Random forest (RF) missing data algorithms are an attractive approach for imputing missing
data. They have the desirable properties of being able to handle mixed types of missing …

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have
surveyed the different statistical methods to conduct imputation and have compared them on …

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG
methylation values to predict chronological or biological age. Increases in predicted …

Background Global but predictable changes impact the DNA methylome as we age, acting
as a type of molecular clock. This clock can be hastened by conditions that decrease …

Aging, for virtually all life, is inescapable. However, within populations, biological aging rates
vary. Understanding sources of variation in this process is central to understanding the …

Researchers are often faced with analyzing data sets that are not complete. To properly
analyze such data sets requires the knowledge of the missing data mechanism. If data are …

INTRODUCTION The human genome contains tens of thousands of rare (minor allele
frequency< 1%) variants, some of which contribute to disease risk. Using 838 samples with …

Patients and animals with chronic kidney disease (CKD) exhibit profound alterations in the
gut environment including shifts in microbial composition, increased fecal pH, and increased …

Functional genomic strategies have become fundamental for annotating gene function and
regulatory networks. Here, we combined functional genomics with proteomics by quantifying …

It is well known in the literature that the problem of learning the structure of Bayesian
networks is very hard to tackle: Its computational complexity is super-exponential in the …