Browsing by Author "Zhang, Qi"

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    Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes
    Yu, Haipeng; Campbell, Malachy T.; Zhang, Qi; Walia, Harkamal; Morota, Gota (Genetics Society of America, 2019-06-01)
    With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.
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    MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation
    Naseem, Usman; Thapa, Surendrabikram; Zhang, Qi; Hu, Liang; Nasim, Mehwish (ACM, 2023-07-19)
    Medical dialogue systems (MDS) have shown promising abilities to diagnose through a conversation with a patient like a human doctor would. However, current systems are mostly based on sequence modeling, which does not account for medical knowledge. This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. Our approach relies on a medical knowledge graph to extract disease-symptom relationships and uses a dynamic graph-based meta-learning framework to learn how to evolve the given knowledge graph to reason about disease-symptom correlations. Our approach incorporates medical knowledge and hence reduces the need for a large number of dialogues. Evaluations show that our system outperforms existing approaches when tested on benchmark datasets.
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    Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
    Guo, Zhen; Zhang, Qi; An, Xinwei; Zhang, Qisheng; Josang, Audun; Kaplan, Lance M.; Chen, Feng; Jeong, Dong H.; Cho, Jin-Hee (2023-02-13)
    Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95% multi-class accuracy.