Person:

Zollanvari, Amin

The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts—rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well.

Motivation: This work constructs a closed loop Bayesian Network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases. Results: An automated pipeline was successfully constructed. Integrative models were made based on gene expression data obtained from GEO experiments relating to four different diseases using Bayesian statistical methods. Many of these models demonstrated a high level of accuracy and predictive ability. The approach described in this paper can be applied to any complex disorder and can include any number and type of genome-scale studies.

We introduce a principled computational framework and methodology for automated discovery of context-specific functional links between ontologies. Our model leverages over disparate free-text literature resources to score the model of dependency linking two terms under a context against their model of independence. We identify linked terms as those having a significant bayes factor (p < 0.01). To scale our algorithm over massive ontologies, we propose a heuristic pruning technique as an efficient algorithm for inferring such links. We have applied this method to translationalize Gene Ontology to all other ontologies available at National Center of Biomedical Ontology (NCBO) BioPortal under the context of Human Disease ontology. Our results show that in addition to broadening the scope of hypothesis for researchers, our work can potentially be used to explore continuum of relationships among ontologies to guide various biological experiments.

Classification has emerged as a major area of investigation in bioinformatics owing to the desire to discriminate phenotypes, in particular, disease conditions, using high-throughput genomic data. While many classification rules have been posed, there is a paucity of error estimation rules and an even greater paucity of theory concerning error estimation accuracy. This is problematic because the worth of a classifier depends mainly on its error rate. It is common place in bioinformatics papers to have a classification rule applied to a small labeled data set and the error of the resulting classifier be estimated on the same data set, most often via cross-validation, without any assumptions being made on the underlying feature-label distribution. Concomitant with a lack of distributional assumptions is the absence of any statement regarding the accuracy of the error estimate. Without such a measure of accuracy, the most common one being the root-mean-square (RMS), the error estimate is essentially meaningless and the worth of the entire paper is questionable. The concomitance of an absence of distributional assumptions and of a measure of error estimation accuracy is assured in small-sample settings because even when distribution-free bounds exist (and that is rare), the sample sizes required under the bounds are so large as to make them useless for small samples. Thus, distributional bounds are necessary and the distributional assumptions need to be stated. Owing to the epistemological dependence of classifiers on the accuracy of their estimated errors, scientifically meaningful distribution-free classification in high-throughput, small-sample biology is an illusion.