Aging-related preventive interventions are not possible without personal aging speed measurement. The most up to date and complex way to measure aging rate is by using varying biomarkers of human aging is based on the utilization of deep neural networks which may be trained on any type of omics biological data to predict the subject's age. Aging has been shown to be a strong driver of transcriptome changes. Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. However an approach that uses temporal scaling and binarization of transcriptomes to define a gene set that predicts biological age with an accuracy allowed to reach an assessment close to the theoretical limit.