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Hussein R, Abou-Shanab AM, Badr E. A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework. NPJ Syst Biol Appl 2024; 10:52. [PMID: 38760476 PMCID: PMC11101461 DOI: 10.1038/s41540-024-00371-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 04/16/2024] [Indexed: 05/19/2024] Open
Abstract
Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities.
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Affiliation(s)
- Rahma Hussein
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt
| | - Ahmed M Abou-Shanab
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt
| | - Eman Badr
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt.
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, 12613, Egypt.
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2
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Mumtaz H, Saqib M, Jabeen S, Muneeb M, Mughal W, Sohail H, Safdar M, Mehmood Q, Khan MA, Ismail SM. Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review. Front Med (Lausanne) 2023; 10:1227168. [PMID: 37849490 PMCID: PMC10577305 DOI: 10.3389/fmed.2023.1227168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023] Open
Abstract
The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.
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Affiliation(s)
| | | | | | - Muhammad Muneeb
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Wajiha Mughal
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Hassan Sohail
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Myra Safdar
- Armed Forces Institute of Cardiology and National Institute of Heart Diseases (AFIC-NIHD), Rawalpindi, Pakistan
| | - Qasim Mehmood
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Muhammad Ahsan Khan
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
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3
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Lee J, Liu C, Kim J, Chen Z, Sun Y, Rogers JR, Chung WK, Weng C. Deep learning for rare disease: A scoping review. J Biomed Inform 2022; 135:104227. [DOI: 10.1016/j.jbi.2022.104227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/22/2022] [Accepted: 10/07/2022] [Indexed: 10/31/2022]
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Omics Data and Data Representations for Deep Learning-Based Predictive Modeling. Int J Mol Sci 2022; 23:ijms232012272. [PMID: 36293133 PMCID: PMC9603455 DOI: 10.3390/ijms232012272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/03/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022] Open
Abstract
Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.
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5
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Chicco D, Oneto L. Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma. Health Informatics J 2021; 27:1460458220984205. [PMID: 33504243 DOI: 10.1177/1460458220984205] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Liver cancer kills approximately 800 thousand people annually worldwide, and its most common subtype is hepatocellular carcinoma (HCC), which usually affects people with cirrhosis. Predicting survival of patients with HCC remains an important challenge, especially because technologies needed for this scope are not available in all hospitals. In this context, machine learning applied to medical records can be a fast, low-cost tool to predict survival and detect the most predictive features from health records. In this study, we analyzed medical data of 165 patients with HCC: we employed computational intelligence to predict their survival, and to detect the most relevant clinical factors able to discriminate survived from deceased cases. Afterwards, we compared our data mining results with those obtained through statistical tests and scientific literature findings. Our analysis revealed that blood levels of alkaline-phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin are the most effective prognostic factors in this dataset. We found literature supporting association of these three factors with hepatoma, even though only AFP has been used in a prognostic index. Our results suggest that ALP and hemoglobin can be candidates for future HCC prognostic indexes, and that physicians could focus on ALP, AFP, and hemoglobin when studying HCC records.
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Affiliation(s)
| | - Luca Oneto
- Università di Genova, Italy; ZenaByte Srl
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6
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Chicco D, Oneto L. Data analytics and clinical feature ranking of medical records of patients with sepsis. BioData Min 2021; 14:12. [PMID: 33536030 PMCID: PMC7860202 DOI: 10.1186/s13040-021-00235-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/05/2021] [Indexed: 12/15/2022] Open
Abstract
Background Sepsis is a life-threatening clinical condition that happens when the patient’s body has an excessive reaction to an infection, and should be treated in one hour. Due to the urgency of sepsis, doctors and physicians often do not have enough time to perform laboratory tests and analyses to help them forecast the consequences of the sepsis episode. In this context, machine learning can provide a fast computational prediction of sepsis severity, patient survival, and sequential organ failure by just analyzing the electronic health records of the patients. Also, machine learning can be employed to understand which features in the medical records are more predictive of sepsis severity, of patient survival, and of sequential organ failure in a fast and non-invasive way. Dataset and methods In this study, we analyzed a dataset of electronic health records of 364 patients collected between 2014 and 2016. The medical record of each patient has 29 clinical features, and includes a binary value for survival, a binary value for septic shock, and a numerical value for the sequential organ failure assessment (SOFA) score. We disjointly utilized each of these three factors as an independent target, and employed several machine learning methods to predict it (binary classifiers for survival and septic shock, and regression analysis for the SOFA score). Afterwards, we used a data mining approach to identify the most important dataset features in relation to each of the three targets separately, and compared these results with the results achieved through a standard biostatistics approach. Results and conclusions Our results showed that machine learning can be employed efficiently to predict septic shock, SOFA score, and survival of patients diagnoses with sepsis, from their electronic health records data. And regarding clinical feature ranking, our results showed that Random Forests feature selection identified several unexpected symptoms and clinical components as relevant for septic shock, SOFA score, and survival. These discoveries can help doctors and physicians in understanding and predicting septic shock. We made the analyzed dataset and our developed software code publicly available online. Supplementary Information The online version contains supplementary material available at (10.1186/s13040-021-00235-0).
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Affiliation(s)
- Davide Chicco
- Krembil Research Institute, Toronto, Ontario, Canada.
| | - Luca Oneto
- Università di Genova, Genoa, Italy.,ZenaByte srl, Genoa, Italy
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Abstract
Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman's rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types which might need compression before processing, these measures would be unsuitable. In these cases, a siamese neural network may be the best choice: it consists of two identical artificial neural networks each capable of learning the hidden representation of an input vector. The two neural networks are both feedforward perceptrons, and employ error back-propagation during training; they work parallelly in tandem and compare their outputs at the end, usually through a cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications in a number of computational fields since its appearance in 1994. Additionally, we list the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model.
