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Genome Biology and Evolution Jun 2024Runs of homozygosity (ROHs) are indicative of elevated homozygosity and inbreeding due to mating of closely related individuals. Self-fertilization can be a major source...
Runs of homozygosity (ROHs) are indicative of elevated homozygosity and inbreeding due to mating of closely related individuals. Self-fertilization can be a major source of inbreeding which elevates genome-wide homozygosity and thus should also create long ROHs. While ROHs are frequently used to understand inbreeding in the context of conservation and selective breeding, as well as for consanguinity of populations and their demographic history, it remains unclear how ROH characteristics are altered by selfing and if this confounds expected signatures of inbreeding due to demographic change. Using simulations, we study the impact of the mode of reproduction and demographic history on ROHs. We apply random forests to identify unique characteristics of ROHs, indicative of different sources of inbreeding. We pinpoint distinct features of ROHs that can be used to better characterize the type of inbreeding the population was subjected to and to predict outcrossing rates and complex demographic histories. Using additional simulations and four empirical datasets, two from highly selfing species and two from mixed-maters, we predict the selfing rate and validate our estimations. We find that self-fertilization rates are successfully identified even with complex demography. Population genetic summary statistics improve algorithm accuracy particularly in the presence of additional inbreeding, e.g., from population bottlenecks. Our findings highlight the importance of ROHs in disentangling confounding factors related to various sources of inbreeding and demonstrate situations where such sources cannot be differentiated. Additionally, our random forest models provide a novel tool to the community for inferring selfing rates using genomic data.
PubMed: 38935434
DOI: 10.1093/gbe/evae139 -
JMIR Mental Health Jun 2024Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of... (Review)
Review
BACKGROUND
Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.
OBJECTIVE
This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS
A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS
Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS
Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.
Topics: Humans; Machine Learning; Suicide Prevention; Mental Health; Social Media; Data Analysis
PubMed: 38935419
DOI: 10.2196/55747 -
Kidney Research and Clinical Practice Jun 2024Acute kidney injury (AKI) is a significant challenge in healthcare, imposing a significant social burden. While there are considerable researches dedicated to AKI and...
BACKGROUND
Acute kidney injury (AKI) is a significant challenge in healthcare, imposing a significant social burden. While there are considerable researches dedicated to AKI and the recovery of AKI patients, a crucial factor in their prognosis, is often overlooked. Thus, our study aims to address this issue through the development of a machine learning-based approach to predict restoration of kidney function in patients with AKI.
METHODS
Our study encompassed data from 350,345 cases, derived from two hospitals. AKI was classified in accordance with the Kidney Disease: Improving Global Outcomes. Criteria for recovery were established as either a 33% decrease in serum creatinine levels at AKI onset or reduction to values lower than the baseline, which was initially employed for the diagnosis of AKI. We employed various machine learning models, selecting 43 pertinent features for analysis.
RESULTS
Our analysis contained 7,041 and 2,929 patients' data from internal cohort and external cohort respectively. The Categorical Boosting model demonstrated significant predictive accuracy, as evidenced by an internal area under the receiver operating characteristic curve (AUROC) of 0.7860, and an external AUROC score of 0.7316, thereby confirming its robustness in predictive performance. SHapley Additive exPlanations values were employed to explain key factors impacting recovery of renal function in AKI patients, highlighting factors such as elevated urine specific gravity, body temperature, and phosphorus levels.
CONCLUSION
This study presented a novel machine learning framework for predicting renal function recovery in patients with AKI, offering a deeper understanding of the key variables affecting recovery. The clinical applicability of the model was assessed across distinct hospital settings, which revealed variations in its efficacy. Although the model exhibited favorable outcomes, the necessity for further enhancements and the incorporation of more diverse datasets is imperative for its application in real-world scenarios.
PubMed: 38934029
DOI: 10.23876/j.krcp.23.330 -
Kidney Research and Clinical Practice Jun 2024Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate...
Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.
PubMed: 38934028
DOI: 10.23876/j.krcp.23.298 -
Heliyon Jun 2024As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention...
As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.
PubMed: 38933933
DOI: 10.1016/j.heliyon.2024.e32090 -
Frontiers in Genetics 2024Tumor tissue origin detection is of great importance in determining the appropriate course of treatment for cancer patients. Classifiers based on gene expression and DNA...
BACKGROUND
Tumor tissue origin detection is of great importance in determining the appropriate course of treatment for cancer patients. Classifiers based on gene expression and DNA methylation profiles have been confirmed to be feasible and reliable to predict the tumor primary. However, few works have been performed to compare the performance of these classifiers based on different profiles.
METHODS
Using gene expression and DNA methylation profiles from The Cancer Genome Atlas (TCGA) project, eight machine learning methods were employed for the tumor tissue origin detection. We then evaluated the predictive performance using DNA methylation, mRNA, microRNA (miRNA) and long non-coding RNA (lncRNA) expression profiles in a comparative manner. A statistical method was introduced to select the most informative CpG sites.
RESULTS
We found that LASSO is the most predictive models based on various profiles. Further analyses indicated that the results derived from DNA methylation (overall accuracy: 97.77%) are better than those derived from mRNA expression (overall accuracy: 88.01%), microRNA expression (overall accuracy: 91.03%) and lncRNA expression (overall accuracy: 95.7%). It has been suggested that we can achieve an overall accuracy >90% using only 1,000 methylated CpG sites for prediction.
CONCLUSION
In this work, we comprehensively evaluated the performance of classifiers based on different profiles for the tumor origin detection. Our findings demonstrated the effectiveness of DNA methylation as biomarker for tracing tumor tissue origin using LASSO and neural network.
PubMed: 38933920
DOI: 10.3389/fgene.2024.1383852 -
Frontiers in Endocrinology 2024The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective...
OBJECTIVES
The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI).
METHODS
Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule's largest diameter.
RESULTS
The SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852-0.906) and 0.713 (95% confidence interval: 0.613-0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I treatment.
CONCLUSION
This study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation.
Topics: Humans; Thyroid Neoplasms; Iodine Radioisotopes; Lung Neoplasms; Female; Male; Middle Aged; Adult; Aged; Deep Learning; Retrospective Studies; Tomography, Emission-Computed, Single-Photon; Cohort Studies
PubMed: 38933823
DOI: 10.3389/fendo.2024.1429115 -
Frontiers in Neuroscience 2024Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory...
PURPOSE
Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs).
METHODS
We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model.
RESULTS
SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity.
CONCLUSION
Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.
PubMed: 38933814
DOI: 10.3389/fnins.2024.1402039 -
Frontiers in Neuroscience 2024One-shot learning, the ability to learn a new concept from a single instance, is a distinctive brain function that has garnered substantial interest in machine learning....
One-shot learning, the ability to learn a new concept from a single instance, is a distinctive brain function that has garnered substantial interest in machine learning. While modeling physiological mechanisms poses challenges, advancements in artificial neural networks have led to performances in specific tasks that rival human capabilities. Proposing one-shot learning methods with these advancements, especially those involving simple mechanisms, not only enhance technological development but also contribute to neuroscience by proposing functionally valid hypotheses. Among the simplest methods for one-shot class addition with deep learning image classifiers is "weight imprinting," which uses neural activity from a new class image data as the corresponding new synaptic weights. Despite its simplicity, its relevance to neuroscience is ambiguous, and it often interferes with original image classification, which is a significant drawback in practical applications. This study introduces a novel interpretation where a part of the weight imprinting process aligns with the Hebbian rule. We show that a single Hebbian-like process enables pre-trained deep learning image classifiers to perform one-shot class addition without any modification to the original classifier's backbone. Using non-parametric normalization to mimic brain's fast Hebbian plasticity significantly reduces the interference observed in previous methods. Our method is one of the simplest and most practical for one-shot class addition tasks, and its reliance on a single fast Hebbian-like process contributes valuable insights to neuroscience hypotheses.
PubMed: 38933813
DOI: 10.3389/fnins.2024.1344114 -
Frontiers in Computational Neuroscience 2024Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and...
Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification.
PubMed: 38933392
DOI: 10.3389/fncom.2024.1414462