In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

/pdf/machine-learning-algorithms-real-world-applications-and-3ah2ghoulk.pdf

Machine Learning: Algorithms, Real-World Applications and Research Directions

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0387

Opportunities and obstacles for deep learning in biology and medicine.

In the current age of the Fourth Industrial Revolution (4IR or Industry 40), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more We also highlight the challenges and potential research directions based on our study Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view

/pdf/machine-learning-algorithms-real-world-applications-and-383hp9fib7.pdf

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

Secure and Robust Machine Learning for Healthcare: A Survey

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

/pdf/data-science-and-analytics-an-overview-from-data-driven-3lfkfp7erp.pdf

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

A machine learning-based framework to identify type 2 diabetes through electronic health records

Objective: To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. Materials and Methods: We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. Results: We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (~0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Discussion: Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls. Conclusions: Our proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.

/pdf/a-machine-learning-based-framework-to-identify-type-2-vu1wj7gfi3.pdf

A Machine Learning-based Framework to Identify Type 2 Diabetes through Electronic Health Records

PFKFB3 facilitates cell proliferation and migration in anaplastic thyroid carcinoma via the WNT/β-catenin signaling pathway.

The Clinical Impact of Heart Failure on the Postoperative Outcomes for Lung Cancer Patients Undergoing Lobectomy and Sublobar Resection by Video-Assisted Thoracic Surgery: A Propensity Score-Matched Analysis of 2016-2020 HCUP-NIS Data

Beige adipocytes have recently attracted attention for their potential as new therapeutic targets in the management of obesity and related metabolic disorders. MicroRNAs (miRNAs) have been reported as transcriptional regulators or biomarkers of brown and beige adipogenesis. Nevertheless, the effects of miRNAs involved in beige differentiation of human visceral adipocytes remain to be investigated. In the current study, microarray screening showed that miR-1275 was significantly decreased during the differentiation of beige adipocytes induced by human omental adipose-derived stem cells (hASCs). Overexpression of miR-1275 suppressed the 'brown-like' differentiation of hASCs by inhibiting the key transcriptional factor PR domain containing 16 (PRDM16) without affecting the proliferation. Adipogenesis and mitochondrial biogenesis of beige adipocytes derived from hASCs were impaired by miR-1275 overexpression. The regulatory effect of miR-1275 was determined by direct binding to the 3'-untranslated region (3'-UTR) of PRDM16, which was demonstrated by a dual-luciferase assay. Taken together, this study identified miR-1275 as a negative regulator of beige cell development in hASCs by inhibiting PRDM16. Thus, miR-1275 might be a potential target in the management of visceral obesity and related metabolic diseases.

miR-1275 inhibits human omental adipose-derived stem cells differentiation towards the beige phenotype via PRDM16.

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