Assessing Concordance between RNA-Seq and NanoString Technologies in Ebola-Infected Nonhuman Primates Using Machine Learning

This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). We performed a detailed comparison of both platforms, demonstrating a strong correlation between them, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88, with a mean of 0.83 and a median of 0.85. Bland-Altman analysis further confirmed high consistency, with most measurements falling within 95% confidence limits. A machine learning approach, using the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, identified OAS1 as a key marker for distinguishing RT-qPCR positive from negative samples. Remarkably, when applied to RNA-Seq data, OAS1 also achieved 100% accuracy in differentiating infected from uninfected samples using logistic regression, demonstrating its robustness across platforms. Further differential expression analysis identified 12 common genes including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL which demonstrated the highest levels of statistical significance and biological relevance across both platforms. Gene Ontology (GO) analysis confirmed that these genes are directly involved in key immune and viral infection pathways, reinforcing their importance in EBOV infection. In addition, RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which play key roles in immune regulation and antiviral defense. This finding highlights the broader detection capabilities of RNA-Seq and underscores the complementary strengths of both platforms in providing a comprehensive and accurate assessment of gene expression changes during Ebola virus infection.

Machine Learning-Based Analysis of Ebola Virus' Impact on Gene Expression in Nonhuman Primates

This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a machine learning-based approach, for analyzing gene expression data obtained from nonhuman primates (NHPs) infected with Ebola virus (EBOV). We utilize a comprehensive dataset of NanoString gene expression profiles from Ebola-infected NHPs, deploying the SMAS system for nuanced host-pathogen interaction analysis. SMAS effectively combines gene selection based on statistical significance and expression changes, employing linear classifiers such as logistic regression to accurately differentiate between RT-qPCR positive and negative NHP samples. A key finding of our research is the identification of IFI6 and IFI27 as critical biomarkers, demonstrating exceptional predictive performance with 100% accuracy and Area Under the Curve (AUC) metrics in classifying various stages of Ebola infection. Alongside IFI6 and IFI27, genes, including MX1, OAS1, and ISG15, were significantly upregulated, highlighting their essential roles in the immune response to EBOV. Our results underscore the efficacy of the SMAS method in revealing complex genetic interactions and response mechanisms during EBOV infection. This research provides valuable insights into EBOV pathogenesis and aids in developing more precise diagnostic tools and therapeutic strategies to address EBOV infection in particular and viral infection in general.

Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries

This study explored the potential of gait analysis as a tool for assessing post-injury complications, e.g., infection, malunion, or hardware irritation, in patients with lower extremity fractures. The research focused on the proficiency of supervised machine learning models predicting complications using consecutive gait datasets. We identified patients with lower extremity fractures at an academic center. Patients underwent gait analysis with a chest-mounted IMU device. Using software, raw gait data was preprocessed, emphasizing 12 essential gait variables. Machine learning models including XGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained, tested, and evaluated. Attention was given to class imbalance, addressed using SMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait variables, independent of the time difference between gait analyses. XGBoost was the optimal model both before and after applying SMOTE. Prior to SMOTE, the model achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test accuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed importance to the duration between injury and gait analysis. Data patterns showed early physiological compensations, followed by stabilization phases, emphasizing prompt gait analysis. This study underscores the potential of machine learning, particularly XGBoost, in gait analysis for orthopedic care. Predicting post-injury complications, early gait assessment becomes vital, revealing intervention points. The findings support a shift in orthopedics towards a data-informed approach, enhancing patient outcomes.

Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images

Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance within the small tumor WSIs. This occurs when the tumor comprises only a few isolated cells. For early detection, it is of utmost importance that MIL algorithms can identify small tumors, even when they are less than 1% of the size of the WSI. Existing studies have attempted to address this issue using attention-based architectures and instance selection-based methodologies, but have not yielded significant improvements. This paper proposes cross-attention-based salient instance inference MIL (CASiiMIL), which involves a novel saliency-informed attention mechanism, to identify breast cancer lymph node micro-metastasis on WSIs without the need for any annotations. Apart from this new attention mechanism, we introduce a negative representation learning algorithm to facilitate the learning of saliency-informed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-of-the-art MIL methods on two popular tumor metastasis detection datasets, and demonstrates great cross-center generalizability. In addition, it exhibits excellent accuracy in classifying WSIs with small tumor lesions. Moreover, we show that the proposed model has excellent interpretability attributed to the saliency-informed attention weights. We strongly believe that the proposed method will pave the way for training algorithms for early tumor detection on large datasets where acquiring fine-grained annotations is practically impossible.

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to gigapixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. Our method initially learns representations of normal WSIs, and it then compares the normal WSIs representations with all the input patches to infer the salient instances of the input WSI. Finally, it employs attention-based MIL to perform the slide-level classification based on the selected patches of the WSI. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times. It is worth mentioning that it performs equally well for large tumor lesions. As a result, SiiMIL achieves a significant improvement in performance over the state-of-the-art MIL methods.

Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), was first found in Wuhan, China late in the December of 2019. Not long after that the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States. One of these changes was the shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and online learning impact college students' emotional wellbeing. To do this we use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions and students' associations. Our results indicate that learning modality (face-to-face, online synchronous, online asynchronous, etc.) is the main predictor of students' emotional wellbeing, followed by financial security. Factors such as satisfaction with their university's and government's handling of the pandemic are also important predictors.

A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility

For the past couple years, the Coronavirus, commonly known as COVID-19, has significantly affected the daily lives of all citizens residing in the United States by imposing several, fatal health risks that cannot go unnoticed. In response to the growing fear and danger COVID-19 inflicts upon societies in the USA, several vaccines and boosters have been created as a permanent remedy for individuals to take advantage of. In this paper, we investigate the relationship between the COVID-19 vaccines and boosters and the total case count for the Coronavirus across multiple states in the USA. Additionally, this paper discusses the relationship between several, selected underlying health conditions with COVID-19. To discuss these relationships effectively, this paper will utilize statistical tests and machine learning methods for analysis and discussion purposes. Furthermore, this paper reflects upon conclusions made about the relationship between educational attainment, race, and COVID-19 and the possible connections that can be established with underlying health conditions, vaccination rates, and COVID-19 total case and death counts.

Hidden Effects of COVID-19 on Healthcare Workers: A Machine Learning Analysis

In this paper, we analyze some effects of the COVID-19 pandemic on healthcare workers. We specifically focus on alcohol consumption habit changes among healthcare workers using a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research. We use supervised and unsupervised machine learning methods and models such as Decision Trees, Logistic Regression, Naive Bayes classifier, k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, Random Forests, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, Chi-Squared Test and mutual information method to find out relationships between COVID-19 related negative effects and alcohol use changes in healthcare workers. Our findings suggest that some effects of the COVID-19 pandemic such as school closure, work schedule change and COVID-related news exposure may lead to an increase in alcohol use.

A Machine Learning Analysis of COVID-19 Mental Health Data

In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting disease COVID-19 were first identified in Wuhan China. The disease slipped through containment measures, with the first known case in the United States being identified on January 20th, 2020. In this paper, we utilize survey data from the Inter-university Consortium for Political and Social Research and apply several statistical and machine learning models and techniques such as Decision Trees, Multinomial Logistic Regression, Naive Bayes, k-Nearest Neighbors, Support Vector Machines, Neural Networks, Random Forests, Gradient Tree Boosting, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, and Chi-Squared Test to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States. Through the interpretation of the many models applied to the mental health survey data, we have concluded that the most important factor in predicting the mental health decline of a frontline worker is the healthcare role the individual is in (Nurse, Emergency Room Staff, Surgeon, etc.), followed by the amount of sleep the individual has had in the last week, the amount of COVID-19 related news an individual has consumed on average in a day, the age of the worker, and the usage of alcohol and cannabis.