Browsing by Author "Wagner, Mitchell J."
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- CS5604: Information and Storage Retrieval Fall 2016 - CMT (Collection Management Tweets)Wagner, Mitchell J.; Abidi, Faiz; Fan, Shuangfei (Virginia Tech, 2016-12-08)As the Collection Management Tweets team in the Fall 2016 CS5604 class, we were responsible for processing >1.2 billion tweets, including data transfer, noise reduction, tweet augmentation, and storage via several technologies. Our work was the first step in a pipeline that included many teams and ultimately culminated in a comprehensive information retrieval system. We were also responsible for building a social network (or set of networks) for those tweets, along with their tweeters. In this report, we detail our experience with this project. Additionally, we propose solutions for transferring incremental database updates from MySQL to HDFS and subsequently to HBase, derive a graph structure and relationships from entities identified in tweet collections, and offer a query-independent method for estimating the importance of those entities. We achieve these goals through the use of several open-source software packages, and present open, scalable solutions addressing the objectives we were given.
- Reconstructing signaling pathways using regular language constrained pathsWagner, Mitchell J.; Pratapa, Aditya; Murali, T. M. (2019-07-15)Motivation High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway. Results We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry. Availability and implementation https://github.com/Murali-group/RegLinker. Supplementary information Supplementary data are available at Bioinformatics online.