Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties

Herrera-Ibatá, Diana María; Orbegozo-Medina, Ricardo A.; Romero-Duran, Francisco; González-Dı́az, Humberto

doi:10.1016/j.biosystems.2015.04.007

Cited by 19 publications

(10 citation statements)

References 37 publications

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“…Although race had a significant impact, health care and social factors known to be associated with racial disparities had greater or similarly important effects across all ages and stages. ( Herrera-Ibatá et al, 2015 ) 2015 To develop a computational algorithm for network epidemiology to map structure-activity data of HAART-drugs cocktails over complex networks of AIDS epidemiology and socioeconomic factors. Prediction Survey United States 131252 The probability of AIDS could be halted in a county with a HAART cocktail Linear neural network The machine-learning algorithms could be useful as an initial form of screening for the prediction of effective drugs in preclinical assays for the treatment of HIV in different populations of U.S. counties with a given AIDS epidemiological prevalence.…”

Section: Methodsmentioning

confidence: 99%

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Kino

Hsu

Shiba

et al. 2021

SSM - Population Health

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Background Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

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Section: Methodsmentioning

confidence: 99%

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Kino

Hsu

Shiba

et al. 2021

SSM - Population Health

View full text Add to dashboard Cite

show abstract

“…This study aimed at using four mature data mining algorithms (random forests, support vector machine, k-nearest neighbors, and decision tree) to build identification models for AIDS patients based on the sentinel monitoring data of HIV high-risk populations (MSM, FSWs, and IDUs) in Urumqi and compared the prediction power of the different models. However, considering 12 Complexity that the major defect in the model build process is class imbalances, the SMOTE method has been used to simulate the data balance and overcome the problem of overfitting according to the previous research [49]. For all datasets, the final experimental results showed that RF algorithm obtains the best results; the diagnostic accuracy for RF on MSM dataset are 94.4821%, 97.5136% on FSW dataset, and 94.6375% on IDU dataset.…”

Section: Discussionmentioning

confidence: 99%

Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High‐Risk Group in Urumqi from 2009 to 2015

Tang

Zhang

et al. 2018

Complexity

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Objective. Urumqi is one of the key areas of HIV/AIDS infection in Xinjiang and in China. The AIDS epidemic is spreading from high-risk groups to the general population, and the situation is still very serious. The goal of this study was to use four data mining algorithms to establish the identification model of HIV infection and compare their predictive performance. Method. The data from the sentinel monitoring data of the three groups of high-risk groups (injecting drug users (IDU), men who have sex with men (MSM), and female sex workers (FSW)) in Urumqi from 2009 to 2015 included demographic characteristics, sex behavior, and serological detection results. Then we used age, marital status, education level, and other variables as input variables and whether to infect HIV as output variables to establish four prediction models for the three datasets. We also used confusion matrix, accuracy, sensitivity, specificity, precision, recall, and the area under the receiver operating characteristic (ROC) curve (AUC) to evaluate classification performance and analyzed the importance of predictive variables. Results. The final experimental results show that random forests algorithm obtains the best results, the diagnostic accuracy for random forests on MSM dataset is 94.4821%, 97.5136% on FSW dataset, and 94.6375% on IDU dataset. The k-nearest neighbors algorithm came out second, with 91.5258% diagnostic accuracy on MSM dataset, 96.3083% diagnostic accuracy on FSW dataset, and 90.8287% diagnostic accuracy on IDU dataset, followed by support vector machine (94.0182%, 98.0369%, and 91.3571%). The decision tree algorithm was the poorest among the four algorithms, with 79.1761% diagnostic accuracy on MSM dataset, 87.0283% diagnostic accuracy on FSW dataset, and 74.3879% accuracy on IDU. Conclusions. Data mining technology, as a new method of assisting disease screening and diagnosis, can help medical personnel to screen and diagnose AIDS rapidly from a large number of information.

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“…The reason to directly use the MLP networks was based on the fact that, in addition to their versatility to solve complex tasks, MLP networks have been widely used in QSAR modeling [ 64 , 65 , 66 ]. On the other hand, MLP networks have proven to be very effective in modeling extremely complex problems in the context of the PTML philosophy [ 29 , 36 , 67 , 68 , 69 , 70 ]. In the process of choosing the best MLP networks (mtc-QSAR-MLP model), we considered different global statistical indices such as sensitivity ( Sn (%)—the percentage of cases/molecules correctly classified as active), specificity ( Sp (%)—the percentage of cases/molecules correctly classified as inactive), accuracy ( Acc (%)—the percentage of correctly classified cases/molecules considering both active and inactive), and the Matthews’ correlation coefficient ( MCC ) [ 71 ].…”

Section: Methodsmentioning

confidence: 99%

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

2021

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Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structural point of view. When using the mtc-QSAR-MLP model as a virtual screening tool, we could identify several agency-regulated chemicals as dual inhibitors of caspase-1 and TNF-alpha, and the experimental information later retrieved from the scientific literature converged with our computational results. This study supports the capabilities of our mtc-QSAR-MLP model in anti-inflammatory therapy with direct applications to current health issues such as the COVID-19 pandemic.

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Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties

Cited by 19 publications

References 37 publications

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High‐Risk Group in Urumqi from 2009 to 2015

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

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