DOI

10.17077/etd.4eskij3m

Document Type

Dissertation

Date of Degree

Fall 2017

Degree Name

PhD (Doctor of Philosophy)

Degree In

Biomedical Engineering

First Advisor

Michael A. Mackey

First Committee Member

Isabel K Darcy

Second Committee Member

Michael J Schnieders

Third Committee Member

Alberto M Segre

Fourth Committee Member

Sarah C Vigmostad

Abstract

This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.

Keywords

Artificial General Intelligence, Complex Adaptive Systems, Computational Systems Biology, General-Purpose Learning, Probabilistic Generative Models, Simplicial Grammar

Pages

x, 129 pages

Bibliography

Includes bibliographical references (pages 115-129).

Comments

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Copyright

Copyright © 2017 John I Kalantari

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