Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Spectral-spatial approaches for hyperspectral data classification

Classification of hyperspectral data is very challenging and mapping of land cover is one of its applications. Improving the classification accuracy and computation time of hyperspectral data were achieved incorporating contextual information in combination with spectral information for correcting classification errors along class boundaries and within class. In the proposed method, the original hyperspectral image was first classified using the Support Vector Machine (SVM) classifier, followed by the Markov Random Field (MRF) approach applied to the boundary areas and Unsupervised Extraction and Classification of Homogeneous Objects (UnECHO) classifier used for the interior parts of regions to produce the final classification map. In this study two agricultural (Hyperion and AVIRIS) and one urban (ROSIS) datasets were used. Investigations of the spectral and various contextual approaches including feature reduction show that the SVM-MRF method with grid search works best for all of the datasets. The highest overall accuracy of 97.35% was achieved for the urban dataset.Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of Lethbridge

Advances in the Diagnosis and Treatment of Thyroid Carcinoma

This reprint is related to the latest research in the field of thyroid surgery, including molecular and imaging diagnosis, surgical treatment, and the treatment of recurrent disease and advanced thyroid carcinoma

Development and application of bioinformatics tools for discovery disease markers and disease targeting antibodies

Ph.DDOCTOR OF PHILOSOPH

Alternations of microRNAs, the microbiome, and gut-host interactions in gastrointestinal diseases

Over the past few decades, an ageing population combined with a shift towards a Western lifestyle has predisposed many individuals towards inter-connected gastrointestinal (GI) diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), gastric cancer (GC) and Clostridioides difficile (C. difficile) infection (CDI). anti-TNF-α treatment for IBD patients has a high unresponsive rate, by using bioinformatics approaches, I identified neutrophil chemotaxis may contribute to the treatment resistance and IL13RA2 is the best predictor to identify treatment unresponsive patients. On the other hand, in the intestinal tract, colonocytes consistently exfoliate and shed into the lumen, affecting gut microbiota composition. These molecular/microbial changes involved in disease pathogenesis can be detected in faeces. By using Taqman probe-based real-time polymerase chain reaction (RT qPCR) assay, several non-coding microRNAs (such as miR-18a, miR-20a, miR-221 and miR 135b) and gut microbes (including Fusobacterium nucleatum, Parvimonas micra, Gemella morbillorum, Peptostreptococcus anaerobius, Clostridium hathewayi and Lachnoclostrium sp.) are highly expressed/enriched in faeces in CRC individuals compared to control subjects. The use of a faecal immunological test (FIT) in combination with these biomarkers may improve the non-invasive CRC screening accuracy. Furthermore, Epstein-Barr virus (EBV) is an oncogenic virus and EBV-driven GC accounts for roughly 10% of total GC cases. GC cells infected with EBV alter the molecular aspect at whole-genome, transcriptome, and epigenome levels. For instance, AKT2 activated by mutation in EBV-positive GC cells affecting downstream MAPK and focal adhesion signalling pathways; AKT2 mutation associates with poor patient survival in EBV-positive GC. Furthermore, once patients have received GI treatments, it may suppress/interfere with the patients’ immune system, disrupt the gut flora homeostasis and trigger CDI. Faecal microbiota transplantation (FMT) has been demonstrated as an effective and alternative treatment strategy for CDI patients. However, it is still in clinical trials due to safety concerns. My study revealed that serum miRNAs such as miR-23a-3p, miR-150-5p, miR-26b-5p and miR-28-5p could be used to monitor FMT treatment in CDI patients, and these markers inversely correlate with IL-12B, IL-18, FGF21 and TNFSRF9 at serum protein and mRNA levels, respectively. Furthermore, miR-23a and miR-150 showed cytoprotective effects against C. difficile Toxin B (TcdB)