Proceedings. International Conference on Computational Science and Computational Intelligence最新文献

{"title":"Smoking Classification Using Novel Plasma Cytokines by implementing Machine Learning and Statistical Methods.","authors":"Seema Singh Saharan, Pankaj Nagar, Kate Townsend Creasy, Eveline O Stock, James Feng, Mary J Malloy, John P Kane","doi":"10.1109/csci62032.2023.00118","DOIUrl":"https://doi.org/10.1109/csci62032.2023.00118","url":null,"abstract":"<p><p>Smoking is a major cause of premature and preventable death. Tobacco exposure has a detrimental effect on many organs and contributes to multiple diseases including chronic obstructive pulmonary disease (COPD), cardiovascular disease, cancer, and diabetes. Cytokines are inflammatory biomarkers that are mechanistically associated with smoking. Machine Learning algorithms allow for the quantitative assessment of the contributions of individual cytokines to tobacco-related diseases. The mapping of cytokines to disease can facilitate and direct treatment modalities. By the application of k Nearest Neighbor (k-NN) and Random Forest machine learning algorithms on 63 plasma cytokines we have demonstrated the classification of smoking. To ensure optimal results, performance improvement techniques such as k-fold cross validation and hyper parameter tuning are employed. Separability efficiency achieved by the models is evaluated using the Area Under the Receiver Operating Characteristic (AUROC) metric. The most significant cytokines that enabled the classification are identified and presented. The statistically significant difference for AUROC score of k-NN and Random Forest has been ascertained using the 2-sample independent t test. A reasonably good classification performance was achieved by k-NN algorithm with an AUROC metric of .87, and a 95% CI of (.823,.917). Random forest exceeded k-NN algorithm's performance, with a perfect AUROC score of 1 and a 95% CI of (1,1). From among the ten most prominent cytokines that contributed to the classification, the ones common to both algorithms are: LIF, IL22, G-CSF/CSF-3, TRAIL. AUROC scores for k-NN and Random Forest are significantly different (p-value = 5.105e-16). The discovery and transference of biomarkers such as cytokines from the platform of molecular investigation to clinical practice, can facilitate precision medicine-based therapeutic interventions.</p>","PeriodicalId":93614,"journal":{"name":"Proceedings. International Conference on Computational Science and Computational Intelligence","volume":"2023 ","pages":"686-694"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11500790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Logistic Regression and Statistical Regularization Techniques for Risk Classification of Coronary Artery Disease using Cytokines transported by high density lipoproteins.","authors":"Seema Singh Saharan, Pankaj Nagar, Kate Townsend Creasy, Eveline O Stock, James Feng, Mary J Malloy, John P Kane","doi":"10.1109/csci62032.2023.00114","DOIUrl":"10.1109/csci62032.2023.00114","url":null,"abstract":"<p><p>Coronary artery disease (CAD) is a leading cause of mortality in the world. It is important to be able to proactively assess the risk of the disease, using novel biomarkers like cytokines that are indicators of inflammation in addition to traditional predictors of risk. Atherosclerosis, the primary cause of CAD, is an inflammatory disease involving cytokines. Identifying which cytokines are specifically altered can advance diagnosis and personalized treatment. Emerging research demonstrates that cytokines are transported on high density lipoproteins (HDL). Therefore, it is important to explore the roles of HDL-associated cytokines in vascular inflammation. Machine Learning (ML) algorithms are enhancing pioneering research from the standpoint of precision medicine. This technology can materially enable the translation of scientific research to clinical practice. In this study we implemented logistic regression and the derived regularized techniques using age and multidimensional cytokine biomarkers with the objective of identification of individuals \"At Risk\" for CAD. These techniques were further empowered by k-fold cross validation and hyper parameter tuning. Of the numerous algorithms investigated, the three most prominent ones, assessed based on area under receiver operating characteristic (AUROC) score are as follows: logistic regression, least absolute shrinkage, and selection operator (LASSO) regression with feature selection and ridge regression with feature selection. Logistic regression demonstrated an AUROC score of .85 with a 95% Confidence Interval CI (.804, .897), LASSO regression achieved a better AUROC score of .875 with a 95% CI (.832, .917) and finally ridge regression with feature selection exhibited the highest AUROC score of .878 with a 95% CI (.837, .92). The 2-sample independent t test proved that the three techniques were statistically significantly different from each other. With regard to the best classification demonstrated by ridge regression with feature selection, the most prominent biomarkers identified for the best classification achieved by ridge regression by feature selection, in the order of importance are as follows: Age, IL-7, RANTES, IFN-gamma, IL-3, GM-CSF, IL-15, IP-10, GCSF, IL-12. The identification and quantification of cytokines transported by HDL provide novel mechanistic insights that can inform the assessment of risk and therapeutic intervention in CAD.</p>","PeriodicalId":93614,"journal":{"name":"Proceedings. International Conference on Computational Science and Computational Intelligence","volume":"2023 ","pages":"652-660"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Smoking Classification by Applying Neural Network with Variable Importance Using Cytokine Biomarkers.","authors":"Seema Singh Saharan, Pankaj Nagar, Kate Townsend Creasy, Eveline O Stock, James Feng, Mary J Malloy, John P Kane","doi":"10.1109/csci62032.2023.00115","DOIUrl":"10.1109/csci62032.2023.00115","url":null,"abstract":"<p><p>Cigarette smoking is a preventable epidemic that is a leading cause of death. It increases the risk of coronary heart disease, stroke, lung cancer, chronic obstructive lung diseases etc., multifold. Smoking tobacco is not only injurious to oneself but also to those who are exposed second hand. Smoking induces endothelial dysfunction via inflammatory cytokines that can be quantified precisely. Cytokines can be leveraged as powerful predictive biomarkers for identifying risk of potential diseases. Current advances in biomarker research are providing substantive evidence of the roles of cytokines in disease. This is driving precision-based diagnosis and translational therapeutic interventions. Innovative machine algorithms (ML) are pioneering transformative changes in the field of medical research. This research implements the Neural Networks (NN) algorithm to classify smokers versus non-smokers using 63 cytokines as predictor features. In addition to the fact that NN is a generative algorithm, which makes it a very powerful tool to achieve the objective of this differentiation, techniques like cross validation and hyperparameter tuning improve the efficacy of the algorithm. The study identified the 10 most impactful predictor features that contributed to the classification and then used these to characterize smokers versus non-smokers. Primarily, the study constructed and investigated two classifiers, of which the first implemented NN using the entire set of 63 cytokines and the second using 10 most informative cytokines. The performance of the first classifier, implemented using 63 cytokines, evaluated by area under receiver operating characteristic (AUROC), was extremely good with an AUROC score of .949 and 95% Confidence Interval (CI) (.923,.974). The second classifier that used the 10 most impactful cytokines with regard to the classification, demonstrated an exemplary performance, with an AUROC score of .995 and a 95% CI (.991,1). The 10 most impactful cytokines from the aspect of smoker versus non-smoker differentiation, listed in order of importance, include: I-TAC, IL-22, IL-2R, IL-3, HGF, IL-18, G-CSF-CSF-3, MIF, SDF-1alpha, MMP-1. To gain a deeper understanding of the effect of smoking on cytokine levels, a 2-sample independent t test was performed, ascertaining the statistical significance of the 63 cytokine levels in smokers versus non-smokers. Machine Learning using biomarkers such as cytokines will enhance the ability to predict the advent of a disease and its outcome, and lead to novel treatment strategies.</p>","PeriodicalId":93614,"journal":{"name":"Proceedings. International Conference on Computational Science and Computational Intelligence","volume":"2023 ","pages":"661-670"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social network analysis of biomedical research growth at a primarily undergraduate institution.","authors":"Amrina Ferdous,&nbsp;Diane B Smith,&nbsp;Tracy Yarnell,&nbsp;Julia Thom Oxford","doi":"10.1109/csci58124.2022.00138","DOIUrl":"10.1109/csci58124.2022.00138","url":null,"abstract":"<p><p>The National Institutes of Health Institutional Development Award Programs support the establishment and growth of biomedical research infrastructure in states that receive a low level of federal funding for biomedical research. The purpose of this investigation was to analyze the growth in research productivity over time. This program fostered an environment in which a biomedical research program could be developed and allowed to grow at Boise State University, a primarily undergraduate institution. The growth of the biomedical research community can be visualized through social network analysis.</p>","PeriodicalId":93614,"journal":{"name":"Proceedings. International Conference on Computational Science and Computational Intelligence","volume":"2022 ","pages":"754-759"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10586753/pdf/nihms-1908265.