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陈 雯, 王 旭, 段 辉, 张 小, 董 婷, 聂 生. [Application of deep learning in cancer prognosis prediction model]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2020; 37:918-929. [PMID: 33140618 PMCID: PMC10320539 DOI: 10.7507/1001-5515.201909066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Indexed: 11/03/2022]
Abstract
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
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Affiliation(s)
- 雯 陈
- 上海理工大学 医学影像工程研究所(上海 200093)Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China
| | - 旭 王
- 上海理工大学 医学影像工程研究所(上海 200093)Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China
| | - 辉宏 段
- 上海理工大学 医学影像工程研究所(上海 200093)Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China
| | - 小兵 张
- 上海理工大学 医学影像工程研究所(上海 200093)Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China
| | - 婷 董
- 上海理工大学 医学影像工程研究所(上海 200093)Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China
| | - 生东 聂
- 上海理工大学 医学影像工程研究所(上海 200093)Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China
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9
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Chicco D, Jurman G. Survival prediction of patients with sepsis from age, sex, and septic episode number alone. Sci Rep 2020; 10:17156. [PMID: 33051513 PMCID: PMC7555553 DOI: 10.1038/s41598-020-73558-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022] Open
Abstract
Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.
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10
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Marchevsky AM, Diniz MA, Manzoor D, Walts AE. Prognosis in pathology: Are we "prognosticating" or only establishing correlations between independent variables and survival? A study with various analytics cautions about the overinterpretation of statistical results. Ann Diagn Pathol 2020; 46:151525. [PMID: 32353712 DOI: 10.1016/j.anndiagpath.2020.151525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 04/16/2020] [Indexed: 01/27/2023]
Abstract
Survival data from 225 patients with resected pulmonary typical carcinoids were analyzed with Kaplan-Meier statistics (K-M) and "deep learning" methods to illustrate the difference between establishing "correlations" and "prognostications". Cases were stratified into G1 and G2 classes using a ≤5% Ki-67% cut-point. Overall survival, number of patients at risk and 95% confidence intervals (CI) were estimated for the two classes. Seven neural network models (NN) were developed with GMDH Shell 3.8.2 and Statgraphics Centurion 18.1 software, using variable prior probabilities and different numbers of training vs testing cases. The NNs used age, sex, and pTNM, G1 and G2 as input neurons and "alive" and "dead" as output neurons. Areas under the curve (AUC) and other performance measures were evaluated for all models. Log-rank test showed a significant difference in overall survival between G1 and G2 (p < 0.001). However, 95% CI estimates showed considerable variability in survival at different time intervals. Including the number of patients at risk at different time intervals showed that most G2 patients had been censored by 100 weeks. The NN models provided variable "prognostications", with AUC ranging from 0.5 to 1 and variability in the sensitivity, specificity, and other performance measures. The results illustrate the limitations of survival statistics and NNs in predicting the prognosis of individual patients. The need for pathologists not to overinterpret the finding of significant correlations as "prognostic" or "predictive" for individual patients is discussed.
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Affiliation(s)
- Alberto M Marchevsky
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America.
| | - Marcio A Diniz
- Biostatistics and Bioinformatics Research Center, Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, United States of America
| | - Daniel Manzoor
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Ann E Walts
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
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11
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Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 2020; 20:16. [PMID: 32013925 PMCID: PMC6998201 DOI: 10.1186/s12911-020-1023-5] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 01/14/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. METHODS In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. RESULTS Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. CONCLUSIONS This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.
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Affiliation(s)
- Davide Chicco
- Krembil Research Institute, Toronto, Ontario, Canada
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12
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Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 2020; 21:6. [PMID: 31898477 PMCID: PMC6941312 DOI: 10.1186/s12864-019-6413-7] [Citation(s) in RCA: 1138] [Impact Index Per Article: 284.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 12/18/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. RESULTS The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. CONCLUSIONS In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.
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Affiliation(s)
- Davide Chicco
- Krembil Research Institute, Toronto, Ontario, Canada
- Peter Munk Cardiac Centre, Toronto, Ontario, Canada
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13
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Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR Mhealth Uhealth 2019; 7:e11966. [PMID: 31376272 PMCID: PMC6696854 DOI: 10.2196/11966] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/14/2019] [Accepted: 06/12/2019] [Indexed: 01/10/2023] Open
Abstract
The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.
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Affiliation(s)
- Igbe Tobore
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China.,Graduate University, Chinese Academy of Sciences, Beijing, China
| | - Jingzhen Li
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liu Yuhang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yousef Al-Handarish
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Abhishek Kandwal
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zedong Nie
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Wang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
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14
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Celi LA, Citi L, Ghassemi M, Pollard TJ. The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data. PLoS One 2019; 14:e0210232. [PMID: 30645625 PMCID: PMC6333339 DOI: 10.1371/journal.pone.0210232] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Recent years have seen a surge of studies in machine learning in health and biomedicine, driven by digitalization of healthcare environments and increasingly accessible computer systems for conducting analyses. Many of us believe that these developments will lead to significant improvements in patient care. Like many academic disciplines, however, progress is hampered by lack of code and data sharing. In bringing together this PLOS ONE collection on machine learning in health and biomedicine, we sought to focus on the importance of reproducibility, making it a requirement, as far as possible, for authors to share data and code alongside their papers.
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Affiliation(s)
- Leo A. Celi
- MIT Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, United States of America
| | - Luca Citi
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Marzyeh Ghassemi
- University of Toronto, Computer Science and Medicine, Toronto, Canada
- Vector Institute, Toronto, Canada
| | - Tom J. Pollard
- MIT Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, United States of America
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