A novel method for predicting the progression rate of ALS disease based on automatic generation of probabilistic causal chains

Ahangaran, Meysam; Jahed‐Motlagh, Mohammad Reza; Minaei-Bidgoli, Behrouz

doi:10.1016/j.artmed.2020.101879

Cited by 9 publications

(2 citation statements)

References 60 publications

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“…The key feature of causal networks is being discovery-based, and suitable for handling large-scale data, where we have limited knowledge about the underlying interconnectivity. There are different applications of systematic analysis of omics including causal networks ( Zhu et al, 2012 ; Franzén et al, 2016 ; Broumand and Dadaneh, 2018 ; Ahangaran et al, 2019 ; Ahangaran et al, 2020 ; Yazdani et al, 2020 ; Gerring et al, 2021 ). For instance one of the early applications is the integration of genetic variants, metabolites, gene expressions, and proteins on yeast data to identify the underlying molecular networks ( Zhu et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

From classical mendelian randomization to causal networks for systematic integration of multi-omics

Yazdani

Méndez-Giráldez

et al. 2022

Front. Genet.

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The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data.

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Section: Introductionmentioning

confidence: 99%

From classical mendelian randomization to causal networks for systematic integration of multi-omics

Yazdani

Méndez-Giráldez

et al. 2022

Front. Genet.

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“…The graphical display of DBNs makes them an excellent tool when describing disease progression, as clinicians can easily interpret the obtained networks due to the intuitive meaning of the conditional dependences learnt for the edges of the networks. The usefulness of DBNs to model patients' progression has been shown in previous work, but mainly using data from the pooled resource open-access ALS clinical trials (PRO-ACT) dataset [17,18], which represents a specific population of ALS patients.…”

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confidence: 99%

Dynamic Bayesian networks for stratification of disease progression in amyotrophic lateral sclerosis

Gromicho

Leão

Santos

et al. 2022

Euro J of Neurology

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Background and purpose Progression rate is quite variable in amyotrophic lateral sclerosis (ALS); thus, tools for profiling disease progression are essential for timely interventions. The objective was to apply dynamic Bayesian networks (DBNs) to establish the influence of clinical and demographic variables on disease progression rate. Methods In all, 664 ALS patients from our database were included stratified into slow (SP), average (AP) and fast (FP) progressors, according to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS‐R) rate of decay. The sdtDBN framework was used, a machine learning model which learnt optimal DBNs with both static (gender, age at onset, onset region, body mass index, disease duration at entry, familial history, revised El Escorial criteria and C9orf72) and dynamic (ALSFRS‐R scores and sub‐scores, forced vital capacity, maximum inspiratory pressure, maximum expiratory pressure and phrenic amplitude) variables. Results Disease duration and body mass index at diagnosis are the foremost influences amongst static variables. Disease duration is the variable that better discriminates the three groups. Maximum expiratory pressure is the respiratory test with prevalent influence on all groups. ALSFRS score has a higher influence on FP, but lower on AP and SP. The bulbar sub‐score has considerable influence on FP but limited on SP. Limb function has a more decisive influence on AP and SP. The respiratory sub‐score has little influence in all groups. ALSFRS‐R questions 1 (speech) and 9 (climbing stairs) are the most influential in FP and SP, respectively. Conclusions The sdtDBN analysis identified five variables, easily obtained during clinical evaluation, which are the most influential for each progression group. This insightful information may help to improve prognosis and care.

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AIM in Amyotrophic Lateral Sclerosis

Ahangaran¹,

Chiò²

2021

Artificial Intelligence in Medicine

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A novel method for predicting the progression rate of ALS disease based on automatic generation of probabilistic causal chains

Cited by 9 publications

References 60 publications

From classical mendelian randomization to causal networks for systematic integration of multi-omics

From classical mendelian randomization to causal networks for systematic integration of multi-omics

Dynamic Bayesian networks for stratification of disease progression in amyotrophic lateral sclerosis

AIM in Amyotrophic Lateral Sclerosis

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