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collagen a1(XI) structure prediction by Alphafold 2.","authors":"Abu Sayeed Chowdhury,&nbsp;Julia Thom Oxford","doi":"10.1109/csci58124.2022.00108","DOIUrl":"10.1109/csci58124.2022.00108","url":null,"abstract":"<p><p>Collagen α1(XI) is a minor fibrillar collagen involved in the critical regulation of collagen fibrils such as nucleation, assembly, and regulation of fibril diameter. The amino propeptide domain of the collagen α1(XI) is retained on the surface of the collagen fibril for an extended period of time and may play a crucial role in the interaction with extracellular matrix glycosaminoglycans and other proteins during the process of fibrillogenesis. Understanding the mechanism of action of this protein will ultimately help us understand the organization and assembly of the extracellular matrix that underlies the structural integrity of connective tissues.</p>","PeriodicalId":93614,"journal":{"name":"Proceedings. International Conference on Computational Science and Computational Intelligence","volume":"2022 ","pages":"572-577"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10586751/pdf/nihms-1908254.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gateway Scholars Program - reducing barriers to STEM for undergraduate students through scholarship and supportive programs.","authors":"Amy Ulappa,&nbsp;Vicki Stieha,&nbsp;Diane B Smith,&nbsp;Julia Thom Oxford","doi":"10.1109/csci58124.2022.00384","DOIUrl":"https://doi.org/10.1109/csci58124.2022.00384","url":null,"abstract":"<p><p>This report presents the Gateway Scholars Program, an NSF-S-STEM supported program that recruited academically talented undergraduate students with demonstrated financial need. The objectives of our program included establishing a mentored cohort program, implementing enhanced risk-based advising, integrating evidence-based instructional practices in the curriculum, engaging students in co-curricular experiences, and generating new knowledge about the effect of activities on retention, student success, and degree attainment. Knowledge about broadening participation and effectiveness of evidence-based practices in STEM curricular and co-curricular activities and systems developed through this program have the potential to impact all STEM departments.</p>","PeriodicalId":93614,"journal":{"name":"Proceedings. International Conference on Computational Science and Computational Intelligence","volume":"2022 ","pages":"2129-2132"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10586752/pdf/nihms-1908257.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Machine Learning to Sleep Stage Classification.","authors":"Andrew Smith,&nbsp;Hardik Anand,&nbsp;Snezana Milosavljevic,&nbsp;Katherine M Rentschler,&nbsp;Ana Pocivavsek,&nbsp;Homayoun Valafar","doi":"10.1109/csci54926.2021.00130","DOIUrl":"https://doi.org/10.1109/csci54926.2021.00130","url":null,"abstract":"<p><p>Sleep studies are imperative to recapitulate phenotypes associated with sleep loss and uncover mechanisms contributing to psychopathology. Most often, investigators manually classify the polysomnography into vigilance states, which is time-consuming, requires extensive training, and is prone to inter-scorer variability. While many works have successfully developed automated vigilance state classifiers based on multiple EEG channels, we aim to produce an automated and openaccess classifier that can reliably predict vigilance state based on a single cortical electroencephalogram (EEG) from rodents to minimize the disadvantages that accompany tethering small animals via wires to computer programs. Approximately 427 hours of continuously monitored EEG, electromyogram (EMG), and activity were labeled by a domain expert out of 571 hours of total data. Here we evaluate the performance of various machine learning techniques on classifying 10-second epochs into one of three discrete classes: paradoxical, slow-wave, or wake. Our investigations include Decision Trees, Random Forests, Naive Bayes Classifiers, Logistic Regression Classifiers, and Artificial Neural Networks. These methodologies have achieved accuracies ranging from approximately 74% to approximately 96%. Most notably, the Random Forest and the ANN achieved remarkable accuracies of 95.78% and 93.31%, respectively. Here we have shown the potential of various machine learning classifiers to automatically, accurately, and reliably classify vigilance states based on a single EEG reading and a single EMG reading.</p>","PeriodicalId":93614,"journal":{"name":"Proceedings. International Conference on Computational Science and Computational Intelligence","volume":"2021 ","pages":"349-354"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597665/pdf/nihms-1842867.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40672469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}