New computational results extending the previous study to Drosophila and applying causal inference
Nov 1 2022
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We are excited to launch our second set of results in this draft paper uploaded to biorxiv.org:
ALaSCA: a computational platform for quantifying the effect of proteins using Pearlian causal inference, with an example application in Alzheimer’s disease
The goal of this study was to demonstrate the ability of our computational platform to identify molecular drivers of neuronal aging using specialised causal inference techniques. S6K was highly ranked in the previous study and here the nearby neighbours in its protein interaction network were selected to explore ALaSCA’s (Adaptable Large-Scale Causal Analysis) ability to identify possible drivers of Alzheimer’s disease.
Initial computational results shining a spotlight on untapped aging genes
June 30 2022
At incubate.bio, we investigate neuronal aging in order to understand how cellular dysfunction in aging can contribute to the onset of neurodegenerative diseases. We are excited to launch our first set of results in this draft paper uploaded to biorxiv.org:
Supervised machine learning with feature selection for prioritization of targets related to time-based cellular dysfunction in aging | bioRxiv
In this study, we propose a pipeline that integrates the plethora of publicly available genomic, transcriptomic, proteomic and morphological data of aging C. elegans with a supervised machine learning approach to prioritize aging-related genes in processes shared by aging and neurodegeneration.
Our ranked output showed that 91% of the top 438 ranked genes consisted of known genes from the GenAge database, while the remaining genes had thus far not yet been associated with aging-related processes. These ranked genes can be translated to known human orthologs and serve as targets against neurodegenerative diseases.