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In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
View Article and Find Full Text PDFA Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.
Stat Med
February 2025
Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas.
Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated microbiome studies. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility.
View Article and Find Full Text PDFIdentifying Disease Associated Multi-Omics Network With Mixed Graphical Models Based on Markov Random Field Model.
Genet Epidemiol
January 2025
Interdisciplinary Program of Bioinformatics, College of Natural Science, Seoul National University, Seoul, South Korea.
In this article, we proposed a new method named fused mixed graphical model (FMGM), which can infer network structures associated with dichotomous phenotypes. FMGM is based on a pairwise Markov random field model, and statistical analyses including the proposed method were conducted to find biological markers and underlying network structures of the atopic dermatitis (AD) from multiomics data of 6-month-old infants. The performance of FMGM was evaluated with simulations by using synthetic datasets of power-law networks, showing that FMGM had superior performance for identifying the differences of the networks compared to the separate inference with the previous method, causalMGM (F1-scores 0.
View Article and Find Full Text PDFG-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.
Int J Comput Assist Radiol Surg
January 2025
Computer Vision and Image Processing Lab., UofL, Louisville, KY, 40292, USA.
Purpose: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.
Methods: The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e.
Behind the scenes of cellular organization: Quantifying spatial phenotypes of puncta structures with statistical models including random fields.
Mol Biol Cell
January 2025
Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, the Netherlands.
The cellular interior is a spatially complex environment shaped by non-trivial stochastic and biophysical processes. Within this complexity, spatial organizational principles-also called spatial phenotypes-often emerge with functional implications. However, identifying and quantifying these phenotypes in the stochastic intracellular environment is challenging.
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