CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data [0.03%]
基于下一代测序数据的ECOD算法的拷贝数变异检测方法_CNVECOD
Ranran Sun,Jinxin Dong,Hua Jiang et al.
Ranran Sun et al.
Copy number variation (CNV), as a major type of DNA structural variations (SVs), plays a key role in causing human diseases and contributing to genetic diversity. Accurate identification of CNVs is significant for disease mechanism analysis...
ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation [0.03%]
基于模型的框架增强学习检测拷贝数变异的方法
Sandip Samaddar,Rituparna Sinha,Rajat Kumar Pal et al.
Sandip Samaddar et al.
Copy number variation (CNV) is one of the most imperative forms of structural variations that can span over the coding and non-coding regulatory regions of an individual's genome. Copy number variations (CNV) can significantly impact the ge...
When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis [0.03%]
当管道运行但坐标失败时：后GWAS分析中虚假局部性的简单空间特定性检查
Zheng Han,Hongcheng Zhu,Changzai Li
Zheng Han
Some post-GWAS analysis software can run to completion without reporting an error while producing results that are not biologically valid. We call this failure false locality: a result appears to be local to a gene or protein because it was...
Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction [0.03%]
基于模板、进化扩散和生成语言模型的IsPETase结构预测综合基准测试
Berkay Orçun Yener,Şurhan Göl,Bora Kutlu et al.
Berkay Orçun Yener et al.
Accurate protein structure prediction is critical for rational enzyme engineering, which requires high-fidelity models. This study benchmarks three distinct structure prediction paradigms against the experimental crystal structure of IsPETa...
Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks [0.03%]
异步布尔网络中可达性结构函数论：作为标记理想的陷落空间理论
Belayneh Yibeltal Yizengaw
Belayneh Yibeltal Yizengaw
Boolean networks provide a qualitative framework for modelling regulatory systems when kinetic parameters are unavailable, with cellular phenotypes represented as attractors of the induced dynamics [J. D. Schwab et al., Concepts in Boolean ...
Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder [0.03%]
致读者一封更正信：基于多分子指纹特征和以注意力为中心的加权自动编码器的药物-药物相互作用预测网络
K Soni Sharmila,S Thanga Revathi,Pokkuluri Kiran Sree
K Soni Sharmila
Designing of sorafenib analogs to target c-Raf for the management of hepatocellular carcinoma: Molecular dynamics and mmPBSA analysis [0.03%]
设计罗拉非尼类似物靶向c-Raf以管理肝细胞癌：分子动力学和mmPBSA分析
Saima Ejaz,Rehan Zafar Paracha,Maryum Nisar et al.
Saima Ejaz et al.
Introduction: Sorafenib remains the only approved treatment for advanced hepatocellular carcinoma (HCC), yet its clinical use is hindered by toxicity and the emergence of drug resistance. Sorafenib's anticancer effects are largely attribute...
Neelofar Sohi,Shaheena Sohi
Neelofar Sohi
Human Genome Project (HGP), Genome Wide Association Studies (GWAS) and The Cancer Genome Atlas (TCGA) are some of the remarkable research endeavors that generated massive amounts of information about Single Nucleotide Polymorphisms (SNPs) a...
Predicting functional co-occurrence probability in PPI networks via multi-level participation expectation [0.03%]
基于PPI网络的多级参与预期的功能共现概率预测方法
Peng Wang
Peng Wang
This study addresses the problem of protein function annotation and proposes a multi-source biological information-fusion framework called Functional co-Occurrence Probability Estimation (FOPE) for estimating functional co-occurrence probab...
NNDock2: A neural network-based scoring function for ranking protein-protein docking models [0.03%]
基于神经网络的蛋白质-蛋白质对接模型排序评分函数NNDock2
Myong-Ho Chae,Gwang So,Ung-Jin Kim
Myong-Ho Chae
Protein-protein interactions (PPIs) play crucial roles in diverse cellular functions and biological processes, and structural knowledge of the protein complexes is valuable for the elucidation of those functions and designing new drugs. Due...