Generative topic modeling in image data mining and bioinformatics studies

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model is proposed for co-existing image features and annotations, in which new latent variables are introduced to allow for more flexible sampling of word topics and visual topics. A perspective hierarchical Dirichlet process (pHDP) model is proposed to deal with user-tagged image modeling, associating image features with image tags and incorporating the user’s perspectives into the image tag generation process. It’s also shown that in mining large scale text corpora of natural language descriptions, the relation between semantic visual attributes and object categories can be encoded as Must-Links and Cannot-Links, which can be represented by Dirichlet-Forest prior. Novel generative topic models are also introduced to meta-genomics studies. The experimental results show that the generative topic model can be used to model the taxon abundance information obtained by the homology-based approach and study the microbial core. It also shows that latent topic modeling can be used to characterize core and distributed genes within a species and to correlate similarities between genes and their functions. A further study on the functional elements derived from the non-redundant CDs catalogue shows that the configuration of functional groups encoded in the gene-expression data of meta-genome samples can be inferred by applying probabilistic topic modeling to functional elements. Furthermore, an extended HDP model is introduced to infer functional basis from detected enterotypes. The latent topics estimated from human gut microbial samples are evidenced by the recent discoveries in fecal microbiota study, which demonstrate the effectiveness of the proposed models.Ph.D., Information Systems -- Drexel University, 201

Diet and Microbiome in Health and Aging

Diet plays a fundamental role in shaping the composition and metabolic activity of the gut microbiota and, thus, it could determine the interrelationship between the gut microbiome and the host. The colon is the part of the human body that is most densely populated, containing bacteria, archaea, viruses, and some unicellular eukaryotes that have co-evolved with humans in a commensal way. The gut microbiota plays a crucial role in the maintenance of normal host physiology. The rapid development of next-generation sequencing (NGS) methods for DNA sequencing in the last decade has facilitated in-depth study of gut microbiome composition and function. These methods have contributed to providing evidence regarding the relevance of the intestinal microbiota for host health as well as the basis for putative dietary interventions aimed at counteracting microbiota dysbiosis. Understanding the complex and dynamic interaction between dietary exposures and gut microbiota can help to elucidate their potential role in different pathologies and to guide future strategies for the prevention and treatment of diseases. Age-related changes in the gut microbiome are also associated with physiological changes in the gastrointestinal tract as well as in dietary patterns, with a concomitant decline in the normal function of the immune system that may contribute to increased risk of infection and frailty. More studies are needed to better understand how the microbiota shifts with different environmental factors and how they are associated with dietary changes

Reticulate Evolution: Symbiogenesis, Lateral Gene Transfer, Hybridization and Infectious heredity

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