Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Computer-Mediated Communication and Ecclesiological Challenges To and From the Reformed Tradition

Communities of faith have appeared online since the inception of computer -­ mediated communication (CMC)and are now ubiquitous. Yet the character and legitimacy of Internet communities as ecclesial bodies is often disputed by traditional churches; and the Internet's ability to host the church as church for online Christians remains a question. This dissertation carries out a practical theological conversation between three main sources: the phenomenon of the church online; ecclesiology (especially that characteristic of Reformed communities); and communication theory. After establishing the need for this study in Chapter 1, Chapter 2 investigates the online presence of Christians and trends in their Internet use, including its history and current expressions. Chapter 3 sets out an historical overview of the Reformed Tradition, focusing on the work of John Calvin and Karl Barth, as well as more contemporary theologians. With a theological context in which to consider online churches in place, Chapter 4 introduces four theological themes prominent in both ecclesiology and CMC studies: authority; community; mediation; and embodiment. These themes constitute the primary lens through which the dissertation conducts a critical-­confessional interface between communication theory and ecclesiology in the examination of CMC. Chapter 5 continues the contextualization of online churches with consideration of communication theories that impact CMC, focusing on three major communication theories: Narrative Theory; Interpretive Theory; and Speech Act Theory. Chapter 6 contains the critical conversation between ecclesiology and communication theory by correlating the aforementioned communication theories with Narrative Theology, Communities of Practice, and Theo-­Drama, and applying these to the four theological themes noted above. In addition, new or anticipated developments in CMC investigated in relationship to traditional ecclesiologies and the prospect of cyber-­ecclesiology. Chapter 7 offers an evaluative tool consisting of a three-­step hermeneutical process that examines: 1) the history, tradition, and ecclesiology of the particular community being evaluated; 2) communication theories and the process of religious-­social shaping of technology; and 3) CMC criteria for establishing the presence of a stable, interactive, and relational community. As this hermeneutical process unfolds, it holds the church at the center of the process, seeking a contextual yet faithful understanding of the church.

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.