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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
portfolios
Series Anomaly Inference with Nonlinear Markov to Circumvent Walking a Tightrope
ANIM is a novel probabilistic module based on Nonlinear Markov for the unsupervised time series anomaly inference. 
Multimodal Anomalous Tissue Region Detection Enhanced with Spatial Transcriptomics
MEATRD is a multimodal anomaly detection method that integrates histology image and Spatial Transcriptomics gene expression data. 
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
ACSleuth is a GAN-based generative model for domain adaptive and fine-grained anomaly detection in the single-cell/tabular data. 
Detecting and Dissecting Anomalous Anatomic Regions in Spatial Transcriptomics with STANDS
STANDS is a GAN-based multi-task deep learning framework, which can detect and dissect anomalous tissue domains (DDATD) with Spatial Transcriptomics or scRNA-seq. 
publications
talks
Oral Academic Seminar about Our Work ACSleuth
Published:
We discussed how to detect anomalous samples in single-cell omics data with generative models and introduced our team’s work, ACSleuth.
Oral Academic Presentation about Our Work ACSleuth
Published:
We presented our team’s work, ACSleuth, discussing in detail its implementation specifics, applicable tasks, and the limitations of the algorithm.
Xiaoxiang Neurology Rare Disease Academic Salon
Published:
We introduced multimodal machine learning technologies for multi-omics data, and discussed how to discover rare diseases with deep learning methods.
Oral Academic Seminar about Our Work STANDS and ACSleuth
Published:
We discussed how to detect anomalous samples in multi-omics data with generative models and introduced our team’s work, ACSleuth and STANDS.
Oral Academic Presentation about Our Work ACSleuth
Published:
We formally introduced our work, Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond.