AIMC Topic: Longevity
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Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard Petri dishes.
Scientific reports
Feb 2, 2022
Data from manual healthspan assays of the nematode Caenorhabditis elegans (C. elegans) can be complex to quantify. The first attempts to quantify motor performance were done manually, using the so-called thrashing or body bends assay. Some laboratori...
Machine learning-based predictions of dietary restriction associations across ageing-related genes.
BMC bioinformatics
Jan 4, 2022
BACKGROUND: Dietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related ge...
A Shape-Constrained Neural Data Fusion Network for Health Index Construction and Residual Life Prediction.
IEEE transactions on neural networks and learning systems
Oct 27, 2021
With the rapid development of sensor technologies, multisensor signals are now readily available for health condition monitoring and remaining useful life (RUL) prediction. To fully utilize these signals for a better health condition assessment and R...
Towards Lifespan Automation for Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification.
Sensors (Basel, Switzerland)
Jul 20, 2021
The automation of lifespan assays with in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining wh...
A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study.
Journal of medical Internet research
Feb 24, 2021
BACKGROUND: Identifying cognitive impairment early enough could support timely intervention that may hinder or delay the trajectory of cognitive impairment, thus increasing the chances for successful cognitive aging.
Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals.
International journal of molecular sciences
Jan 22, 2021
One of the important questions in aging research is how differences in transcriptomics are associated with the longevity of various species. Unfortunately, at the level of individual genes, the links between expression in different organs and maximum...
ARDD 2020: from aging mechanisms to interventions.
Aging
Dec 30, 2020
Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence and advanced screening techniques, as well as a strong influence from the industry sector may lead t...
Identifying longevity associated genes by integrating gene expression and curated annotations.
PLoS computational biology
Nov 30, 2020
Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are...
Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging.
Aging cell
Oct 8, 2020
We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q-value and age coefficient of these proteins in a plasma prot...
AI-based investigation of molecular biomarkers of longevity.
Biogerontology
Jul 6, 2020
In this paper, I build deep neural networks of various structures and hyperparameters in order to predict human chronological age based on open-access biochemical indicators and their specifications from the NHANES database. In total, 1152 neural net...
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