Wenhao Gao,Sai Pooja Mahajan,Jeremias Sulam et al.
Wenhao Gao et al.
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting...
Stephen R Midway
Stephen R Midway
We live in a contemporary society surrounded by visuals, which, along with software options and electronic distribution, has created an increased importance on effective scientific visuals. Unfortunately, across scientific disciplines, many...
Tadashi Hidaka,Keiko Imamura,Takeshi Hioki et al.
Tadashi Hidaka et al.
Machine learning is expected to improve low throughput and high assay cost in cell-based phenotypic screening. However, it is still a challenge to apply machine learning to achieving sufficiently complex phenotypic screening due to imbalanc...
scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data [0.03%]
scTenifoldNet：一种从单细胞数据构建和比较全转录组基因调控网络的机器学习工作流
Daniel Osorio,Yan Zhong,Guanxun Li et al.
Daniel Osorio et al.
We present scTenifoldNet-a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment-for constructing and comparing single-cell gene regulatory networks (scGRNs) using data fr...
A Blueprint for Identifying Phenotypes and Drug Targets in Complex Disorders with Empirical Dynamics [0.03%]
基于实证动力学的复杂疾病表型识别和药物靶点绘制方案
Madison S Krieger,Joshua M Moreau,Haiyu Zhang et al.
Madison S Krieger et al.
A central challenge in medicine is translating from observational understanding to mechanistic understanding, where some observations are recognized as causes for the others. This can lead not only to new treatments and understanding, but a...
Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra [0.03%]
平行因子分析能够量化并识别高度重叠的数据非依赖获得的蛋白质谱图
Filip Buric,Jan Zrimec,Aleksej Zelezniak
Filip Buric
High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun approaches. The resultant DIA spectra are, however, highly convolved and with no di...
Erratum: A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics [0.03%]
新冠肺炎流行下评价住院优先级的评估模型的错误修正英文标题翻译成中文就是：关于用于评估新冠肺炎疫情期间住院优先级的学习模型的勘误表
Yichao Zheng,Yinheng Zhu,Mengqi Ji et al.
Yichao Zheng et al.
[This corrects the article DOI: 10.1016/j.patter.2020.100092.]. © 2020 The Authors.
Published Erratum
Patterns (New York, N.Y.). 2020 Dec 11;1(9):100173. DOI:10.1016/j.patter.2020.100173 2020
The Uk Reproducibility Network Steering Group
The Uk Reproducibility Network Steering Group
Academia uses methods and techniques that are cutting edge and constantly evolving, while the underlying cultures and working practices remain rooted in the 19th-century model of the independent scientist. Standardization in processes and d...
Inioluwa Deborah Raji
Inioluwa Deborah Raji
The contribution of Black female scholars to our understanding of data and their limits of representation hint at a more empathetic vision for data science that we should all learn from. ...
Gauthier Vernier,Hugo Caselles-Dupré,Pierre Fautrel
Gauthier Vernier
This opinion piece offers an insight on the origins of the debates around the question of whether and when we can reach artificial general intelligence in machine learning, and how science meets with spirituality when addressing this matter...