Significant SNVs in Replication phase 1 (Phase II) and replication phase 2 (Phase III).

NIAID Data Ecosystem2026-03-09 收录

数据链接：

https://figshare.com/articles/dataset/Significant_SNVs_in_Replication_phase_1_Phase_II_and_replication_phase_2_Phase_III_/3872418 数据链接链接失效反馈

官方服务：

资源简介：

Significant SNVs in Replication phase 1 (Phase II) and replication phase 2 (Phase III).

创建时间：

2016-08-04

Table_8_Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome.xlsx

脓毒症与SIRS鉴别诊断

基因生物标志物发现

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). T

NIAID Data Ecosystem70

Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets

基因生物标志物发现

不平衡分类

High-Throughput technologies provide genomic and trascriptomic data that are suitable for biomarker detection for classification purposes. However, the high dimension of the output of such technologie

NIAID Data Ecosystem30

Data_Sheet_1_Gene Expression Analysis Reveals Novel Gene Signatures Between Young and Old Adults in Human Prefrontal Cortex.ZIP

脑衰老基因表达分析

基因生物标志物发现

Human neurons function over an entire lifetime, yet the molecular mechanisms which perform their functions and protecting against neurodegenerative disease during aging are still elusive. Here, we con

NIAID Data Ecosystem20

An unbiased machine learning exploration reveals gene sets predictive of allograft tolerance after kidney transplantation

肾移植耐受预测

基因生物标志物发现

Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potentia

NIAID Data Ecosystem40

Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

肿瘤生存预测

基因生物标志物发现

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensi

NIAID Data Ecosystem20

Significant SNVs in Replication phase 1 (Phase II) and replication phase 2 (Phase III).

资源简介：

相关数据集