Research

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19 pages, 1233 KiB

Open AccessArticle

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

by Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Choon-Sik Park, Sungroul Kim and Joel Davidson

Life 2022, 12(10), 1631; https://doi.org/10.3390/life12101631 - 18 Oct 2022

Cited by 1 | Viewed by 1189

Abstract

The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively [...] Read more.

The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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18 pages, 672 KiB

Open AccessArticle

An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning

by Ashir Javeed, Ana Luiza Dallora, Johan Sanmartin Berglund and Peter Anderberg

Life 2022, 12(7), 1097; https://doi.org/10.3390/life12071097 - 21 Jul 2022

Cited by 12 | Viewed by 2866

Abstract

Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning [...] Read more.

Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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10 pages, 1603 KiB

Open AccessArticle

MegaD: Deep Learning for Rapid and Accurate Disease Status Prediction of Metagenomic Samples

by Yassin Mreyoud, Myoungkyu Song, Jihun Lim and Tae-Hyuk Ahn

Life 2022, 12(5), 669; https://doi.org/10.3390/life12050669 - 30 Apr 2022

Cited by 2 | Viewed by 2995

Abstract

The diversity within different microbiome communities that drive biogeochemical processes influences many different phenotypes. Analyses of these communities and their diversity by countless microbiome projects have revealed an important role of metagenomics in understanding the complex relation between microbes and their environments. This [...] Read more.

The diversity within different microbiome communities that drive biogeochemical processes influences many different phenotypes. Analyses of these communities and their diversity by countless microbiome projects have revealed an important role of metagenomics in understanding the complex relation between microbes and their environments. This relationship can be understood in the context of microbiome composition of specific known environments. These compositions can then be used as a template for predicting the status of similar environments. Machine learning has been applied as a key component to this predictive task. Several analysis tools have already been published utilizing machine learning methods for metagenomic analysis. Despite the previously proposed machine learning models, the performance of deep neural networks is still under-researched. Given the nature of metagenomic data, deep neural networks could provide a strong boost to growth in the prediction accuracy in metagenomic analysis applications. To meet this urgent demand, we present a deep learning based tool that utilizes a deep neural network implementation for phenotypic prediction of unknown metagenomic samples. (1) First, our tool takes as input taxonomic profiles from 16S or WGS sequencing data. (2) Second, given the samples, our tool builds a model based on a deep neural network by computing multi-level classification. (3) Lastly, given the model, our tool classifies an unknown sample with its unlabeled taxonomic profile. In the benchmark experiments, we deduced that an analysis method facilitating a deep neural network such as our tool can show promising results in increasing the prediction accuracy on several samples compared to other machine learning models. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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12 pages, 1150 KiB

Open AccessArticle

Identifying Predictors of COVID-19 Mortality Using Machine Learning

by Tsz-Kin Wan, Rui-Xuan Huang, Thomas Wetere Tulu, Jun-Dong Liu, Asmir Vodencarevic, Chi-Wah Wong and Kei-Hang Katie Chan

Life 2022, 12(4), 547; https://doi.org/10.3390/life12040547 - 06 Apr 2022

Cited by 8 | Viewed by 2393

Abstract

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality [...] Read more.

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84–0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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14 pages, 11582 KiB

Open AccessArticle

SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network

by Xueting Han, Ruixia Xie, Xutao Li and Junyi Li

Life 2022, 12(2), 319; https://doi.org/10.3390/life12020319 - 21 Feb 2022

Cited by 14 | Viewed by 3321

Abstract

Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug–drug interactions widely and effectively before the drugs enter the [...] Read more.

Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug–drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug–drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug–drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug–drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug–drug interactions. Five out of the top ten predicted new drug–drug interactions are verified from the latest database, which proves the credibility of SmileGNN. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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10 pages, 1331 KiB

Open AccessArticle

Tandem Use of OvMANE1 and Ov-16 ELISA Tests Increases the Sensitivity for the Diagnosis of Human Onchocerciasis

by Cabirou Mounchili Shintouo, Stephen Mbigha Ghogomu, Robert Adamu Shey, An Hotterbeekx, Emel Yagmur, Tony Mets, Luc Vanhamme, Robert Colebunders, Jacob Souopgui and Rose Njemini

Life 2021, 11(12), 1284; https://doi.org/10.3390/life11121284 - 23 Nov 2021

Cited by 3 | Viewed by 1603

Abstract

The current serological test for human onchocerciasis relies on IgG4 reactivity against the parasite Ov-16 antigen, with reported sensitivities of only 60–80%. As control programs move from control to elimination, it is imperative to identify novel molecules that could improve the serodiagnosis reliability [...] Read more.

