Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease

Dahal, Keshab R.; Gautam, Yadu

doi:10.4236/ojs.2020.104043

Cited by 16 publications

(12 citation statements)

References 17 publications

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“…Physics-informed machine learning (PIML) is the most widely studied area of applied mathematics in molecular modeling, drug discovery, and medicine [58,63,65,[70][71][72][73][74][75][76]. Depending upon whether the ML architecture requires the pre-defined input representations as input features or can learn their own input representation by itself, PIML can be broadly classified into two sub-categories.…”

Section: Physics-informed Machine Learningmentioning

confidence: 99%

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Joshi

Kumar

2021

Molecules

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Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

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Section: Physics-informed Machine Learningmentioning

confidence: 99%

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Joshi

Kumar

2021

Molecules

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“…Predictive modeling is the most widely studied area of applied machine learning in molecular modeling, drug discovery and medicine [67,68,69,70,71,72,62,60,55,73]. Depending upon whether the ML architecture requires the pre-defined input representations as input features or can learn their own input representation by itself, predictive modeling can be broadly classified into two sub-categories.…”

Section: Predictive Modelingmentioning

confidence: 99%

Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective

Joshi,

Kumar

2021

Preprint

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Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design in the recent years. These models have been enabled by significant advancement in state-of-the-art artificial intelligence (AI) and computing infrastructures. Several ML architectures are predominantly and independently used either for predicting the properties of small molecules, or for generating lead therapeutic candidates. Synergetically using these individual components along with robust representation and data generation techniques autonomously in closed loops holds enormous promise for accelerated drug design which is a time consuming and expensive task otherwise. In this perspective, we present the most recent breakthrough achieved by each of the components, and how such autonomous AI and ML workflow can be realized to radically accelerate the hit identification and lead optimization. Taken together, this could significantly shorten the timeline for end-to-end antiviral discovery and optimization times to weeks upon the arrival of a novel zoonotic transmission event. Our perspective serves as a guide for researchers to practice autonomous molecular design in therapeutic discovery.

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“…The use of the z-Alizadeh Sani dataset was also carried out in the study of Abdar et al [13] and Dahal and Gautam [14]. Research by Abdar et al [13] proposed a diagnosis model using the Nested Ensemble Nu-Support Vector Classification (NE-nu-SVC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, no feature selection process was carried out, meaning that all attributes of the dataset were used in the test. Dahal and Gautam's [14] research proposes a model using the feature selection stage, the feature selection process produces 15 attributes. The results of the feature selection process are then classified by testing using a number of classification algorithms, namely logistic regression (LR), bagging PART, RF, SVM, and kNN.…”

Section: Introductionmentioning

confidence: 99%

Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease

Wiharto¹,

Suryani²,

Setyawan³

2021

ISI

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Coronary heart disease is a non-communicable disease with high mortality. A good action to anticipate this is to do prevention, namely by carrying out a healthy lifestyle and routine early examinations. Early detection of coronary heart disease requires a number of examinations, such as demographics, ECG, laboratory, symptoms, and even angiography. The number of inspection parameters in the context of early detection will have an impact on the time and costs that must be incurred. Selection of the right and important inspection parameters will save time and costs. This study proposes an intelligence system model for the detection of coronary heart disease by using a minimal examination attribute, with performance in the good category. This research method is divided into a number of stages, namely data normalization, feature selection, classification, and performance analysis. Feature selection uses a Two-tier feature selection framework consisting of correlation-based filters and wrappers. The system model is tested using a number of datasets, and classification algorithms. The test results show that the proposed two-tier feature selection framework is able to reduce the highest attribute of 73.51% in the z-Alizadeh Sani dataset. The performance of the system using the bagging-PART algorithm is able to provide the best performance with parameters area under the curve (AUC) 95.4%, sensitivity 95.9% while accuracy is 94.1%. Referring to the AUC value, the proposed system model is included in the good category.

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Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease

Cited by 16 publications

References 17 publications

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective

Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease

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