Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes

Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.&nbsp

Exploring Strategies to Integrate Disparate Bioinformatics Datasets

Distinct bioinformatics datasets make it challenging for bioinformatics specialists to locate the required datasets and unify their format for result extraction. The purpose of this single case study was to explore strategies to integrate distinct bioinformatics datasets. The technology acceptance model was used as the conceptual framework to understand the perceived usefulness and ease of use of integrating bioinformatics datasets. The population of this study included bioinformatics specialists of a research institution in Lebanon that has strategies to integrate distinct bioinformatics datasets. The data collection process included interviews with 6 bioinformatics specialists and reviewing 27 organizational documents relating to integrating bioinformatics datasets. Thematic analysis was used to identify codes and themes related to integrating distinct bioinformatics datasets. Key themes resulting from data analysis included a focus on integrating bioinformatics datasets, adding metadata with the submitted bioinformatics datasets, centralized bioinformatics database, resources, and bioinformatics tools. I showed throughout analyzing the findings of this study that specialists who promote standardizing techniques, adding metadata, and centralization may increase efficiency in integrating distinct bioinformatics datasets. Bioinformaticians, bioinformatics providers, the health care field, and society might benefit from this research. Improvement in bioinformatics affects poistevely the health-care field which has a positive social change. The results of this study might also lead to positive social change in research institutions, such as reduced workload, less frustration, reduction in costs, and increased efficiency while integrating distinct bioinformatics datasets

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

Інформаційна технологія побудови розподілених сховищ даних гібридного типу

У дисертаційній роботі вирішено актуальне науково-практичне завдання створення інформаційної технології побудови розподілених сховищ даних гібридного типу з врахуванням властивостей даних і статистики виконання запитів до сховища. Здійснено аналіз проблеми побудови сховищ даних з врахуванням властивостей даних і виконуваних запитів, обґрунтовано актуальність вирішення цієї проблеми. Визначено вимоги до інформаційної технології побудови розподілених сховищ гібридного типу. Введено поняття мультибазових сховищ даних, розроблено концептуальну, логічну та фізичну моделі таких сховищ і процедури міжрівневих переходів. Описано інтеграцію даних у сховище за допомогою процедур перетворення елементів даних і операцій, вибору моделей представлення даних. Розташування даних по вузлах, маршрути реплікації даних визначаються за критерієм мінімальної сукупної вартості збереження та обробки даних з використанням модифікованого генетичного алгоритму. На основі запропонованих моделей і методів створено інформаційну технологію побудови розподілених сховищ гібридного типу, яка вирішує поставлене наукове завдання. Зазначена технологія застосована при розробленні інформаційних та інформаційно-аналітичних систем Міністерства фінансів України. Результати впровадження підтвердили її відповідність поставленим вимогам