The current serological test for human onchocerciasis relies on IgG4 reactivity against the parasite Ov-16 antigen, with reported sensitivities of only 60–80%. As control programs move from control to elimination, it is imperative to identify novel molecules that could improve the serodiagnosis reliability of this disease. In this study we compared the sensitivity of total IgG against OvMANE1—a chimeric antigen previously identified as a potential biomarker of human onchocerciasis—with that of an Ov-16 antibody test to detect an Onchocerca volvulus infection in persons presenting with microfilaria in skin snips. One hundred and ninety serum samples were obtained from persons with epilepsy in an onchocerciasis-endemic area at Ituri in the Democratic Republic of Congo where ivermectin has never been distributed. Fifty-nine (31.1%) samples were from individuals with a positive skin snip test; 41 (69.5%) of these 59 samples were positive with the OvMANE1 test and 41 (69.5%) with the Ov-16 test; 30 (50.8%) samples were positive for both tests and in 52 (88.1%) at least one of the tests was positive. Testing the 131 sera from persons with a negative skin snip result revealed that 63 (48.1%) were positive exclusively with the OvMANE1 test, 13 (9.9%) exclusively with the Ov-16 test and 25 (19.1%) with both tests. Nine European samples from individuals without past travel history in onchocerciasis endemic zones and 15 samples from Rwanda, a hypoendemic country for onchocerciasis were all negative for the OvMANE1 and Ov-16 tests. However, the specificity of both tests was difficult to determine due to the lack of a gold standard for antibody tests. In conclusion, the tandem use of OvMANE1 and Ov-16 tests improves the sensitivity of detecting Onchocerca volvulus seropositive individuals but, the OvMANE1 test needs to be further evaluated on samples from a population infected with other helminths to cautiously address its specificity. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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26 pages, 14148 KiB

Open AccessArticle

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

by Theyazn H. H. Aldhyani and Hasan Alkahtani

Life 2021, 11(11), 1118; https://doi.org/10.3390/life11111118 - 21 Oct 2021

Cited by 20 | Viewed by 2573

Abstract

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction [...] Read more.

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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12 pages, 2358 KiB

Open AccessArticle

A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients

by Sikandar Ali, Ali Hussain, Satyabrata Aich, Moo Suk Park, Man Pyo Chung, Sung Hwan Jeong, Jin Woo Song, Jae Ha Lee and Hee Cheol Kim

Life 2021, 11(10), 1092; https://doi.org/10.3390/life11101092 - 15 Oct 2021

Cited by 7 | Viewed by 2288

Abstract

Idiopathic pulmonary fibrosis, which is one of the lung diseases, is quite rare but fatal in nature. The disease is progressive, and detection of severity takes a long time as well as being quite tedious. With the advent of intelligent machine learning techniques, [...] Read more.

Idiopathic pulmonary fibrosis, which is one of the lung diseases, is quite rare but fatal in nature. The disease is progressive, and detection of severity takes a long time as well as being quite tedious. With the advent of intelligent machine learning techniques, and also the effectiveness of these techniques, it was possible to detect many lung diseases. So, in this paper, we have proposed a model that could be able to detect the severity of IPF at the early stage so that fatal situations can be controlled. For the development of this model, we used the IPF dataset of the Korean interstitial lung disease cohort data. First, we preprocessed the data while applying different preprocessing techniques and selected 26 highly relevant features from a total of 502 features for 2424 subjects. Second, we split the data into 80% training and 20% testing sets and applied oversampling on the training dataset. Third, we trained three state-of-the-art machine learning models and combined the results to develop a new soft voting ensemble-based model for the prediction of severity of IPF disease in patients with this chronic lung disease. Hyperparameter tuning was also performed to get the optimal performance of the model. Fourth, the performance of the proposed model was evaluated by calculating the accuracy, AUC, confusion matrix, precision, recall, and F1-score. Lastly, our proposed soft voting ensemble-based model achieved the accuracy of 0.7100, precision 0.6400, recall 0.7100, and F1-scores 0.6600. This proposed model will help the doctors, IPF patients, and physicians to diagnose the severity of the IPF disease in its early stages and assist them to take proactive measures to overcome this disease by enabling the doctors to take necessary decisions pertaining to the treatment of IPF disease. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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14 pages, 606 KiB

Open AccessArticle

Increased Pace of Aging in COVID-Related Mortality

by Fedor Galkin, Austin Parish, Evelyne Bischof, John Zhang, Polina Mamoshina and Alex Zhavoronkov

Life 2021, 11(8), 730; https://doi.org/10.3390/life11080730 - 22 Jul 2021

Cited by 11 | Viewed by 17285

Abstract

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed [...] Read more.

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients’ pace of aging with BloodAge—a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (

Δ

= 0.8–1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001–1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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14 pages, 516 KiB

Open AccessArticle

First-Trimester Maternal Folic Acid Supplementation Modifies the Effects of Risk Factors Exposures on Congenital Heart Disease in Offspring

by Yanji Qu, Shao Lin, Michael S. Bloom, Ximeng Wang, Zhiqiang Nie, Yanqiu Ou, Jinzhuang Mai, Xiangmin Gao, Yong Wu, Jimei Chen, John Justino, Hongzhuan Tan, Jian Zhuang and Xiaoqing Liu

Life 2021, 11(8), 724; https://doi.org/10.3390/life11080724 - 21 Jul 2021

Cited by 4 | Viewed by 2201

Abstract

This study aimed to examine effect modification of maternal risk factor exposures and congenital heart disease (CHD) by maternal folic acid supplementation (FAS)/non-FAS. We included 8379 CHD cases and 6918 CHD-free controls from 40 clinical centers in Guangdong Province, Southern China, 2004–2016. Controls [...] Read more.

This study aimed to examine effect modification of maternal risk factor exposures and congenital heart disease (CHD) by maternal folic acid supplementation (FAS)/non-FAS. We included 8379 CHD cases and 6918 CHD-free controls from 40 clinical centers in Guangdong Province, Southern China, 2004–2016. Controls were randomly chosen from malformation-free fetuses and infants and frequency matched to the echocardiogram-confirmed cases by enrollment hospital and year of birth. We used multiple regression models to evaluate interactions between FAS/non-FAS and risk factors on CHDs and major CHD categories, adjusted for confounding variables. We detected statistically significant additive and multiplicative interactions between maternal FAS/non-FAS and first-trimester fever, viral infection, and threatened abortion on CHDs. An additive interaction on CHDs was also identified between non-FAS and living in a newly renovated home. We observed a statistically significant dose-response relationship between non-FAS and a greater number of maternal risk factors on CHDs. Non-FAS and maternal risk factors interacted additively on multiple critical CHDs, conotruncal defects, and right ventricular outflow tract obstruction. Maternal risk factor exposures may have differential associations with CHD risk in offspring, according to FAS. These findings may inform the design of targeted interventions to prevent CHDs in highly susceptible population groups. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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Review

Jump to: Research

26 pages, 1274 KiB

Open AccessReview

An Immunological Perspective of Circulating Tumor Cells as Diagnostic Biomarkers and Therapeutic Targets

by Eunice Dotse, King H. Lim, Meijun Wang, Kevin Julio Wijanarko and Kwan T. Chow

Life 2022, 12(2), 323; https://doi.org/10.3390/life12020323 - 21 Feb 2022

Cited by 6 | Viewed by 3472

Abstract

Immune modulation is a hallmark of cancer. Cancer–immune interaction shapes the course of disease progression at every step of tumorigenesis, including metastasis, of which circulating tumor cells (CTCs) are regarded as an indicator. These CTCs are a heterogeneous population of tumor cells that [...] Read more.

Immune modulation is a hallmark of cancer. Cancer–immune interaction shapes the course of disease progression at every step of tumorigenesis, including metastasis, of which circulating tumor cells (CTCs) are regarded as an indicator. These CTCs are a heterogeneous population of tumor cells that have disseminated from the tumor into circulation. They have been increasingly studied in recent years due to their importance in diagnosis, prognosis, and monitoring of treatment response. Ample evidence demonstrates that CTCs interact with immune cells in circulation, where they must evade immune surveillance or modulate immune response. The interaction between CTCs and the immune system is emerging as a critical point by which CTCs facilitate metastatic progression. Understanding the complex crosstalk between the two may provide a basis for devising new diagnostic and treatment strategies. In this review, we will discuss the current understanding of CTCs and the complex immune-CTC interactions. We also present novel options in clinical interventions, targeting the immune-CTC interfaces, and provide some suggestions on future research directions. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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20 pages, 1072 KiB

Open AccessReview

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

by Younghoon Seo, Hyemin Jang and Hyejoo Lee

Life 2022, 12(2), 275; https://doi.org/10.3390/life12020275 - 12 Feb 2022

Cited by 9 | Viewed by 2599

Abstract

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and [...] Read more.

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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39 pages, 884 KiB

Open AccessReview

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

by Linjing Liu, Xingjian Chen, Olutomilayo Olayemi Petinrin, Weitong Zhang, Saifur Rahaman, Zhi-Ri Tang and Ka-Chun Wong

Life 2021, 11(7), 638; https://doi.org/10.3390/life11070638 - 30 Jun 2021

Cited by 26 | Viewed by 4742

Abstract

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships [...] Read more.

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics. Full article

(This article belongs to the Special Issue Disease Prediction and Prevention: From Computational Biology and Artificial Intelligence to Epidemiology and Clinical Sciences)

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