Statistical Methods for Integrative Analysis, Subgroup Identification, and Variable Selection Using Cancer Genomic Data

In recent years, comprehensive cancer genomics platform, such as The Cancer Genome Atlas (TCGA), provides access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alteration, DNA methylation, and somatic mutation. Currently most existing analysis approaches focused only on gene-level analysis and suffered from limited interpretability and low reproducibility of findings. Additionally, with increasing availability of the modern compositional data including immune cellular fraction data and high-dimensional zero-inflated microbiome data, variable selection techniques for compositional data became of great interest because they allow inference of key immune cell types (immunology data) and key microbial species (microbiome data) associated with development and progression of various diseases. In the first dissertation aim, we address these challenges by developing a Bayesian sparse latent factor model for pathway-guided integrative genomic data analysis. Specifically, we constructed a unified framework to simultaneously identify cancer patient subgroups (clustering) and key molecular markers (variable selection) based on the joint analysis of continuous, binary and count data. In addition, we applied Polya-Gamma mixtures of normal for binary and count data to promote an exact and fully automatic posterior sampling. Moreover, pathway information was used to improve accuracy and robustness in identification of cancer patient subgroups and key molecular features. In the second dissertation aim, we developed the R package InGRiD , a comprehensive software for pathway-guided integrative genomic data analysis. We further implemented the statistical model developed in Aim 1 and provide it as a part of this software. The third dissertation aim exploits variable selection in compositional data analysis with application to immunology data and microbiome data. Specifically, we identified key immune cell types by applying a stepwise pairwise log-ratio procedure to the immune cellular fractions data, while selecting key species in the microbiome data by using zero-inflated Wilcoxon rank sum test. These approaches consider key components specific to these data types, such as compositionality (i.e., sum-to-one), zero inflation, and high dimensionality, among others. The proposed methods were developed and evaluated on: 1) large scale, high dimensional, and multi-modal datasets from the TCGA database, including gene expression, DNA copy number alteration, and somatic mutation data (Aim 1); 2) cellular fraction data induced from Colorectal Adenocarcinoma TCGA Pan-Cancer study (Aim 3); 3) high dimensional zero-inflated microbiome data from studies of colorectal cancer (Aim 3)

Colloidal III–V Nitride Quantum Dots

Colloidal quantum dots (QDs) have attracted intense attention in both fundamental studies and practical applications. To date, the size, morphology, and composition-controlled syntheses have been successfully achieved in II–VI semiconductor nanocrystals. Recently, III-nitride semiconductor quantum dots have begun to draw significant interest due to their promising applications in solid-state lighting, lasing technologies, and optoelectronic devices. The quality of nitride nanocrystals is, however, dramatically lower than that of II–VI semiconductor nanocrystals. In this review, the recent development in the synthesis techniques and properties of colloidal III–V nitride quantum dots as well as their applications are introduced

EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs

Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to achieve promising performance. As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment. The dataset and source code are publicly available to foster future research. Our work calls for more effective and practical embedding-based solutions to entity alignment.Comment: submitted to ISWC 202

Weighted AdaGrad with Unified Momentum

Integrating adaptive learning rate and momentum techniques into SGD leads to a large class of efficiently accelerated adaptive stochastic algorithms, such as Nadam, AccAdaGrad, \textit{etc}. In spite of their effectiveness in practice, there is still a large gap in their theories of convergences, especially in the difficult non-convex stochastic setting. To fill this gap, we propose \emph{weighted AdaGrad with unified momentum}, dubbed AdaUSM, which has the main characteristics that (1) it incorporates a unified momentum scheme which covers both the heavy ball momentum and the Nesterov accelerated gradient momentum; (2) it adopts a novel weighted adaptive learning rate that can unify the learning rates of AdaGrad, AccAdaGrad, Adam, and RMSProp. Moreover, when we take polynomially growing weights in AdaUSM, we obtain its $\mathcal{O}(\log(T)/\sqrt{T})$ convergence rate in the non-convex stochastic setting. We also show that the adaptive learning rates of Adam and RMSProp correspond to taking exponentially growing weights in AdaUSM, which thereby provides a new perspesctive for understanding Adam and RMSProp. Lastly, comparative experiments of AdaUSM against SGD with momentum, AdaGrad, AdaEMA, Adam, and AMSGrad on various deep learning models and datasets are also provided

Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.Comment: Accepted in the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2023

Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.Comment: Accepted in the 37th AAAI Conference on Artificial Intelligence (AAAI 2023