BiETech : Bicluster Ensemble Techniques

Various biclustering algorithms have emerged now a days that try to deliver good biclusters from gene expression data which satisfy a particular objective function. Users are lost in finding the best out of these algorithms. Ensemble techniques come to rescue     of these users by aggregating all the solutions and providing a single solution which is more robust and stable than its constituent solutions.  In this paper, we present two different ensemble techniques for biclustering solutions. We have used classifiers in one approach and the other approach uses the concept of metaclustering for forming the consensus. Experiments in this research are performed   on synthetic and real gene expression datasets as biologists are interested in finding meaningful patterns in expression of genes.  The experiments show that both the approaches proposed in the paper show improvement over the input solutions as well as the existing bicluster ensemble techniques

Analysis of alternative signaling pathways of endoderm induction of human embryonic stem cells identifies context specific differences

Background: Lineage specific differentiation of human embryonic stem cells (hESCs) is largely mediated by specific growth factors and extracellular matrix molecules. Growth factors initiate a cascade of signals which control gene transcription and cell fate specification. There is a lot of interest in inducing hESCs to an endoderm fate which serves as a pathway towards more functional cell types like the pancreatic cells. Research over the past decade has established several robust pathways for deriving endoderm from hESCs, with the capability of further maturation. However, in our experience, the functional maturity of these endoderm derivatives, specifically to pancreatic lineage, largely depends on specific pathway of endoderm induction. Hence it will be of interest to understand the underlying mechanism mediating such induction and how it is translated to further maturation. In this work we analyze the regulatory interactions mediating different pathways of endoderm induction by identifying co-regulated transcription factors.Results: hESCs were induced towards endoderm using activin A and 4 different growth factors (FGF2 (F), BMP4 (B), PI3KI (P), and WNT3A (W)) and their combinations thereof, resulting in 15 total experimental conditions. At the end of differentiation each condition was analyzed by qRT-PCR for 12 relevant endoderm related transcription factors (TFs). As a first approach, we used hierarchical clustering to identify which growth factor combinations favor up-regulation of different genes. In the next step we identified sets of co-regulated transcription factors using a biclustering algorithm. The high variability of experimental data was addressed by integrating the biclustering formulation with bootstrap re-sampling to identify robust networks of co-regulated transcription factors. Our results show that the transition from early to late endoderm is favored by FGF2 as well as WNT3A treatments under high activin. However, induction of late endoderm markers is relatively favored by WNT3A under high activin.Conclusions: Use of FGF2, WNT3A or PI3K inhibition with high activin A may serve well in definitive endoderm induction followed by WNT3A specific signaling to direct the definitive endoderm into late endodermal lineages. Other combinations, though still feasible for endoderm induction, appear less promising for pancreatic endoderm specification in our experiments. © 2012 Mathew et al.; licensee BioMed Central Ltd

Multi-biclustering solutions for classification and prediction problems

2009 - 20101The search for similarities in large data sets has a relevant role in many scientific fields. It permits to classify several types of data without an explicit information about them. Unfortunately, the experimental data contains noise and errors, and therefore the main task of mathematicians is to find algorithms that permit to analyze this data with maximal precision. In many cases researchers use methodologies such as clustering to classify data with respect to the patterns or conditions. But in the last few years new analysis tool such as biclustering was proposed and applied to many specific problems. My choice of biclustering methods is motivated by the accuracy obtained in the results and the possibility to find not only rows or columns that provide a dataset partition but also rows and columns together. In this work, two new biclustering algorithms, the Combinatorial Biclustering Algorithm (CBA) and an improvement of the Possibilistic Biclustering Algorithm, called Biclustering by resampling, are presented. The first algorithm (that I call Combinatorial) is based on the direct definition of bicluster, that makes it clear and very easy to understand. My algorithm permits to control the error of biclusters in each step, specifying the accepted value of the error and defining the dimensions of the desired biclusters from the beginning. The comparison with other known biclustering algorithms is shown. The second algorithm is an improvement of the Possibilistic Biclustering Algorithm (PBC). The PBC algorithm, proposed by M. Filippone et al., is based on the Possibilistic Clustering paradigm, and finds one bicluster at a time, assigning a membership to the bicluster for each gene and for each condition. PBC uses an objective function that maximizes a bicluster cardinality and minimizes a residual error. The biclustering problem is faced as the optimization of a proper functional. This algorithm obtains a fast convergence and good quality of the solutions. Unfortunately, PBC finds only one bicluster at a time. I propose an improved PBC algorithm based on data resampling, specifically Bootstrap aggregation, and Genetics algorithms. In such a way I can find all the possible biclusters together and include overlapped solutions. I apply the algorithm to a synthetic data and to the Yeast dataset and compare it with the original PBC method. [edited by the author]IX n.s

An effective measure for assessing the quality of biclusters

Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in data. Such heuristics have one common characteristic: they are guided by a measure that determines the quality of biclusters. It follows that defining such a measure is probably the most important aspect. One of the popular quality measure is the mean squared residue (MSR). However, it has been proven that MSR fails at identifying some kind of patterns. This motivates us to introduce a novel measure, called virtual error (VE), that overcomes this limitation. Results obtained by using VE confirm that it can identify interesting patterns that could not be found by MSR

