Univariable logistic regression of every protein at admission.
收藏NIAID Data Ecosystem2026-05-01 收录
数据链接：
官方服务：
资源简介：
Univariable logistic regression of every protein at admission.
应用场景：
创建时间：
2024-03-08
相关数据集
DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery using Data-Independent Acquisition and Parallel Reaction Monitoring
We have included human samples including bladder tissue, Cervical tissue, Colorectal tissue, diffuse large B-cell lymphoma, Gastric tissue, glioblastoma multiforme, K562 cell, thyroid tissue, Plasma,
国家人口健康科学数据中心10
Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv
BackgroundDiabetic nephropathy (DN) is a common complication of diabetes, characterized by damage to renal tubules and glomeruli, leading to progressive renal dysfunction. The aim of our study is to e
NIAID Data Ecosystem00
Data_Sheet_3_Machine learning-based prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke: a large multicenter study.PDF
BackgroundThis study aimed to compare the performance of different machine learning models in predicting symptomatic intracranial hemorrhage (sICH) after thrombolysis treatment for ischemic stroke. Me
NIAID Data Ecosystem00
DataSheet1_RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases.DOCX
Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlation
NIAID Data Ecosystem10
Table 1_Machine learning-based prediction of intensive care unit admission in COVID-19 patients presenting with mild respiratory failure.docx
IntroductionPrevious studies applying machine learning to predict severe respiratory failure in COVID-19 patients have shown inconsistent results due to variations in study populations and predictor v
NIAID Data Ecosystem00
