CrossPredGO: A Novel Light-weight Cross-modal Multi-attention Framework for Protein Function Prediction [0.03%]
CrossPredGO：一种轻量级的交叉模态多注意框架用于蛋白质功能预测
Vikash Kumar,Akshay Deepak,Ashish Ranjan et al.
Vikash Kumar et al.
Proteins are represented in various ways, each contributing differently to protein-related tasks. Here, information from each representation (protein sequence, 3D structure, and interaction data) is combined for an efficient protein functio...
Siddharth Bhadra-Lobo,Georgy Derevyanko,Guillaume Lamoureux
Siddharth Bhadra-Lobo
Predicting the physical interaction of proteins is a cornerstone problem in computational biology. New classes of learning-based algorithms are actively being developed, and are typically trained end-to-end on protein complex structures ext...
Exploring combined effects of DNA methylation and copy number on gene expression with a two-stage approach [0.03%]
基于两阶段方法探究DNA甲基化和拷贝数对基因表达的联合影响
Henry Claussen,Santu Ghosh,Jie Chen
Henry Claussen
DNA methylation and copy number may be associated with each other to some extent, in positive or negative ways. Whether differential methylation and copy number variation have combined effects on gene expression is largely unknown. We use a...
Hybrid Causal Feature Selection for Cancer Biomarker Identification from RNA-seq Data [0.03%]
基于RNA序列数据的癌症生物标志物 hybrid casual特征选择识别方法
Wenwei Xu,Hao Zhang,Yewei Xia et al.
Wenwei Xu et al.
The discovery of cancer biomarkers helps to advance medical diagnosis and plays an important role in biomedical applications. Most of the existing data-driven methods identify biomarkers by ranking-based strategies, which generally return a...
Xiaoxiao Sun,Paul Sajda
Xiaoxiao Sun
There is a growing interest in characterizing circular data found in biological systems. Such data are wide-ranging and varied, from the signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering algo...
Maotao Liu,Yifan Yang,Qun Liu et al.
Maotao Liu et al.
Due to the great successes of Graph Neural Networks (GNN) in numerous fields, growing research interests have been devoted to applying GNN to molecular learning tasks. The molecule structure can be naturally represented as graphs where atom...
scVSC: Deep variational subspace clustering for single-cell transcriptome data [0.03%]
基于深度变分子空间聚类的单细胞转录组数据分析方法（scVSC）
Zile Wang,Haiyun Wang,Jianping Zhao et al.
Zile Wang et al.
Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limi...
An efficient exact algorithm for planted motif search on large DNA sequence datasets [0.03%]
一种高效的精确算法用于在大型DNA序列数据集中搜索人工标签motif
Qiang Yu,Yana Hu,Xinnan Hu et al.
Qiang Yu et al.
DNA motif is the pattern shared by similar fragments in DNA sequences, which plays a key role in regulating gene expression, and DNA motif discovery has become a key research topic. Exact planted (l,d)-motif search (PMS) is one of the motif...
RDGAN: Prediction of circRNA-Disease Associations Via Resistance Distance and Graph Attention Network [0.03%]
基于电阻距离和图注意力网络的圆形单链rna-疾病关联预测模型(RDGAN)
Pengli Lu,Yuehao Wang
Pengli Lu
As a series of single-stranded RNAs, circRNAs have been implicated in numerous diseases and can serve as valuable biomarkers for disease therapy and prevention. However, traditional biological experiments demand significant time and effort....
ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction [0.03%]
基于并行图卷积网络的化合物-蛋白质互作预测模型
Longxin Zhang,Wenliang Zeng,Jingsheng Chen et al.
Longxin Zhang et al.
Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up ...