An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study

Zafeiris, Dimitrios; Rutella, Sergio; Ball, Graham

doi:10.1016/j.csbj.2018.02.001

Cited by 54 publications

(37 citation statements)

References 44 publications

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“…The latest research on the validation and discovery of Alzheimer's disease was carried out in [68], where an artificial neural network pipeline was used for the said task. The research is a major contribution to the domain of dementia, which has no established cause and cure.…”

Section: A Artificial Neural Network (Anns)mentioning

confidence: 99%

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja¹,

Asghar²

2020

IJACSA

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Clinical advancements are one of the major outcomes of the technological phase shift of data sciences. The significance of information technology in medical sciences by utilizing the Clinical Decision Support System (CDSS) has opened the spillways of exponentially improved predictive models. Utilizing the latest norms of classification algorithms on clinical data are widely incorporated for prognostic assessments. Medical experts have to make decisions that are crucial in nature and if the research can develop a mechanism that assists them in evolving solid reasoning, infer the knowledge and clearly express their clinical decision by justifying their assertions made, it will be a win-win situation. However, this field of science is still an unknown world for clinicians despite the fact that the enormous amount of medical data cannot be exploited to its maximum without invoking the technological support. The objective of this research is to introduce the clinicians and policymakers of the medical domain with the renowned computer-based methodologies employed to construct a clinical decision support system. We expect that gaining the technical insight into the medical domain by the stakeholders will ensure commissioning the accurate and effective CDSS for improved healthcare delivery.

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Section: A Artificial Neural Network (Anns)mentioning

confidence: 99%

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja¹,

Asghar²

2020

IJACSA

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“…Artificial neural network (ANN) constitutes a promising statistical tool since it is flexible and can model highly non-linear systems, in which the relationships between variables are unknown or very complex [16][17][18]. The ANN models simulate the learning process carried out by the neurons, establishing connections among different variables, and allowing a complex data analysis through mathematical functions ANN.…”

Section: Introductionmentioning

confidence: 99%

“…Then, some connections, similar to those in synapses, are established among the variables by means of different mathematical functions (hyperbolic, sigmoid…), and different coefficients are assigned to these interactions in order to improve the model's classification ability [19]. In this sense, ANN analysis is based on supervised learning which has the advantage of being tolerant with the highly complex and noisy data obtained from biological samples [17]. ANN analysis has also some disadvantages, namely the inability to exactly reproduce the same model due to the complex learning processes involved in the models' development [16], as well as the fact of being considered a "black box" by some authors [17].…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, ANN analysis is based on supervised learning which has the advantage of being tolerant with the highly complex and noisy data obtained from biological samples [17]. ANN analysis has also some disadvantages, namely the inability to exactly reproduce the same model due to the complex learning processes involved in the models' development [16], as well as the fact of being considered a "black box" by some authors [17].…”

Section: Introductionmentioning

confidence: 99%

“…Conclusion: Lipid peroxidation and ANN constitute a useful approach to establish a reliable diagnosis when the prognosis is complex, multidimensional and non-linear. [17]. Neurons are placed in several layers (input, hidden, output) in the https://doi.…”

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confidence: 99%

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Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis

Peña-Bautista

Oger

Baquero

et al. 2019

Clinical Biochemistry

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A B S T R A C TObjective: Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease (AD) stages, and artificial neural network (ANN) analysis is a promising non-linear regression model, characterized by its high flexibility and utility in clinical diagnosis. ANN simulates neuron learning procedures and it could provide good diagnostic performances in this complex and heterogeneous disease compared with linear regression analysis. Design and Methods: In our study, a new set of lipid peroxidation compounds were determined in urine and plasma samples from patients diagnosed with early Alzheimer Disease (n = 70) and healthy controls (n = 26) by means of ultra-performance liquid chromatography coupled with tandem mass-spectrometry. Then, a model based on ANN was developed to classify groups of participants. Results: The diagnostic performances obtained using an ANN model for each biological matrix were compared with the corresponding linear regression model based on partial least squares (PLS), and with the non-linear (radial and polynomial) support vector machine (SVM) models. Better accuracy, in terms of receiver operating characteristic-area under curve (ROC-AUC), was obtained for the ANN models (ROC-AUC 0.882 in plasma and 0.839 in urine) than for PLS and SVM models. Conclusion: Lipid peroxidation and ANN constitute a useful approach to establish a reliable diagnosis when the prognosis is complex, multidimensional and non-linear. [17]. Neurons are placed in several layers (input, hidden, output) in the https://doi.

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Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Winchester,

Harshfield,

Shi

et al. 2023

Alzheimer's & Dementia

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With the increase in large multimodal cohorts and high‐throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice.Highlights Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real‐world validation, and interdisciplinary collaboration are required.

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An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study

Cited by 54 publications

References 44 publications

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

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