Scalable Probabilistic Model Selection for Network Representation Learning in Biological Network Inference

A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. Although the biological networks not only provide an elegant theoretical framework but also offer a mathematical foundation to analyze, understand, and learn from complex biological systems, the reconstruction of biological networks is an important and unsolved problem. Current biological networks are noisy, sparse and incomplete, limiting the ability to create a holistic view of the biological reconstructions and thus fail to provide a system-level understanding of the biological phenomena. Experimental identification of missing interactions is both time-consuming and expensive. Recent advancements in high-throughput data generation and significant improvement in computational power have led to novel computational methods to predict missing interactions. However, these methods still suffer from several unresolved challenges. It is challenging to extract information about interactions and incorporate that information into the computational model. Furthermore, the biological data are not only heterogeneous but also high-dimensional and sparse presenting the difficulty of modeling from indirect measurements. The heterogeneous nature and sparsity of biological data pose significant challenges to the design of deep neural network structures which use essentially either empirical or heuristic model selection methods. These unscalable methods heavily rely on expertise and experimentation, which is a time-consuming and error-prone process and are prone to overfitting. Furthermore, the complex deep networks tend to be poorly calibrated with high confidence on incorrect predictions. In this dissertation, we describe novel algorithms that address these challenges. In Part I, we design novel neural network structures to learn representation for biological entities and further expand the model to integrate heterogeneous biological data for biological interaction prediction. In part II, we develop a novel Bayesian model selection method to infer the most plausible network structures warranted by data. We demonstrate that our methods achieve the state-of-the-art performance on the tasks across various domains including interaction prediction. Experimental studies on various interaction networks show that our method makes accurate and calibrated predictions. Our novel probabilistic model selection approach enables the network structures to dynamically evolve to accommodate incrementally available data. In conclusion, we discuss the limitations and future directions for proposed works

The embedding of transnational entrepreneurs in diaspora networks:Leveraging the assets of foreignness

In this paper we examine how foreign actors capitalize on their ethnic identity to gain skills and capabilities that enable them to operate in a new and strange environment. We explore the mechanisms by which Bulgarian entrepreneurs in London use their ethnic identity to develop competitive advantage and business contacts. We find that the entrepreneurs studied gain access to a diaspora network, which enables them to develop essential business capabilities and integrate knowledge from both home and host country environments. The diaspora community possesses a collective asset (transactive memory) that allows its members to remove competition from the interfirm level to the network level (i.e., diaspora networks vs. networks of native businesspeople). Additionally, the cultural identity and networks to which community members have access provide bridging capabilities that allow diaspora businesspeople to make links to host country business partners and thus embed themselves in the host country environment. Thus, this paper adds to the growing body of work showing how foreignness can serve as an asset in addition to its better-known role as a liability