scholarly journals Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Rahila Sardar ◽  
Arun Sharma ◽  
Dinesh Gupta
Keyword(s):  
Machine Learning ◽  
Sensitivity And Specificity ◽  
Protein Profile ◽  
Clinical Information ◽  
Classification Model ◽  
Clinical Parameters ◽  
Proteomics Data ◽  
Model Based ◽  
Diagnosis And Prognosis ◽  
Maximum Accuracy

With the availability of COVID-19-related clinical data, healthcare researchers can now explore the potential of computational technologies such as artificial intelligence (AI) and machine learning (ML) to discover biomarkers for accurate detection, early diagnosis, and prognosis for the management of COVID-19. However, the identification of biomarkers associated with survival and deaths remains a major challenge for early prognosis. In the present study, we have evaluated and developed AI-based prediction algorithms for predicting a COVID-19 patient’s survival or death based on a publicly available dataset consisting of clinical parameters and protein profile data of hospital-admitted COVID-19 patients. The best classification model based on clinical parameters achieved a maximum accuracy of 89.47% for predicting survival or death of COVID-19 patients, with a sensitivity and specificity of 85.71 and 92.45%, respectively. The classification model based on normalized protein expression values of 45 proteins achieved a maximum accuracy of 89.01% for predicting the survival or death, with a sensitivity and specificity of 92.68 and 86%, respectively. Interestingly, we identified 9 clinical and 45 protein-based putative biomarkers associated with the survival/death of COVID-19 patients. Based on our findings, few clinical features and proteins correlate significantly with the literature and reaffirm their role in the COVID-19 disease progression at the molecular level. The machine learning–based models developed in the present study have the potential to predict the survival chances of COVID-19 positive patients in the early stages of the disease or at the time of hospitalization. However, this has to be verified on a larger cohort of patients before it can be put to actual clinical practice. We have also developed a webserver CovidPrognosis, where clinical information can be uploaded to predict the survival chances of a COVID-19 patient. The webserver is available at http://14.139.62.220/covidprognosis/.

scholarly journals Predicting the Appearance of Hypotension During Hemodialysis Sessions Using Machine Learning Classifiers

2021 ◽  
Vol 18 (5) ◽  
pp. 2364
Author(s):  
Juan A. Gómez-Pulido ◽  
José M. Gómez-Pulido ◽  
Diego Rodríguez-Puyol ◽  
María-Luz Polo-Luque ◽  
Miguel Vargas-Lombardo
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Chronic Renal Disease ◽  
Clinical Information ◽  
Healthcare Personnel ◽  
Dialysis Session ◽  
Clinical Parameters ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Gender And Age

A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

scholarly journals Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 1535 ◽  
Author(s):  
Francisco Azuaje ◽  
Sang-Yoon Kim ◽  
Daniel Perez Hernandez ◽  
Gunnar Dittmar
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Diagnostic Value ◽  
Classification Model ◽  
Clinical Approach ◽  
Proteomics Data ◽  
Cell Renal Cell Carcinoma ◽  
Molecular Features ◽  
Genes Encoding ◽  
New Research

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.

scholarly journals Texture Analysis Based on Pi-Rads 4/5-Scored MRI Images Combined with Machine Learning to Distinguish Benign Lesions from Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Qi Zhou ◽  
Huming Yin ◽  
Xiaojie Ang ◽  
Yu Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Clinical Indicators ◽  
Model Based ◽  
Feature Parameters ◽  
Auc Value ◽  
Malignant Prostate

Abstract Background: To extract the texture features of Apparent Diffusion Coefficient (ADC) images in Mp-MRI and build a machine learning model based on radiomics texture analysis to determine its ability to distinguish benign from prostate cancer (PCa) lesions using PI-RADS 4/5 score.Materials and methods: First, use ImageJ software to obtain texture feature parameters based on ADC images; use R language to standardize texture feature parameters, and use Lasso regression to reduce the dimensionality of multiple feature parameters; then, use the feature parameters after dimensionality reduction to construct image-based groups. Learn R-Logistic, R-SVM, R-AdaBoost to identify the machine learning classification model of prostate benign and malignant nodules. Secondly, the clinical indicators of the patients were statistically analyzed, and the three clinical indicators with the largest AUC values were selected to establish a classification model based on clinical indicators of benign and malignant prostate nodules. Finally, compare the performance of the model based on radiomics texture features and clinical indicators to identify benign and malignant prostate nodules in PI-RADS 4/5.Results: The experimental results show that the AUC of the R-Logistic model test set is 0.838, which is higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-Logistic classification model formula is: Y_radiomics=9.396-7.464*median ADC-0.584 *kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the 3 indicators with the highest discrimination efficiency are PSA, Fib, LDL-C, and the corresponding C-Logistic classification model formula is: Y_clinical =-2.608 +0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set is 0.860, which is smaller than the training set R-Logistic classification model AUC value of 0.936.Conclusion: The machine learning classifier model is established based on the texture features of radiomics. It has a good classification performance in identifying benign and malignant nodules of the prostate in PI-RADS 4/5. This has certain potential and clinical value for patients with prostate cancer to adopt different treatment methods and prognosis.

scholarly journals Protein Signature in Saliva of Temporomandibular Disorders Myalgia

2020 ◽  
Vol 21 (7) ◽  
pp. 2569 ◽  
Author(s):  
Hajer Jasim ◽  
Malin Ernberg ◽  
Anders Carlsson ◽  
Björn Gerdle ◽  
Bijar Ghafouri
Keyword(s):  
Temporomandibular Disorders ◽  
Protein Profile ◽  
Salivary Protein ◽  
Proteomics Data ◽  
Two Dimensional Gel Electrophoresis ◽  
Diagnosis And Prognosis ◽  
Liquid Chromatography Tandem Mass ◽  
Proteomic Study ◽  
Additional Sample ◽  
Protein Signature

In the last years, several attempts have been made to study specific biological markers of temporomandibular disorders (TMD). So far, no laboratory tests have been appropriately validated for the diagnosis and prognosis of these disorders. This study aimed to investigate the proteomic profile of the whole stimulated saliva of TMD myalgia patients in order to evaluate potential diagnostic and/or prognostic salivary candidate proteins which could be useful for the management of TMD. Twenty patients diagnosed with TMD myalgia according to the validated Diagnostic Criteria for TMD (DC/TMD) and 20 matched healthy pain-free controls were enrolled. Saliva samples were collected in the morning. Comparative proteomic analysis was performed with two-dimensional gel electrophoresis followed by identification with liquid chromatography–tandem mass spectrometry. Statistical analysis of the quantitative proteomics data revealed that 20 proteins were significantly altered in patients compared to controls. Among these proteins, 12 showed significantly increased levels, and 8 showed significantly decreased levels in patients with TMD myalgia compared to controls. The identified proteins are involved in metabolic processes, immune response, and stress response. This proteomic study shows that the salivary protein profile can discriminate patients with TMD myalgia from healthy subjects, but the protein signature has no correlation with the clinical features of TMD myalgia. Additional studies are needed to validate our observations in additional sample sets and to continue assessing the utility of saliva as a suitable sample for studying processes related to TMD myalgia.

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