Multi-grained pooling network for age estimation in degraded low-resolution images.

Low-resolution images present significant challenges for age estimation in real-world. Current models are unsuitable for low-resolution scenarios as they lose crucial details and weaken feature representations, leading to significant performance degradation. To address the limitation, we propose the Multi-Grained Pooling Network (MGP-Net), a novel architecture that effectively captures multi-grained information during the downsampling process, preserving essential features for age estimation.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis.

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database.

Functional connectome gradient predicts clinical symptoms of chronic insomnia disorder.

Insomnia is the second most prevalent psychiatric disorder worldwide, but the understanding of the pathophysiology of insomnia remains fragmented. In this study, we calculated the connectome gradient in 50 chronic insomnia disorder (CID) patients and 38 healthy controls (HC) to assess changes due to insomnia and utilized these gradients in a connectome-based predictive modeling (CPM) to predict clinical symptoms associated with insomnia. The results suggested that insomnia led to significant alterations in the functional gradients of some brain areas.

Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism.

Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors.

Multiple Sequence Alignment based on deep Q network with negative feedback policy.

Background And Objective: Multiple Sequence Alignment (MSA) is an essential procedure in the sequence analysis of biological macromolecules, which can obtain the potential information between multiple sequences, such as functional and structural information. At present, the main challenge of MSA is an NP-complete problem; the algorithm's complexity increases exponentially with the increase of the number of sequences. Some methods are constantly approaching the results towards the optimal ratio and easy to fall into the local optimization, so the accuracy of these methods is still greatly improved.

A hybrid ensemble and evolutionary algorithm for imbalanced classification and its application on bioinformatics.

Imbalanced data classification is the fundamental problem of data mining. Relevant researchers have proposed many solutions to solve the problem, such as sampling and ensemble learning methods. However, random under-sampling is easy to lose representative samples, and ensemble learning does not use the correlation information between pieces in the data set.