Julian M. W. Quinn
Orcid: 0000-0001-9674-9646
According to our database1,
Julian M. W. Quinn
authored at least 25 papers
between 2018 and 2023.
Collaborative distances:
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Bibliography
2023
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN.
Neural Networks, May, 2023
2022
Machine learning models for classification and identification of significant attributes to detect type 2 diabetes.
Health Inf. Sci. Syst., 2022
2021
TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.
Knowl. Based Syst., 2021
Comput. Biol. Medicine, 2021
Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer.
Comput. Biol. Medicine, 2021
Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison.
Comput. Biol. Medicine, 2021
Machine learning-based statistical analysis for early stage detection of cervical cancer.
Comput. Biol. Medicine, 2021
A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.
Comput. Biol. Medicine, 2021
Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases.
Briefings Bioinform., 2021
Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.
Briefings Bioinform., 2021
Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities.
Briefings Bioinform., 2021
Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions.
Briefings Bioinform., 2021
Gene expression profiling of SARS-CoV-2 infections reveal distinct primary lung cell and systemic immune infection responses that identify pathways relevant in COVID-19 disease.
Briefings Bioinform., 2021
2020
A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients.
Expert Syst. Appl., 2020
Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data.
CoRR, 2020
Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19.
CoRR, 2020
Network-Based Computational Approach to Identify Delineating Common Cell Pathways Influencing Type 2 Diabetes and Diseases of Bone and Joints.
IEEE Access, 2020
2019
PeerJ Comput. Sci., 2019
Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality.
J. Biomed. Informatics, 2019
Comput. Biol. Medicine, 2019
A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue.
Comput. Biol. Medicine, 2019
Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities.
IEEE Access, 2019
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders.
IEEE Access, 2019
2018
A Network-Based Approach to Identify Molecular Signatures and Comorbidities of Thyroid Cancer.
Proceedings of International Joint Conference on Computational Intelligence, 2018