Machine learning concepts and its applications for prediction of diseases based on drug behaviour: An extensive review

Singh, Davinder Paul; Kaushik, Baijnath

doi:10.1016/j.chemolab.2022.104637

Cited by 12 publications

(4 citation statements)

References 54 publications

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“…While ML models are an important part of data handling, other steps need to be taken in preparation, like data acquisition, the selection of the appropriate algorithm, model training, and model validation [3]. The selection of relevant features is one of the key prerequisites to designing an efficient classifier, which allows for robust and focused learning models [23].…”

Section: Machine Learning Overviewmentioning

confidence: 99%

“…Supervised learning methods are suitable for regression and data classification, being primarily used for a variety of algorithms like linear regression, artificial neural networks (ANNs), decision trees (DTs), support vector machines (SVMs), k-nearest neighbors (KNNs), random forest (RF), and others [3]. As an example, systems using RF and DT algorithms have developed a huge impact on areas such as computational biology and disease prediction, while SVM has also been used to study drug-target interactions and to predict several life-threatening diseases, such as cancer or diabetes [23].…”

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confidence: 99%

“…It can provide data-driven methods for future insights and has been regarded as a promising approach for predicting workload quickly and accurately [26]. As an example, ML applications in biological fields are growing rapidly in several areas, such as genome annotation, protein binding, and recognizing the key factors of cancer disease prediction [23]. The deployment of ML algorithms on cloud servers has also offered opportunities for more efficient resource management [26].…”

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confidence: 99%

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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Section: Machine Learning Overviewmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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“…Predicting the unknown DDIs using intelligent algorithms, such as machine learning and deep learning, has become increasingly popular. 25 Abdelaziz et al have presented a large-scale similarity-based framework that predicts DDIs by link prediction. 26 Zhang et al have proposed a sparse feature learning ensemble method with linear neighborhood regularization for DDIs prediction.…”

Section: Introductionmentioning

confidence: 99%