1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences 1.1 Methods 1.1.1 Training and validation cohort descriptions 1.1.2 Taxonomic and functional profiling 1.1.3 Metabolite profiling 1.1.4 Elastic net regularization 1.1.5 Significance testing with shuffled data 1.1.6 Gene set enrichment analysis 1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences 开发的软件名字叫做Melonn
1 Abstract 2 Methods 2.1 Study population 2.2 Definition of the fetal and birth measurements 2.3 Metabolic profiling by NMR 2.4 Statistical analysis 2.4.1 Step 1. Identify main sources of metabolic variation between first and third trimesters of gestation 2.4.2 Step 2. Identify metabolites associated with fetal growth 2.4.3 Step 3. Assess the extent to which the metabolite panel associated with fetal growth can be explained by known growth-related factors
0.0.1 Machine learning for biomedical information freenome
freenome intergateing innovative science to develop a blood-based cancer test starting with CRC.
cfDNA (cell free DNA).
Interpreting deep learning models of regulatory DNA
decoding genome function
control elements gene
prediCtive model of regularity DNA
Just transform DNA sequence to dummy matrxi.
0.0.1 Machine learning for biomedical information hierarchical latent variable models?
1 Google key work search 1.1 Urinary metabolic variation analysis during pregnancy and application in Gestational Diabetes Mellitus and spontaneous abortion biomarker discovery 1.2 Metabolic profiling of pregnancy: cross- sectional and longitudinal evidence 1.3 Maternal urinary metabolic signatures of fetal growth and associated clinical and environmental factors in the INMA study 1.3.1 结论 1.3.2 Study population 1.3.3 Definition of the fetal and birth measurements 1.4 Variants in the fetal