SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variable

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Background: Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure. Results: The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective. Conclusions: It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.Ministerio de Economía y Competitividad TIN2014-55894-C2-

Preparation and characterization of magnetite (Fe3O4) nanoparticles By Sol-Gel method

The magnetite (Fe3O4) nanoparticles were successfully synthesized and annealed under vacuum at different temperature. The Fe3O4 nanoparticles prepared via sol-gel assisted method and annealed at 200-400ºC were characterized by Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Diffraction spectra (XRD), Field Emission Scanning Electron Microscope (FESEM) and Atomic Force Microscopy (AFM). The XRD result indicate the presence of Fe3O4 nanoparticles, and the Scherer`s Formula calculated the mean particles size in range of 2-25 nm. The FESEM result shows that the morphologies of the particles annealed at 400ºC are more spherical and partially agglomerated, while the EDS result indicates the presence of Fe3O4 by showing Fe-O group of elements. AFM analyzed the 3D and roughness of the sample; the Fe3O4 nanoparticles have a minimum diameter of 79.04 nm, which is in agreement with FESEM result. In many cases, the synthesis of Fe3O4 nanoparticles using FeCl3 and FeCl2 has not been achieved, according to some literatures, but this research was able to obtained Fe3O4 nanoparticles base on the characterization results

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

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed