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This study proposes a new test for genome-wide association studies that incorporates Hardy-Weinberg equilibrium into SNP analysis, demonstrating improved power and interpretability over traditional methods, as evidenced by simulations and an alopecia study dataset.

In this study, applying case-only trees and random forests to a prostate cancer prevention trial revealed genotypes that may influence the efficacy of finasteride for prostate cancer prevention.

This study introduced a statistical method, PLACO, which revealed novel genetic regions associated with both Type 2 Diabetes and Prostate Cancer from GWAS data.

In this study, a deep neural network model called regX was developed to prioritize driver regulators for cell state transitions by incorporating gene-level regulation and interactions, showing potential therapeutic targets in type 2 diabetes and hair follicle development when applied to single-cell multi-omics data.

This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.

This study found that faster algorithms for inferring ancestry in genomic data can better capture historical and functional insights into genome variation than traditional methods in large datasets like the UK Biobank.

This study constructed a panel of pharmacokinetic and pharmacodynamic genes, revealing that current SNP chips insufficiently capture many drug-response gene variants, highlighting the need for complementary genetic approaches.

This study developed a framework to handle missing participant data in systematic reviews for continuous outcomes, finding varied impacts on confidence in effect estimates across reviewed cases.

This article reviews challenges in interpreting observational COVID-19 data due to biases from non-random sampling, discussing strategies to address these biases but reporting no new results.

This study found that non-blinded assessors in randomized clinical trials exaggerated the effects of experimental interventions by about 29% compared to blinded assessors, indicating a significant potential for observer bias in treatment evaluations.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

This review discusses the challenges of interpreting observational studies on COVID-19 due to biases from non-representative samples, such as collider bias, and suggests better sampling methods to mitigate these issues.

Results are not reported in this abstract, but it outlines the objective to evaluate the efficacy of microneedling, alone and in combination with other treatments, for pattern hair loss through a systematic review and meta-analysis.

This meta-analysis utilized genome-wide association data to explore the genetic traits related to perceived youthfulness across different sex groups in the UK Biobank, incorporating factors like facial aging and lifestyle habits, but the abstract does not report specific results.

This study utilized U-Net's image-processing capabilities to achieve 92% accuracy in segmenting individual hair strands, enhancing early detection and reliable identification of hair fall areas, which assists in addressing challenges of subtle hair thinning that are difficult to see otherwise.

This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.

This study found that integrating the Theory of Planned Behaviour with the Stimulus-Organism-Response framework improved the prediction of intentions to buy embarrassing products, with subjective norms being significant across drugstore, internet, and multi-channel environments.

This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

This study found that nicotine may have a therapeutic role in mitigating the exacerbation of infertility conditions connected with alpha-synuclein-related Parkinson’s disease through molecular interactions identified via pathway analysis.

This study conducted a genome-wide association meta-analysis using UK Biobank data to explore genetic correlations with perceived youthfulness, revealing traits linked to this perception among both male and female participants.

This thesis presents research conducted in partial fulfillment of a Master's degree in Software Systems Engineering but does not report new empirical findings.

In this study, deep learning models accurately predicted gene expression in whole slide images of colorectal cancer, with convolutional neural networks outperforming transformer and graph-based approaches in spatial RNA pattern prediction.

This study observed that using the LMNN algorithm improved diagnostic accuracy in identifying biomarker correlations associated with hair loss, suggesting potential for advanced automated diagnostics.

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.