Application of Biomedical Text Mining

With the enormous volume of biological literature, increasing growth phenomenon due to the high rate of new publications is one of the most common motivations for the biomedical text mining. Aiming at this massive literature to process, it could extract more biological information for mining biomedical knowledge. Using the information will help understand the mechanism of disease generation, promote the development of disease diagnosis technology, and promote the development of new drugs in the field of biomedical research. Based on the background, this chapter introduces the rise of biomedical text mining. Then, it describes the biomedical text-mining technology, namely natural language processing, including the several components. This chapter emphasizes the two aspects in biomedical text mining involving static biomedical information recognization and dynamic biomedical information extraction using instance analysis from our previous works. The aim is to provide a way to quickly understand biomedical text mining for some researchers

Gene Discovery in Mendelian and Complex Diseases

Through the Finding of Rare Disease Genes in Canada (FORGE Canada) initiative, individuals affected with rare Mendelian diseases were clinically ascertained with a goal of identifying the genetic origin of their disease. Herein, I describe the methods for identifying the genetic basis of four Mendelian diseases. The application of next generation sequencing led to the discovery of non-synonymous variation in the DNA of individuals affected by rare diseases. The effects of the candidate variants were assessed using a series of functional experiments to complement the human genetics data. The variants observed in patients’ cells are extremely rare, were consistently predicted to be pathogenic by multiple in silico predictive programs, segregated with disease status in the family, and affected the biological properties of their respective gene products, as measured by functional assays. Having successfully identified genetic variants underlying the Mendelian diseases, we sought to use the same approach to extract the genetic variation that may predispose individuals to complex diseases, primarily neurodegenerative disorders. We designed a neurodegeneration specific gene panel that utilizes next generation sequencing chemistry. We sequenced patients diagnosed with one of five neurodegenerative diseases: 1) Alzheimer’s disease; 2) amyotrophic lateral sclerosis (ALS); 3) frontotemporal dementia (FTD); 4) Parkinson’s disease; or 5) vascular cognitive impairment, as part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI). We were successful in detecting rare variants in a large fraction of cases that may be related to the neurodegenerative phenotypes. We also independently ascertained three large, unique families affected with familial ALS and FTD across multiple generations. The three families are diagnosed with ALS and/or FTD and in which the same hexanucleotide repeat expansion in the C9orf72 gene has been observed in all three pedigrees, but their phenotypes vary significantly. We sequenced affected individuals and observed several, distinct variants in these families that may explain the additional neurodegenerative phenotypes observed. In summary, the application of next generation sequencing has successfully identified novel genetic loci in both Mendelian and complex diseases

Metabolic and Blood Flow Properties of Functional Brain Networks Using Human Multimodal Neuroimaging

The brain has a high energetic cost to support neuronal activity, requiring both oxygen and glucose supply from the cerebral vascular system. Additionally, the brain functions through complex patterns of interconnectivity between neuronal assemblies giving rise to functional network architectures that can be investigated across multiple spatial scales. Different brain regions have different roles and importance within these network architectures, with some regions exhibiting more global importance by being involved in cross-network communication while other being predominantly involved in local connections. There are indications that regions exhibiting a more global role in inter networks connectivity are characterized by a higher and more efficient metabolic profile, leading to differences in metabolic properties when compared to more locally connected regions. Understanding the link between oxygen/glucose metabolism and functional features of brain network architectures, across different spatial scales, is of primary importance. This thesis consists of three original studies combining human brain resting-state multimodal neuroimaging and transcriptional data to investigate the glucose/oxygen metabolic costs of brain functional connectivity. We quantified glucose metabolism from positron emission tomography, and oxygen metabolism and functional connectivity from magnetic resonance imaging. In the first study, we highlight how the oxygen/glucose metabolism of brain regions can non-linearly relate to their functional hubness, within the resting-state networks of the brain across a nested hierarchy. We found that an increase in oxygen/glucose metabolism is associated with a non-linear increase in functional hubness where increase rates are both network- and scale-dependent. In the second study, we show specific transcriptional signatures that characterize the oxygen/glucose metabolic costs of regions involved in network global versus local centrality. This study highlights the different metabolic profiles of local and global regions, with gene expression related to oxidative metabolism and synaptic pathways being enriched in association with spatial patterns in common with resting blood flow and metabolism (oxygen and glucose) and globally-connected regions. In the third study, we demonstrate that there are oxygen/glucose metabolic costs to the functional integration and segregation of resting-state networks. We highlight that the metabolic costs of functional integration could reflect the hierarchical organization of the brain from unimodal to transmodal regions

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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