Extraction de biclusters contraints dans des contextes bruités

National audienceL'extraction de biclusters, qui consiste à rechercher un groupe d'attributs qui montrent un comportement cohérent pour un sous-ensemble d'observations dans une matrice de données, est une tâche importante dans divers domaines, telle que la biologie. Nous proposons ici un nouveau système, COBIC, qui combine des algorithmes de graphes avec des méthodes de fouille de données pour une recherche efficace de biclusters pertinents et susceptibles de se recouvrir. COBIC est fondé sur les algorithmes de flot maximal/coupe minimale et est capable de prendre en compte les connaissances d'une base exprimées sous forme d'une classification, par un mécanisme d'adaptation des poids lors de l'extraction itérative des régions denses. L'évaluation de COBIC sur des données réelles et la comparaison par rapport à des méthodes efficaces de biclustering montrent que COBIC est très performant et en particulier lorsque la qualité des biclusters s'évalue en fonction de la significativité de l'enrichissement des clusters calculés avec les fonctions cellulaires décrites dans l'Ontologie GO

Biclustering electronic health records to unravel disease presentation patterns

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2019A Esclerose Lateral Amiotrófica (ELA) é uma doença neurodegenerativa heterogénea com padrões de apresentação altamente variáveis. Dada a natureza heterogénea dos doentes com ELA, aquando do diagnóstico os clínicos normalmente estimam a progressão da doença utilizando uma taxa de decaimento funcional, calculada com base na Escala Revista de Avaliação Funcional de ELA (ALSFRS-R). A utilização de modelos de Aprendizagem Automática que consigam lidar com este padrões complexos é necessária para compreender a doença, melhorar os cuidados aos doentes e a sua sobrevivência. Estes modelos devem ser explicáveis para que os clínicos possam tomar decisões informadas. Desta forma, o nosso objectivo é descobrir padrões de apresentação da doença, para isso propondo uma nova abordagem de Prospecção de Dados: Descoberta de Meta-atributos Discriminativos (DMD), que utiliza uma combinação de Biclustering, Classificação baseada em Biclustering e Prospecção de Regras de Associação para Classificação. Estes padrões (chamados de Meta-atributos) são compostos por subconjuntos de atributos discriminativos conjuntamente com os seus valores, permitindo assim distinguir e caracterizar subgrupos de doentes com padrões similares de apresentação da doença. Os Registos de Saúde Electrónicos (RSE) utilizados neste trabalho provêm do conjunto de dados JPND ONWebDUALS (ONTology-based Web Database for Understanding Amyotrophic Lateral Sclerosis), composto por questões standardizadas acerca de factores de risco, mutações genéticas, atributos clínicos ou informação de sobrevivência de uma coorte de doentes e controlos seguidos pelo consórcio ENCALS (European Network to Cure ALS), que inclui vários países europeus, incluindo Portugal. Nesta tese a metodologia proposta foi utilizada na parte portuguesa do conjunto de dados ONWebDUALS para encontrar padrões de apresentação da doença que: 1) distinguissem os doentes de ELA dos seus controlos e 2) caracterizassem grupos de doentes de ELA com diferentes taxas de progressão (categorizados em grupos Lentos, Neutros e Rápidos). Nenhum padrão coerente emergiu das experiências efectuadas para a primeira tarefa. Contudo, para a segunda tarefa os padrões encontrados para cada um dos três grupos de progressão foram reconhecidos e validados por clínicos especialistas em ELA, como sendo características relevantes de doentes com progressão Lenta, Neutra e Rápida. Estes resultados sugerem que a nossa abordagem genérica baseada em Biclustering tem potencial para identificar padrões de apresentação noutros problemas ou doenças semelhantes.Amyotrophic Lateral Sclerosis (ALS) is a heterogeneous neurodegenerative disease with a high variability of presentation patterns. Given the heterogeneous nature of ALS patients and targeting a better prognosis, clinicians usually estimate disease progression at diagnosis using the rate of decay computed from the Revised ALS Functional Rating Scale (ALSFRS-R). In this context, the use of Machine Learning models able to unravel the complexity of disease presentation patterns is paramount for disease understanding, targeting improved patient care and longer survival times. Furthermore, explainable models are vital, since clinicians must be able to understand the reasoning behind a given model’s result before making a decision that can impact a patient’s life. Therefore we aim at unravelling disease presentation patterns by proposing a new Data Mining approach called Discriminative Meta-features Discovery (DMD), which uses a combination of Biclustering, Biclustering-based Classification and Class Association Rule Mining. These patterns (called Metafeatures) are composed of discriminative subsets of features together with their values, allowing to distinguish and characterize subgroups of patients with similar disease presentation patterns. The Electronic Health Record (EHR) data used in this work comes from the JPND ONWebDUALS (ONTology-based Web Database for Understanding Amyotrophic Lateral Sclerosis) dataset, comprised of standardized questionnaire answers regarding risk factors, genetic mutations, clinical features and survival information from a cohort of patients and controls from ENCALS (European Network to Cure ALS), a consortium of diverse European countries, including Portugal. In this work the proposed methodology was used on the ONWebDUALS Portuguese EHR data to find disease presentation patterns that: 1) distinguish the ALS patients from their controls and 2) characterize groups of ALS patients with different progression rates (categorized into Slow, Neutral and Fast groups). No clear pattern emerged from the experiments performed for the first task. However, in the second task the patterns found for each of the three progression groups were recognized and validated by ALS expert clinicians, as being relevant characteristics of slow, neutral and fast progressing patients. These results suggest that our generic Biclustering approach is a promising way to unravel disease presentation patterns and could be applied to similar problems and other diseases

Data Mining Techniques in Gene Expression Data Analysis

Ph.DDOCTOR OF PHILOSOPH