AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer [0.03%]
基于AttentiveLSTM和Fox Wolf优化器的囊性纤维化风险预测与分类的AI方法研究
Ashwini A Pandagale,Lalit V Patil
Ashwini A Pandagale
Cystic fibrosis (CF), a genetic disorder stemming from CFTR gene mutations, requires accurate risk prediction to improve management. Modulator therapies have advanced treatment but remain limited, as they don't cover all gene variants and f...
Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data [0.03%]
完美的共线性并不相同：测量和可视化现代组学数据的多重共线性的严重程度
Wei Q Deng,Radu V Craiu,Lei Sun
Wei Q Deng
Multi-collinearity frequently occurs in modern statistical applications and when ignored, can negatively impact model selection and statistical inference. Though perfect collinearity is always present in "n < p" data, we demonstrate that pe...
Corrigendum to: Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data [0.03%]
联合建模纵向和时间到事件的癌症生存数据中基础风险的选择的勘误表
Anand Hari,Edakkalathoor George Jinto,Divya Dennis et al.
Anand Hari et al.
A fast (CNN + MCWS-transformer) based architecture for protein function prediction [0.03%]
基于快速(CNN+MCWS-变压器)的蛋白质功能预测架构
Abhipsa Mahala,Ashish Ranjan,Rojalina Priyadarshini et al.
Abhipsa Mahala et al.
The transformer model for sequence mining has brought a paradigmatic shift to many domains, including biological sequence mining. However, transformers suffer from quadratic complexity, i.e., O(l 2), where l is the sequence length, which af...
Empirically adjusted fixed-effects meta-analysis methods in genomic studies [0.03%]
基因组学研究中经验修正的固定效应meta分析方法
Wimarsha T Jayanetti,Sinjini Sikdar
Wimarsha T Jayanetti
In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta...
Lavkush Sharma,Akshay Deepak,Ashish Ranjan et al.
Lavkush Sharma et al.
Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning...
Annika Krutto,Therese Haugdahl Nøst,Magne Thoresen
Annika Krutto
This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model using discrete stable distributions i...
Flexible model-based non-negative matrix factorization with application to mutational signatures [0.03%]
一种用于突变特征分析的灵活模型化非负矩阵分解方法
Ragnhild Laursen,Lasse Maretty,Asger Hobolth
Ragnhild Laursen
Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either ...
Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data [0.03%]
癌症生存数据的联合建模中基础风险函数的选择
Anand Hari,Edakkalathoor George Jinto,Divya Dennis et al.
Anand Hari et al.
Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exog...
Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets [0.03%]
基于合成抗体-抗原数据集的统计推断的可行性研究
Thomas Minotto,Philippe A Robert,Ingrid Hobæk Haff et al.
Thomas Minotto et al.
Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of data complexity, including em...