Browsing by Author "Tyagi, Tanya"

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    ACM Venue Recommendation System
    Kumar, Harinni Kodur; Tyagi, Tanya (Virginia Tech, 2019-12-23)
    A frequent goal of a researcher is to publish his/her work in appropriate conferences and journals. With a large number of options for venues in the microdomains of every research discipline, the issue of selecting suitable locations for publishing cannot be underestimated. Further, the venues diversify themselves in the form of workshops, symposiums, and challenges. Several publishers such as IEEE and Springer have recognized the need to address this issue and have developed journal recommenders. In the proposed project, the goal is to design and develop similar a recommendation system for the ACM dataset. The conventional approach to building such a recommendation system is to utilize the content features in a dataset through content-based and collaborative approaches and proffer suggestions. An alternative is to view this recommendation problem from a classification perspective. With the success of deep learning classifiers in recent times and their pervasiveness in several domains, our goal is to solve the problem of recommending conference and journal venues by incorporating deep learning methodologies given some information about the submission like title, keywords, abstract, etc. The dataset used for the project is the ACM Digital Library metadata that includes metadata and textual information for research papers and journals submitted at various conferences and journals over the past 60 years. Our current system offers recommendations based on 80 binary classifiers. From our results, we could observe that for past submissions, our system recommends ground truth venues precisely. In the subsequent iterations of the project, we aim to improve the performance of individual classifiers and thereby offer better recommendations.
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    Benchmarking Methods For Predicting Phenotype Gene Associations
    Tyagi, Tanya (Virginia Tech, 2020-09-16)
    Assigning human genes to diseases and related phenotypes is an important topic in modern genomics. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities that occur in human diseases. Computational methods such as label-propagation and supervised-learning address challenges posed by traditional approaches such as manual curation to link genes to phenotypes in the HPO. It is only in recent years that computational methods have been applied in a network-based approach for predicting genes to disease-related phenotypes. In this thesis, we present an extensive benchmarking of various computational methods for the task of network-based gene classification. These methods are evaluated on multiple protein interaction networks and feature representations. We empirically evaluate the performance of multiple prediction tasks using two evaluation experiments: cross-fold validation and the more stringent temporal holdout. We demonstrate that all of the prediction methods considered in our benchmarking analysis have similar performance, with each of the methods outperforming a random predictor.