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Yang J, Gong Z, Dong J, Bi H, Wang B, Du K, Zhang C, Chen L. lncRNA XIST inhibition promotes M2 polarization of microglial and aggravates the spinal cord injury via regulating miR-124-3p / IRF1 axis. Heliyon 2023; 9:e17852. [PMID: 37455998 PMCID: PMC10344764 DOI: 10.1016/j.heliyon.2023.e17852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023] Open
Abstract
Spinal cord injury (SCI) has a high disability rate and mortality rate. Recently, LncRNA XIST has been found to be involved in the regulation of inflammatory responses. Therefore, we aimed to investigate the role of XIST in the occurrence and development of SCI and the specific regulation mechanism. Methods: 100 ng/mL lipopolysaccharide (LPS) was used to treat mouse microglia BV2 cells. Hitting spinal cord was performed to C57BL/6 mice for establishing SCI model. Real-time reverse transcriptase-polymerase chain reaction (RT-qPCR), Western blot, Immunofluorescence (IF) and Enzyme linked immunosorbent assay (ELISA) experiments were used to explore the function of XIST, miR-124-3p and IRF1 in LPS-induced BV2 cells. RT-qPCR, Nissl staining, IF, Western blot and ELISA experiment were performed to study the function of XIST in SCI mice. Dual-luciferase reporter assay, RNA immunoprecipitation (RIP), RT-qPCR and Western blot assays were utilized to identify the interaction among XIST, miR-124-3p and IRF1. Results: XIST was upregulated in LPS-induced BV2 cells and spinal cord tissues of SCI mice. Overexpression of XIST promoted the M1 microphages polarization and cytokines concentration in LPS-stimulated BV2 cells, aggravated SCI of mice. Downregulated XIST promoted M1-to-M2 conversion of microglial and relieved the injury of SCI mice. Mechanism verification indicated that XIST acted as a molecular sponge of miR-124-3p and regulated IRF1 expression. Increased miR-124-3p or reduced IRF1 inhibited M1 polarization of microglial and decreased the production of inflammatory cytokines in LPS-induced BV2 cells. Increased XIST or decreased miR-124-3p had an opposite of on LPS-induced BV2 cells. Conclusion: Overexpression of XIST enhanced M1 polarization of microglia and promoted the level of inflammatory cytokines through sponging miR-124-3p and regulating IRF1 expression.
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Affiliation(s)
| | | | | | | | | | | | | | - Lingqiang Chen
- Corresponding author. Department of Orthopaedics, The First affiliated hospital of Kunming medical University, No.295 Xichang Rd, Kunming 650032, Yunnan, China
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2
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Xu Q, Lei H, Li X, Li F, Shi H, Wang G, Sun A, Wang Y, Peng B. Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients. Heliyon 2023; 9:e12681. [PMID: 36632097 PMCID: PMC9826862 DOI: 10.1016/j.heliyon.2022.e12681] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 01/07/2023] Open
Abstract
Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.
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Affiliation(s)
- Qianjie Xu
- Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, China
| | - Haike Lei
- Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Xiaosheng Li
- Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Fang Li
- Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Hao Shi
- Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Guixue Wang
- MOE Key Lab for Biorheological Science and Technology, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering Chongqing University, Chongqing, 400030, China
| | - Anlong Sun
- Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China,Corresponding author.
| | - Ying Wang
- Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China,Corresponding author.
| | - Bin Peng
- Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, China,Corresponding author.
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3
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Carreras J, Roncador G, Hamoudi R. Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels. Cancers (Basel) 2022; 14:5318. [PMID: 36358737 PMCID: PMC9657332 DOI: 10.3390/cancers14215318] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 08/01/2023] Open
Abstract
Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.
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Affiliation(s)
- Joaquim Carreras
- Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan
| | - Giovanna Roncador
- Monoclonal Antibodies Unit, Spanish National Cancer Research Center (Centro Nacional de Investigaciones Oncologicas, CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain
| | - Rifat Hamoudi
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, Gower Street, London WC1E 6BT, UK
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4
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Application of kNN and SVM to predict the prognosis of advanced schistosomiasis. Parasitol Res 2022; 121:2457-2460. [PMID: 35767047 DOI: 10.1007/s00436-022-07583-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 06/17/2022] [Indexed: 10/17/2022]
Abstract
Predictive models for prognosis of small sample advanced schistosomiasis patients have not been well studied. We aimed to construct prognostic predictive models of small sample advanced schistosomiasis patients using two machine learning algorithms, k nearest neighbour (kNN) and support vector machine (SVM) utilising routinely available data under the government medical assistance programme. The predictive models were derived from 229 patients from Xiantao and externally validated by 77 patients of Jiayu, two county-level cities in Hubei province, China. Candidate predictors were selected according to expert opinions and literature reports, including clinical features, sociodemographic characteristics, and medical examinations results. An area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models' predictive performances. The AUC values were 0.879 for the kNN model and 0.890 for the SVM model in the training set, 0.852 for the kNN model, and 0.785 for the SVM model in the external validation set. The kNN and SVM models can be used to improve the health services provided by healthcare planners, clinicians, and policymakers.
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5
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Gerussi A, Verda D, Bernasconi DP, Carbone M, Komori A, Abe M, Inao M, Namisaki T, Mochida S, Yoshiji H, Hirschfield G, Lindor K, Pares A, Corpechot C, Cazzagon N, Floreani A, Marzioni M, Alvaro D, Vespasiani-Gentilucci U, Cristoferi L, Valsecchi MG, Muselli M, Hansen BE, Tanaka A, Invernizzi P, Hansen BE, Tanaka A, Invernizzi P. Machine learning in primary biliary cholangitis: A novel approach for risk stratification. Liver Int 2022; 42:615-627. [PMID: 34951722 DOI: 10.1111/liv.15141] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 12/13/2021] [Accepted: 12/16/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND & AIMS Machine learning (ML) provides new approaches for prognostication through the identification of novel subgroups of patients. We explored whether ML could support disease sub-phenotyping and risk stratification in primary biliary cholangitis (PBC). METHODS ML was applied to an international dataset of PBC patients. The dataset was split into a derivation cohort (training set) and a validation cohort (validation set), and key clinical features were analysed. The outcome was a composite of liver-related death or liver transplantation. ML and standard survival analysis were performed. RESULTS The training set was composed of 11,819 subjects, while the validation set was composed of 1,069 subjects. ML identified four clusters of patients characterized by different phenotypes and long-term prognosis. Cluster 1 (n = 3566) included patients with excellent prognosis, whereas Cluster 2 (n = 3966) consisted of individuals at worse prognosis differing from Cluster 1 only for albumin levels around the limit of normal. Cluster 3 (n = 2379) included young patients with florid cholestasis and Cluster 4 (n = 1908) comprised advanced cases. Further sub-analyses on the dynamics of albumin within the normal range revealed that ursodeoxycholic acid-induced increase of albumin >1.2 x lower limit of normal (LLN) is associated with improved transplant-free survival. CONCLUSIONS Unsupervised ML identified four novel groups of PBC patients with different phenotypes and prognosis and highlighted subtle variations of albumin within the normal range. Therapy-induced increase of albumin >1.2 x LLN should be considered a treatment goal.
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Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | | | - Davide Paolo Bernasconi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Marco Carbone
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Atsumasa Komori
- Clinical Research Center, National Hospital Organization (NHO) Nagasaki Medical Center, Nagasaki, Japan
| | - Masanori Abe
- Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Mie Inao
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Tadashi Namisaki
- Department of Gastroenterology, Nara Medical University, Nara, Japan
| | - Satoshi Mochida
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Hitoshi Yoshiji
- Department of Gastroenterology, Nara Medical University, Nara, Japan
| | - Gideon Hirschfield
- Toronto Centre for Liver Disease, Toronto Western & General Hospital, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Keith Lindor
- Division of Gastroenterology and Hepatology, Mayo Clinic, Phoenix, Arizona, United States.,Arizona State University, Phoenix, Arizona, United States
| | - Albert Pares
- Liver Unit, Hospital Clínic, CIBERehd, IDIBAPS, University of Barcelona, Barcelona, Spain.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), Hospital Clinic de Barcelona, Barcelona, Spain
| | - Christophe Corpechot
- Centre de Référence des Maladies Inflammatoires des Voies Biliaires et des Hépatites auto-immunes, Hôpital Saint- Antoine, APHP-Sorbonne Université, Paris, France.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), Hôpital Saint-Antoine, Paris, France
| | - Nora Cazzagon
- Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), Azienda Ospedale - Università Padova, Padova, Italy
| | - Annarosa Floreani
- Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy.,Scientific Institute for Research, Hospitalization and Healthcare, Verona, Italy
| | - Marco Marzioni
- Division of Gastroenterology and Hepatology, Ospedali Riuniti University Hospital, Ancona, Italy
| | - Domenico Alvaro
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | | | - Laura Cristoferi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy.,Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Maria Grazia Valsecchi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Marco Muselli
- Rulex Inc, Newton, Massachusetts, USA.,Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genova, Italy
| | - Bettina E Hansen
- Clinical Research Center, National Hospital Organization (NHO) Nagasaki Medical Center, Nagasaki, Japan.,Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Atsushi Tanaka
- Department of Medicine, Teikyo University School of Medicine, Tokyo, Japan
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Bettina E Hansen
- Clinical Research Center, National Hospital Organization (NHO) Nagasaki Medical Center, Nagasaki, Japan.,Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Atsushi Tanaka
- Department of Medicine, Teikyo University School of Medicine, Japan
| | - Pietro Invernizzi
- Division of Gastroenterology, Centre for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.,European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
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6
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Muhsen IN, Shyr D, Sung AD, Hashmi SK. Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases. Clin Hematol Int 2021; 3:13-20. [PMID: 34595462 PMCID: PMC8432325 DOI: 10.2991/chi.k.201130.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 11/05/2020] [Indexed: 12/23/2022] Open
Abstract
The use of machine learning (ML) and deep learning (DL) methods in hematology includes diagnostic, prognostic, and therapeutic applications. This increase is due to the improved access to ML and DL tools and the expansion of medical data. The utilization of ML remains limited in clinical practice, with some disciplines further along in their adoption, such as radiology and histopathology. In this review, we discuss the current uses of ML in diagnosis in the field of hematology, including image-recognition, laboratory, and genomics-based diagnosis. Additionally, we provide an introduction to the fields of ML and DL, highlighting current trends, limitations, and possible areas of improvement.
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Affiliation(s)
- Ibrahim N Muhsen
- Department of Medicine, Houston Methodist Hospital, Houston, TX, USA
| | - David Shyr
- Division of Stem Cell Transplantation and Regenerative Medicine, Stanford School of Medicine, Palo Alto, CA, USA
| | - Anthony D Sung
- Department of Medicine, Division of Hematologic Malignancies and Cellular Therapy, Duke University School of Medicine, NC, USA
| | - Shahrukh K Hashmi
- Department of Medicine, Mayo Clinic, Rochester, MN, USA.,Department of Medicine, Sheikh Shakbout Medical City, Abu Dhabi, UAE
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7
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Daher AW, Rizik A, Muselli M, Chible H, Caviglia DD. Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge. SENSORS 2021; 21:s21196526. [PMID: 34640846 PMCID: PMC8512253 DOI: 10.3390/s21196526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/16/2021] [Accepted: 09/27/2021] [Indexed: 11/28/2022]
Abstract
Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.
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Affiliation(s)
- Ali Walid Daher
- COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; (A.W.D.); (A.R.)
- MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon;
- Consiglio Nazionale delle Ricerche, Institute of Electronics Computer and Telecommunication Engineering (IEIIT), 16149 Genoa, Italy;
- Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy
| | - Ali Rizik
- COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; (A.W.D.); (A.R.)
- MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon;
| | - Marco Muselli
- Consiglio Nazionale delle Ricerche, Institute of Electronics Computer and Telecommunication Engineering (IEIIT), 16149 Genoa, Italy;
- Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy
| | - Hussein Chible
- MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon;
| | - Daniele D. Caviglia
- COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; (A.W.D.); (A.R.)
- Correspondence: ; Tel.: +39-010-33-56-587
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8
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Gumaei A, Sammouda R, Al-Rakhami M, AlSalman H, El-Zaart A. Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression. Health Informatics J 2021; 27:1460458221989402. [PMID: 33570011 DOI: 10.1177/1460458221989402] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.
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Affiliation(s)
- Abdu Gumaei
- Research Chair of Pervasive and Mobile Computing, King Saud University, Saudi Arabia.,Taiz University, Yemen
| | | | - Mabrook Al-Rakhami
- Research Chair of Pervasive and Mobile Computing, King Saud University, Saudi Arabia
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9
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Moran-Sanchez J, Santisteban-Espejo A, Martin-Piedra MA, Perez-Requena J, Garcia-Rojo M. Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis. Biomolecules 2021; 11:793. [PMID: 34070632 PMCID: PMC8227233 DOI: 10.3390/biom11060793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/13/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People's Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.
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Affiliation(s)
- Julia Moran-Sanchez
- Division of Hematology and Hemotherapy, Puerta del Mar Hospital, 11009 Cadiz, Spain;
- Ph.D Program of Clinical Medicine and Surgery, University of Cadiz, 11009 Cadiz, Spain
| | - Antonio Santisteban-Espejo
- Pathology Department, Puerta del Mar Hospital, 11009 Cadiz, Spain; (J.P.-R.); (M.G.-R.)
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), University of Cadiz, 11009 Cadiz, Spain
| | | | - Jose Perez-Requena
- Pathology Department, Puerta del Mar Hospital, 11009 Cadiz, Spain; (J.P.-R.); (M.G.-R.)
| | - Marcial Garcia-Rojo
- Pathology Department, Puerta del Mar Hospital, 11009 Cadiz, Spain; (J.P.-R.); (M.G.-R.)
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), University of Cadiz, 11009 Cadiz, Spain
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10
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Verda D, Parodi S, Ferrari E, Muselli M. Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods. BMC Bioinformatics 2019; 20:390. [PMID: 31757200 PMCID: PMC6873393 DOI: 10.1186/s12859-019-2953-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 06/14/2019] [Indexed: 12/27/2022] Open
Abstract
Background Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier. Results LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98–1.0) and outperformed any other method except SVM. Conclusions LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.
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Affiliation(s)
| | - Stefano Parodi
- Epidemiology and Biostatistics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Marco Muselli
- Rulex Inc., Newton, MA, USA. .,Institute of Electronics, Computer and Telecommunication Engineering National Research Council of Italy, Via De Marini, 6, 16149, Genoa, Italy.
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Benedetti A, Khoo J, Sharma S, Facco P, Barolo M, Zomer S. Data analytics on raw material properties to accelerate pharmaceutical drug development. Int J Pharm 2019; 563:122-134. [PMID: 30951857 DOI: 10.1016/j.ijpharm.2019.04.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 03/29/2019] [Accepted: 04/01/2019] [Indexed: 12/19/2022]
Abstract
Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.
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Affiliation(s)
- Antonio Benedetti
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK.
| | - Jiyi Khoo
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK
| | - Sandeep Sharma
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK
| | - Pierantonio Facco
- CAPE-Lab, Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy
| | - Massimiliano Barolo
- CAPE-Lab, Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131 Padova, Italy
| | - Simeone Zomer
- Product Development and Supply, GlaxoSmithKline Research & Development, Park Road, SG12 0DP Ware, UK
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Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine. J Gambl Stud 2018; 33:1121-1137. [PMID: 28255941 DOI: 10.1007/s10899-017-9679-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative method of supervised data mining (Logic Learning Machine or LLM). Data were taken from a pilot cross-sectional study to identify subjects with PG behaviour, assessed by two internationally validated scales (SOGS and Lie/Bet). Information was obtained from 251 gamblers recruited in six betting establishments. Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire. The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend. Furthermore, the combination of LLM and LR indicated the presence of two different types of PG, namely: (a) daily gamblers, more prone to illegal behaviour, with poor money management skills and who started gambling at an early age, and (b) non-daily gamblers, characterised by superstitious beliefs and a higher preference for immediate reward games. Finally, instant gratification games were strongly associated with the number of games usually played. Studies on gamblers habitually frequently betting shops are rare. The finding of different types of PG by habitual gamblers deserves further analysis in larger studies. Advanced data mining algorithms, like LLM, are powerful tools and potentially useful in identifying risk factors for PG.
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