ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.

Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play crucial roles in gene expression regulation and are significant in disease associations and medical research. Accurate ncRNA-disease association prediction is essential for understanding disease mechanisms and developing treatments. Existing methods often focus on single tasks like lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), or lncRNA-miRNA interactions (LMIs), and fail to exploit heterogeneous graph characteristics.

KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-disease Association Prediction.

CircRNA is closely related to human disease, so it is important to predict circRNA-disease association (CDA). However, the traditional biological detection methods have high difficulty and low accuracy, and computational methods represented by deep learning ignore the ability of the model to explicitly extract local depth information of the CDA. We propose a model based on knowledge graph from recursion and attention aggregation for circRNA-disease association prediction (KGRACDA).

Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection.

Motivation: Topologically associating domains (TADs) are fundamental building blocks of 3D genome. TAD-like domains in single cells are regarded as the underlying genesis of TADs discovered in bulk cells. Understanding the organization of TAD-like domains helps to get deeper insights into their regulatory functions.

scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data.

Motivation: The emerging single-cell Hi-C technology provides opportunities to study dynamics of chromosomal organization. How to construct a pseudotime path using single-cell Hi-C contact matrices to order cells along developmental trajectory is a challenging topic, since these matrices produced by the technology are inherently high dimensional and sparse, they suffer from noises and biases, and the topology of trajectory underlying them may be diverse.

Results: We present scHiCPTR, an unsupervised graph-based pipeline to infer pseudotime from single-cell Hi-C contact matrices.

TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data.

Topologically associating domains (TADs) are fundamental building blocks of three dimensional genome, and organized into complex hierarchies. Identifying hierarchical TADs on Hi-C data helps to understand the relationship between genome architectures and gene regulation. Herein we propose TADfit, a multivariate linear regression model for profiling hierarchical chromatin domains, which tries to fit the interaction frequencies in Hi-C contact matrix with and without replicates using all-possible hierarchical TADs, and the significant ones can be determined by the regression coefficients obtained with the help of an online learning solver called Follow-The-Regularized-Leader (FTRL).

TADBD: a sensitive and fast method for detection of typologically associated domain boundaries.

A topologically associated domain (TAD) is a self-interacting genomic block. Detection of TAD boundaries on Hi-C contact matrix is one of the most important issues in the analysis of 3D genome architecture at TAD level. Here, we present TAD boundary detection (TADBD), a sensitive and fast computational method for detection of TAD boundaries on Hi-C contact matrix.

Comparison of normalization methods for Hi-C data.

Hi-C has been predominately used to study the genome-wide interactions of genomes. In Hi-C experiments, it is believed that biases originating from different systematic deviations lead to extraneous variability among raw samples, and affect the reliability of downstream interpretations. As an important pipeline in Hi-C analysis, normalization seeks to remove the unwanted systematic biases; thus, a comparison between Hi-C normalization methods benefits their choice and the downstream analysis.

A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining.

A filter feature selection technique has been widely used to mine biomedical data. Recently, in the classical filter method minimal-Redundancy-Maximal-Relevance (mRMR), a risk has been revealed that a specific part of the redundancy, called irrelevant redundancy, may be involved in the minimal-redundancy component of this method. Thus, a few attempts to eliminate the irrelevant redundancy by attaching additional procedures to mRMR, such as Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR), have been made.

A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology.

Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously.