A philosophic account of quantum physics. The thesis is divided into two parts. Part I is dedicated to laying the groundwork of quantum physics, and explaining some of the primary difficulties. Subjects of interest will include the principle of locality, the quantum uncertainty principle, and Einstein's criterion for reality. Quantum dilemmas discussed include the double-slit experiment, observations of spin and polarization, EPR, and Bell's theorem. The first part will argue that mathematical-physical descriptions of the world fall short of explaining the experimental observations of quantum phenomenon. The problem, as will be argued, is framework of the physical descriptive schema. Part I includes in-depth discussions of mathematical principles. Part II will discuss the Copenhagen interpretation as put forth by its founders. The Copenhagen interpretation will be expressed as a paradox: The classical physical language cannot describe quantum phenomenon completely and with certainty, yet this language is the only possible method of articulating the physical world. The paradox of Copenhagen will segway into Kant's critique of metaphysics. Kant's understanding of causality, things-in-themselves, and a priori synthetic metaphysics. The thesis will end with a conclusion of the quantum paradox by juxtaposing anti-materialist Martin Heidegger with quantum founder Werner Heisenberg. Our conclusion will be primarily a discussion of how we understand the world, and specifically how our understanding of the world creates potential for truth.

miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics.

In this work we use a graph-theoretic representation of secondary RNA structure found in the database RAG: RNA-As-Graphs. We model the bonding of two RNA secondary structures to form a larger structure with a graph operation called merge. The resulting data from each tree merge operation is summarized and represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not based on the merge data vector. The network correctly assigned a high probability of RNA-likeness to trees identified as RNA-like in the RAG database, and a low probability of RNA-likeness to those classified as not RNA-like in the RAG database. We then used the neural network to predict the RNA-likeness of all the trees of order 9. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel.