Deep biomarkers of aging and longevity: from research to applications

Abstract

Multiple recent advances in machine learning enabled computer systems to exceed human performance in many tasks including voice, text, and speech recognition and complex strategy games. Aging is a complex multifactorial process driven by and resulting in the many minute changes transpiring at every level of the human organism. Deep learning systems trained on the many measurable features changing in time can generalize and learn the many biological processes on the population and individual levels. The deep age predictors can help advance aging research by establishing causal relationships in non-linear systems. Deep aging clocks can be used for identification of novel therapeutic targets, evaluating the efficacy of the various interventions, data quality control, data economics, prediction of health trajectories, mortality, and many other applications. Here we present the current state of development of the deep aging clocks in the context of the pharmaceutical research and development and clinical applications.

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

“…Indeed, by comparing females included in the YA and OA groups as well as males included in the YA and OA groups, in separate analyses, we have examined the pure effect of ageing on voice. Our findings fully agree with previous reports demonstrating the effect of ageing on the human voice [ 24 , 25 , 26 , 27 , 28 , 33 , 34 , 35 , 36 , 37 , 38 ]. Early studies based on the qualitative/perceptual evaluation of voice recordings have demonstrated that physiologic ageing leads to several changes in specific characteristics of the human voice [ 1 ].…”

“…In our study, by applying the ROC curve analysis, we demonstrated in detail the high accuracy of our machine learning analysis in demonstrating age-related changes in the human voice. Our results fit in well with previous studies applying automatic classifiers based on machine learning analysis [ 24 , 25 , 26 , 27 , 28 , 33 , 34 , 35 , 36 , 37 , 38 ]. More in detail, our machine learning algorithm has achieved higher results than those obtained on the INTERSPEECH 2010 age and gender sub-challenge feature set [ 33 , 34 ].…”

“…The advantages of applying those algorithms consist of obtaining smaller dataset of features (only 20 features in our study), easier math handled and with shorter computation time. Moreover, all the previous studies considered only MFCC-, f0-, pitch-, energy-, jitter-, and shimmer-related features [ 24 , 25 , 26 , 27 , 28 , 33 , 34 , 35 , 36 , 37 ], with only a study considering non-traditional features including RASTA-PLP coefficients [ 38 ]. In addition to the traditional frequency-, jitter-, shimmer-, energy-, spectral, and cepstral-related features, we have also included MFCC and RASTA-PLP coefficients and three additional representative features (HNR, SNR, and CPPs).…”

“…The most recent approach used to assess physiologic ageing in healthy subjects consists of the objective voice analysis based on machine learning algorithms [ 24 , 25 , 26 , 27 , 28 ]. Machine-learning is a novel and robust method commonly applied to classify complex variables obtained from large datasets [ 29 , 30 , 31 ].…”

“…More in detail, machine learning can be applied to predict outcomes from recurring patterns of features within various types of multidimensional data sets [ 32 ]. Several authors have applied automatic classifiers based on machine learning analysis on voice recordings to classify healthy subjects according to their age and gender [ 24 , 25 , 26 , 27 , 28 , 33 , 34 , 35 , 36 , 37 , 38 ]. More recently, to further improve the overall accuracy of the machine learning analysis, several studies have included an increasing number of voice features in the datasets [ 24 , 25 , 26 , 27 , 28 ] and compared the performance of different machine learning algorithms [ 37 , 38 ].…”