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Detailed method publication on the ALaSCA platform, with a case study on T1D validating its capability to do both causal analysis and counterfactual simulation
March 18 2023

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ALaSCA: A novel in silico simulation platform to untangle biological pathway mechanisms, with a case study in Type 1 Diabetes progression

The analysis of signaling pathways is a cornerstone in clarifying the biological mechanisms involved in complex genetic disorders. These pathways have intricate topologies, and the existing methods that are used for the interpretation of these pathways, remain limited. We have therefore developed the Adaptable Large-Scale Causal Analysis (ALaSCA) computational platform, which uses causal analysis and counterfactual simulation techniques. ALaSCA offers the ability to simulate the outcome of a number of different hypotheses to gain insight into the complex dynamics of biological mechanisms prior to, or even without, wet lab experimentation. ALaSCA is offered as a proprietary Python library for bioinformaticians and data scientists to use in their life sciences workflows. Here we demonstrate the ability of ALaSCA to untangle the pivots and redundancies within biological pathways of various drivers of a specific phenotypic process. This is achieved by studying a major disease of global relevance, namely Type 1 Diabetes (T1D), and quantifying causal relationships between antioxidant proteins and T1D progression. ALaSCA is also benchmarked against standard associative analysis methods.

ALaSCA Validation Programme
December 9 2022

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Validation programme document offers biology-as-a-service. The purpose of this document is to show sufficient evidence in support of results and insights from the biology-as-a-service offering. It is a validation of the ability of to design the appropriate directed acyclic graph (DAG) needed to determine causal inference, as well as the ability to choose the correct accompanying causal inference model (e.g. linear regression, distance matching, etc.).

Computational results extending the previous study to Drosophila and applying causal inference
Nov 1 2022

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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.

Computational results shining a spotlight on untapped aging genes
June 30 2022

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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.

Anti Microbial Resistance Case Study
July 2022

Demonstration of our system's early capability
November 3 2021

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