme proposé sur des données à large échelle pour vérifier la faisabilité de notre approche en pratique. Les résultats de notre travail peuvent aider les utilisateurs de services cloud à prendre la bonne décision et aider également les fournisseurs de services cloud pour cibler les améliorations à apporter aux services qu’ils proposent dans le cadre du cloud computing / With the development of the cloud computing technique, users enjoy various benefits that high technology services bring. However, there are more and more cloud service programs emerging. So it is important for users to choose the right cloud service. For cloud service providers, it is also important to improve the cloud services they provide, in order to get more customers and expand the scale of their cloud services.Rough set theory is a good data processing tool to deal with uncertain information. It can mine the hidden knowledge or rules on data sets. The main purpose of this thesis is to apply rough set theory to help cloud users make decision about cloud services. In this work, firstly, a framework using the rough set theory in cloud service selection is proposed, and we give an example using rough set in cloud services selection to illustrate and analyze the feasibility of our approach. Secondly, the proposed cloud services selection approach has been used to evaluate parameters importance based on the users’ preferences. Finally, we perform experiments on large scale dataset to verity the feasibility of our proposal.The performance results can help cloud service users to make the right decision and help cloud service providers to target the improvement about their cloud services

This thesis presents the implementation of a knowledge base system for rough sets [Paw92]within the logic programming framework. The combination of rough set theory with logic programming is a novel approach. The presented implementation serves as a prototype system for the ideas presented in [VDM03a, VDM03b]. The system is available at "http://www.ida.liu.se/rkbs". The presented language for describing knowledge in the rough knowledge base caters for implicit definition of rough sets by combining different regions (e.g. upper approximation, lower approximation, boundary) of other defined rough sets. The rough knowledge base system also provides methods for querying the knowledge base and methods for computing quantitative measures. We test the implemented system on a medium sized application example to illustrate the usefulness of the system and the incorporated language. We also provide performance measurements of the system.

No description available.

Acute lymphoblastic leukemia is a hematological malignancy that gains a proliferative advantage and originates in the bone marrow. One of the more common genetic alterations in ALL is KMT2A-rearrangement which constitutes 80% of the cases of ALL in infants. Patients carrying the KMT2A rearrangement have a poor prognosis and will eventually develop drug resistance. This project aimed to find new therapeutic targets which would help in the development of novel drugs. We designed a model which uses gene expression data, to infer expressions of oncogenes and the genes which could be associated with immune pathways. The data was extracted and transformed by removing the batch effects and identifying the biotypes of these genes for more focused research. Here we utilized exome RNA-seq,  hence it was necessary to reduce the high dimensionality of the data. The dimensionality reduction was performed using Monte Carlo Feature Selection. After the feature selection, a list of highly significant genes was obtained. These genes were used in a machine learning model, R.ROSETTA, which produces rule-based results centered on rough sets theory. The rules were visualized using VisuNet, an interactive tool that creates networks from the rules. Among others, we identified levels of expressions of genes such as JAK3, TOX3, and DMRTA1 and their relations to other genes  using the machine learning model. These significant genes were also used to do pathway analysis using pathfindR which allowed us to infer the oncogenic pathways. The pathway analysis helped us deduce pathways such as immunodeficiency and other signaling pathways that could be potential drugs

Rough sets and neural networks both offer good theoretical background for data processing and analysis. However, both of them suffer from few issues. This thesis will investigate methods by which these two concepts are merged, and few such solutions will be implemented and compared with conventional algorithm to study the benefits of this approach.