ASXL1 interacts with the cohesin complex to maintain chromatid separation and gene expression for normal hematopoiesis

ASXL1 is frequently mutated in a spectrum of myeloid malignancies with poor prognosis. Loss of Asxl1 leads to myelodysplastic syndrome-like disease in mice; however, the underlying molecular mechanisms remain unclear. We report that ASXL1 interacts with the cohesin complex, which has been shown to guide sister chromatid segregation and regulate gene expression. Loss of Asxl1 impairs the cohesin function, as reflected by an impaired telophase chromatid disjunction in hematopoietic cells. Chromatin immunoprecipitation followed by DNA sequencing data revealed that ASXL1, RAD21, and SMC1A share 93% of genomic binding sites at promoter regions in Lin-cKit+ (LK) cells. We have shown that loss of Asxl1 reduces the genome binding of RAD21 and SMC1A and alters the expression of ASXL1/cohesin target genes in LK cells. Our study underscores the ASXL1-cohesin interaction as a novel means to maintain normal sister chromatid separation and regulate gene expression in hematopoietic cells

Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning

Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic scenes and limited visual cues, it is of great challenge to predict how long there will be an accident from early observed frames. Most existing approaches are developed to learn features of accident-relevant agents for accident anticipation, while ignoring the features of their spatial and temporal relations. Besides, current deterministic deep neural networks could be overconfident in false predictions, leading to high risk of traffic accidents caused by self-driving systems. In this paper, we propose an uncertainty-based accident anticipation model with spatio-temporal relational learning. It sequentially predicts the probability of traffic accident occurrence with dashcam videos. Specifically, we propose to take advantage of graph convolution and recurrent networks for relational feature learning, and leverage Bayesian neural networks to address the intrinsic variability of latent relational representations. The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features. In addition, we collect a new Car Crash Dataset (CCD) for traffic accident anticipation which contains environmental attributes and accident reasons annotations. Experimental results on both public and the newly-compiled datasets show state-of-the-art performance of our model. Our code and CCD dataset are available at https://github.com/Cogito2012/UString.Comment: Accepted by ACM MM 202

Bayesian Learning with Heterogeneous Data for Life Sciences

We propose a suite of Bayesian learning methods to address challenges arising from task and data heterogeneity in life science applications. First, we develop a novel multi-domain negative binomial (NB) factorization model to analyze next-generation sequencing (NGS) count data, with the goal of enhancing cancer subtyping in the target domain with a limited number of NGS samples by leveraging surrogate data from other cancer types (source domains). In particular, such a Bayesian multi-domain learning (BMDL) method addresses data scarcity issues due to task heterogeneity by learning domain relevance through common latent factors based on given samples across domains. It automatically avoids ``negative transfer'', to which many existing transfer learning methods are amenable, and performs consistently better than single-domain learning regardless of the domain relevance level. In addition to study task heterogeneity, investigating longitudinal heterogeneity of temporal NGS count data may help to better understand the underlying cellular mechanisms of living systems. We propose gamma Markov negative binomial (GMNB) as a fully Bayesian solution to study temporal RNA-seq data. A notable advantage is the capacity to capture a broad range of gene expression patterns over time by integrating a gamma Markov chain into the NB distribution model. We then adopt the Bayes Factor (BF) as a measure that exploits information collectively from all time points to detect the genes with significant variations in temporal expression patterns across phenotypes or treatment conditions. Moving to more complicated experimental settings, we propose variational graph recurrent neural network (VGRNN) that combines additional structural heterogeneity to the longitudinal data. The use of high-level latent random variables in VGRNN can better capture potential variability observed in dynamic graphs as well as the uncertainty of node latent representations, with graphs capturing prior knowledge on dependency relationships. We further develop semi-implicit variational inference for this new VGRNN architecture (SI-VGRNN) to allow flexible non-Gaussian latent representations. Finally, in the last chapter, we propose a novel Bayesian relation learning framework, BayReL, that infers interactions across different heterogeneous input datasets as different views from different types of bio-molecules, aiming at deriving meaningful biological knowledge for integrative multi-omics data analysis. BayReL can flexibly incorporate the available graph dependency structure of each view, exploits non-linear transformations, and provides probabilistic interpretation simultaneously