Fundamental boolean network modelling for genetic regulatory pathways : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

A Boolean model is a switch-like behaviour model of which it is easy to ignore any effects at the intermediate levels. Boolean modelling has been applied in many areas, including mammalian cell cycle networks. However, little effort has been put into the consideration of activation, inhibition and protein decay networks to designate the direct roles of a gene or a synthesised protein, as an activator or inhibitor of a target gene. Hence, we proposed to split the conventional Boolean functions at the subfunction level into activation and inhibition domains, taking into account the effectiveness of protein decay. As a consequence, two novel data-driven Boolean models for genetic regulatory pathways, namely the fundamental Boolean model (FBM) and the temporal fundamental Boolean model (TFBM), have been proposed to draw insights into gene activation, inhibition, and protein decay. The novel Boolean models could reveal significant trajectories in genes and provide a new direction on Boolean modelling research. The proposed novel Boolean models are fine-grained. A novel network inference methodology named Orchard cube technology has been proposed to infer the related networks, namely fundamental Boolean networks (FBNs) and temporal fundamental Boolean networks (TFBNs) based on FBM and TFBM respectively. As a primary result of this study, an R package, called FBNNet, has been developed based on the proposed methodology and has been used to demonstrate the FBNs and TFBNs for mammalian cell cycle pathways and acute childhood leukaemia pathways respectively. Our experimental results show that the proposed FBM and TFBM could be used to explicitly reconstruct the internal networks of mammalian cell cycles and acute childhood leukaemia. Especially during the study, we produced the fundamental Boolean networks on the childhood acute lymphoblastic leukaemia gene expression data, which were produced in clinical settings. The pathways may be useful for pharmaceutical agents to identify any side effects when applying GC induced apoptosis on children

Functional networks inference from rule-based machine learning models

BACKGROUND: Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. RESULTS: We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. AVAILABILITY: The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0106-4) contains supplementary material, which is available to authorized users

Knowledge extraction from biomedical data using machine learning

PhD ThesisThanks to the breakthroughs in biotechnologies that have occurred during the recent years, biomedical data is accumulating at a previously unseen pace. In the field of biomedicine, decades-old statistical methods are still commonly used to analyse such data. However, the simplicity of these approaches often limits the amount of useful information that can be extracted from the data. Machine learning methods represent an important alternative due to their ability to capture complex patterns, within the data, likely missed by simpler methods. This thesis focuses on the extraction of useful knowledge from biomedical data using machine learning. Within the biomedical context, the vast majority of machine learning applications focus their e↵ort on the generation and validation of prediction models. Rarely the inferred models are used to discover meaningful biomedical knowledge. The work presented in this thesis goes beyond this scenario and devises new methodologies to mine machine learning models for the extraction of useful knowledge. The thesis targets two important and challenging biomedical analytic tasks: (1) the inference of biological networks and (2) the discovery of biomarkers. The first task aims to identify associations between di↵erent biological entities, while the second one tries to discover sets of variables that are relevant for specific biomedical conditions. Successful solutions for both problems rely on the ability to recognise complex interactions within the data, hence the use of multivariate machine learning methods. The network inference problem is addressed with FuNeL: a protocol to generate networks based on the analysis of rule-based machine learning models. The second task, the biomarker discovery, is studied with RGIFE, a heuristic that exploits the information extracted from machine learning models to guide its search for minimal subsets of variables. The extensive analysis conducted for this dissertation shows that the networks inferred with FuNeL capture relevant knowledge complementary to that extracted by standard inference methods. Furthermore, the associations defined by FuNeL are discovered - 6 - more pertinent in a disease context. The biomarkers selected by RGIFE are found to be disease-relevant and to have a high predictive power. When applied to osteoarthritis data, RGIFE confirmed the importance of previously identified biomarkers, whilst also extracting novel biomarkers with possible future clinical applications. Overall, the thesis shows new e↵ective methods to leverage the information, often remaining buried, encapsulated within machine learning models and discover useful biomedical knowledge.European Union Seventh Framework Programme (FP7/2007- 2013) that funded part of this work under the “D-BOARD” project (grant agreement number 305815)

Additional file 2 of Functional networks inference from rule-based machine learning models

Networks. All networks used in the analysis given as a list of edges. (XZ 11980 kb

Additional file 4 of Functional networks inference from rule-based machine learning models

Enriched terms. Complete list of enriched terms for each dataset and network. (XZ 3041 kb