Estimation of circadian parameters and investigation in cyanobacteria via semiparametric varying coefficient periodic models

This dissertation includes three components. Component 1 provides an estima- tion procedure for circadian parameters in cyanobacteria. Component 2 explores the relationship between baseline and amplitude by model selection under the framework of smoothing spline. Component 3 investigates properties of hypothesis testing. The following three paragraphs briefly summarize these three components, respectively. Varying coefficient models are frequently used in statistical modeling. We pro- pose a semiparametric varying coefficient periodic model which is suitable to study periodic patterns. This model has ample applications in the study of the cyanobac- teria circadian clock. To achieve the desired flexibility, the model we consider may not be globally identifiable. We propose to perform local approximations by kernel based methods and focus on estimating one solution that is biologically meaningful. Asymptotic properties are developed. Simulations show that the gain by our proce- dure over the commonly used method is substantial. The methodology is illustrated by an application to a cyanobacteria dataset. Smoothing spline can be implemented, but a direct application with the penalty selected by the generalized cross-validation often leads to non-convergence outcomes. We propose an adjusted cross-validation instead, which resolves the difficulties. Biol- ogists believe that the amplitude function of the periodic component is proportional to the baseline function. To verify this belief, we propose a full model without any assumptions regarding such a relationship, and two reduced models with the ratio of baseline and amplitude to be a constant and a quadratic function of time, respectively. We use model selection techniques, Akaike information criterion (AIC) and Schwarz Bayesian information criterion (BIC), to determine the optimal model. Simulations show that AIC and BIC select the correct model with high probabilities. Application to cyanobacteria data shows that the full model is the best model. To investigate the same problem in component 2 by a formal hypothesis testing procedure, we develop kernel based methods. In order to construct the test statistic, we derive the global degree of freedom for the residual sum of squares. Simulations show that the proposed tests perform well. We apply the proposed procedures to the data and conclude that the baseline and amplitude functions share no linear or quadratic relationship

Estimation of circadian parameters and investigation in cyanobacteria via semiparametric varying coefficient periodic models

This dissertation includes three components. Component 1 provides an estima- tion procedure for circadian parameters in cyanobacteria. Component 2 explores the relationship between baseline and amplitude by model selection under the framework of smoothing spline. Component 3 investigates properties of hypothesis testing. The following three paragraphs briefly summarize these three components, respectively. Varying coefficient models are frequently used in statistical modeling. We pro- pose a semiparametric varying coefficient periodic model which is suitable to study periodic patterns. This model has ample applications in the study of the cyanobac- teria circadian clock. To achieve the desired flexibility, the model we consider may not be globally identifiable. We propose to perform local approximations by kernel based methods and focus on estimating one solution that is biologically meaningful. Asymptotic properties are developed. Simulations show that the gain by our proce- dure over the commonly used method is substantial. The methodology is illustrated by an application to a cyanobacteria dataset. Smoothing spline can be implemented, but a direct application with the penalty selected by the generalized cross-validation often leads to non-convergence outcomes. We propose an adjusted cross-validation instead, which resolves the difficulties. Biol- ogists believe that the amplitude function of the periodic component is proportional to the baseline function. To verify this belief, we propose a full model without any assumptions regarding such a relationship, and two reduced models with the ratio of baseline and amplitude to be a constant and a quadratic function of time, respectively. We use model selection techniques, Akaike information criterion (AIC) and Schwarz Bayesian information criterion (BIC), to determine the optimal model. Simulations show that AIC and BIC select the correct model with high probabilities. Application to cyanobacteria data shows that the full model is the best model. To investigate the same problem in component 2 by a formal hypothesis testing procedure, we develop kernel based methods. In order to construct the test statistic, we derive the global degree of freedom for the residual sum of squares. Simulations show that the proposed tests perform well. We apply the proposed procedures to the data and conclude that the baseline and amplitude functions share no linear or quadratic relationship

Bidirectional Learning for Offline Infinite-width Model-based Optimization

In offline model-based optimization, we strive to maximize a black-box objective function by only leveraging a static dataset of designs and their scores. This problem setting arises in numerous fields including the design of materials, robots, DNA sequences, and proteins. Recent approaches train a deep neural network (DNN) on the static dataset to act as a proxy function, and then perform gradient ascent on the existing designs to obtain potentially high-scoring designs. This methodology frequently suffers from the out-of-distribution problem where the proxy function often returns poor designs. To mitigate this problem, we propose BiDirectional learning for offline Infinite-width model-based optimization (BDI). BDI consists of two mappings: the forward mapping leverages the static dataset to predict the scores of the high-scoring designs, and the backward mapping leverages the high-scoring designs to predict the scores of the static dataset. The backward mapping, neglected in previous work, can distill more information from the static dataset into the high-scoring designs, which effectively mitigates the out-of-distribution problem. For a finite-width DNN model, the loss function of the backward mapping is intractable and only has an approximate form, which leads to a significant deterioration of the design quality. We thus adopt an infinite-width DNN model, and propose to employ the corresponding neural tangent kernel to yield a closed-form loss for more accurate design updates. Experiments on various tasks verify the effectiveness of BDI. The code is available at https://github.com/GGchen1997/BDI.Comment: NeurIPS2022 camera-ready version; AI4Science; Drug discovery; Offline model-based optimization; Neural tangent kernel; Bi-level optimizatio

PROTOPANAXADIOL SAPONINS IN THE CAUDEXES AND LEAVES OF PANAX NOTOGINSENG COULD BE THE MAIN CONSTITUENTS THAT CONTRIBUTE TO ITS ANTIDEPRESSANT EFFECTS

Objective: We previously found that total saponins, purified from the caudexes and leaves of Panax notoginseng (SCLPN), had antidepressant effects. In the present study, we investigated saponin monomers of SCLPN that may be the main constituents that contribute to the antidepressant effects of SCLPN. Methods: Three effective fractions of SCLPN, purified using a macroporous resin method, at doses of 50 and 100 mg/kg were tested in four different animal models of stress, including the learned helplessness test, tail suspension test, forced swim test, open field test, and reserpine-induced syndrome model. Using the same models of stress and the same doses, we then evaluated the antidepressant effects of four main and representative saponin monomers (ginsenosides Rd, Rb1 and Rg1 and notoginsenoside R1) in different effective fractions. We also examined the effects of Rd and Rb3 on monoamine neurotransmitter levels. To investigate the biotransformation of Rb1 and Rb3 orally administered in mice, Rb1 and Rb3 metabolites in blood and brain were determined by high-performance liquid chromatography. Results: Effective fraction A and C exerted greater antidepressant effects than fraction B in the behavioral tests and reserpine-induced syndrome model. Among the four saponin monomers, Rd had the strongest antidepressant effects, which improved depressive-like behavior in all four animal models of depression. We then found that Rb3 (50 and 100 mg/kg) and Rd (100 mg/kg) increased the levels of 5-hydroxytryptamine, dopamine, and norepinephrine, whereas 50 mg/kg Rd had no effect on the levels of these three neurotransmitters. Ginsenoside Rh2, C-K, and 20 (S)-protopanaxadiol saponins were detected in blood samples from mice that received Rb1 and Rb3, and protopanaxadiol saponins were found in the brain. Conclusion: The present results indicate that protopanaxadiol saponins in SCLPN have potential antidepressant-like effects

Knowledge-Enhanced Top-K Recommendation in Poincar\'e Ball

Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Memory Augmented Graph Neural Networks for Sequential Recommendation

The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difficulty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Specifically, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI 2020

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user. However, most of these methods treat all the training triplets equally, which ignores the subtle difference between different positive or negative items. On the other hand, even though some other works make use of the auxiliary information (e.g., dwell time) of user behaviors to capture this subtle difference, such auxiliary information is hard to obtain. To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets. We devise two strategies for the importance score generation and formulate the whole procedure as a bilevel optimization, which does not require any rule-based design. We integrate the proposed training procedure with several Matrix Factorization (MF)- and Graph Neural Network (GNN)-based recommendation models, demonstrating the compatibility of our framework. Via a comparison using three real-world datasets with many state-of-the-art methods, we show that our proposed method outperforms the best existing models by 3-21\% in terms of Recall@k for the top-k recommendation