Duly validated in a large cohort, models based on a combination of these markers could provide a highly accurate assessment of biological age and the pace of aging. Clinicians routinely use several indicators that could be biomarkers of aging. Markers of aging could facilitate effective health control, more accurate life expectancy estimates, and improved health and quality of life. Moreover, a consensual approach to the very concept of aging and the means of its assessment are yet to be developed. There is no single universal biomarker yet to estimate overall health status and longevity prospects. Overall, we have presented a path for effectively leveraging massive public omics data collections to investigate the molecular basis of age- and sex-differences in physiology and disease. Enrichment analysis of these gene signatures with prior gene annotations helped in identifying age- and sex-associated multi-tissue and pan-body molecular phenomena (e.g., general immune response, inflammation, metabolism, hormone response). Further, these predictive models capture sex-stratified age-group 'gene signatures', i.e., the strength and the direction of importance of genes across the genome for each age group in each sex.
Dataset-level cross validation shows that these ML classifiers are able to discriminate between age groups in a biologically meaningful way in each sex across technologies. Specifically, we trained one-vs-rest logistic regression classifiers with elastic-net regularization to classify transcriptomes into age groups.
Then, we used this dataset to infer sex-biased genes within eleven age groups along the human lifespan and then trained machine learning (ML) models to predict these age groups from gene expression values separately within females and males. We first curated ~30,000 primary samples associated with age and sex information and profiled using microarray and RNA-seq. As the vast majority of these profiles lack age and sex labels, the core idea of our framework is to use the measured expression data to predict missing age/sex metadata and derive the signatures from the predictive models. Here, we present a computational framework that leverages these data to infer genome-wide molecular signatures specific to sex and age groups. Hundreds of thousands of publicly-available human transcriptomes capturing gene expression profiles of tissues across the body and subject to various biomedical and clinical factors present an invaluable, yet untapped, opportunity for bridging these gaps. As a result, there are massive gaps in our understanding of genes and molecular mechanisms that underlie sex- and age-associated physiology and disease. In our calculations, we use the former method.Age and sex are historically understudied factors in biomedical studies even though many complex traits and diseases vary by these factors in their incidence and presentation. The confusion comes from the uneven number of days in different months. Similar situations exist for dates like Apr. 31 as the end of the month, then the result is one month. 28 to be one month, then the result is one month and 3 days. However, there are two ways to calculate the age from Feb. In some situations, the months and day result of this age calculator may be confusing, especially when the starting date is the end of a month. For example, if one baby is born just one day before the Traditional Chinese New Year, 2 days later, the baby will be 2 even though he/she is only 2 days old. In one of the traditional Chinese age systems, people are born at age 1 and their age increases up at the Traditional Chinese New Year rather than their birthday. For example, a person who is twenty years old is the same age as another person who is in their twenty-first year of life. In some cultures, age is expressed by counting years with or without including the current year. Most western countries use this age system. For example, the age of a person who has lived for 3 years and 11 months is 3, and their age will increase to 4 on their next birthday one month later. In this system, age increases on a person's birthday. This calculator is based on the most common age system. The age of a person can be counted differently in different cultures. Related Date Calculator | Time Calculator
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