Analysis Of Large Datasets Research Articles

Using topological data analysis and machine learning to predict customer churn

Customer churn is a common problem faced by many industries, including telecommunication industries. This has resulted in the development of advanced techniques for the prediction and prevention of customer churn. The availability of stored customer data in the form of big data, together with the use of advanced and tuned machine learning (ML) algorithms, have paved the way for the realisation and extraction of useful features associated with customer behaviour and consequently the prediction of customer churn. An effective way to further improve churn prediction capability of different ML algorithms is through the employment of topological data analysis (TDA). TDA is a framework that applies topological methods to uncover the underlying hidden structural features in complex, high-dimensional data. Here, a TDA summary of 0- and 1-dimensional holes of the data, called barcode statistics, was extracted and used as an additional feature to the preprocessed customer data. To address issues such as the effective preprocessing and analysis of large customer datasets and the effective tuning of ML hyperparameters, we implement an advanced data preprocessing technique that consists of different stages such as handling of missing data, feature engineering, encoding of categorical features using the hashing encoding method, and feature selection. Without including barcode statistics in the model, the XGBoost algorithm with tuned hyperparameters achieved the best results, with accuracy of 92.71%, precision of 85.95%, recall of 92.71%, and F-measure of 89.20%. Including barcode statistics as an additional feature, the XGBoost algorithm with tuned hyperparameters achieved the best and much improved results, with accuracy of 98.50%, precision of 98.50%, recall of 98.50%, and F-measure of 98.50%. The use of TDA barcode statistics significantly improved the churn prediction capability of the ML algorithms. In addition, hyperparameter tuning is not needed when an effective data preprocessing technique is used, or when barcode statistics is used. The best accuracy of 98.5% from this work was in line with the best accuracy of 98.7% from a related work, but interestingly, the best precision of 98.5% from this work was superior to the 94.3% precision from the same related work with higher accuracy.

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches

Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the Mtb genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 Mtb WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3–96.5) and second-line (AUC range 84.1–95.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide + 25%; ethionamide + 10%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., ndh 293dupG, Rv3861 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (n = 12,066), and identify mutations associated with highly resistant phenotypes (e.g., inhA − 779G > T and 62T > C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting Mtb binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.

Development of a method for estimating asari clam distribution by combining three-dimensional acoustic coring system and deep neural network

Developing non-contact, non-destructive monitoring methods for marine life is crucial for sustainable resource management. Recent monitoring technologies and machine learning analysis advancements have enhanced underwater image and acoustic data acquisition. Systems to obtain 3D acoustic data from beneath the seafloor are being developed; however, manual analysis of large 3D datasets is challenging. Therefore, an automatic method for analyzing benthic resource distribution is needed. This study developed a system to estimate benthic resource distribution non-destructively by combining high-precision habitat data acquisition using high-frequency ultrasonic waves and prediction models based on a 3D convolutional neural network (3D-CNN). The system estimated the distribution of asari clams (Ruditapes philippinarum) in Lake Hamana, Japan. Clam presence and count were successfully estimated in a voxel with an ROC-AUC of 0.9 and a macro-average ROC-AUC of 0.8, respectively. This system visualized clam distribution and estimated numbers, demonstrating its effectiveness for quantifying marine resources beneath the seafloor.

Numerical Methods and Computation in Financial Mathematics: Solving PDEs, Monte Carlo Simulations, and Machine Learning Applications

Abstract. This paper delves into three fundamental numerical methods and computational techniques in financial mathematics: Finite Difference Methods (FDM), Monte Carlo Simulations (MCS), and Machine Learning (ML) applications. Finite Difference Methods are widely utilized for solving partial differential equations (PDEs) in option pricing, with various schemes offering different stability and convergence properties. Monte Carlo Simulations provide a powerful approach for pricing complex derivatives and risk management, addressing the challenges of high-dimensionality and computational complexity. Machine Learning has revolutionized predictive modeling in finance, enabling sophisticated analysis of large datasets to uncover hidden patterns and enhance trading strategies. Through a detailed examination of these methods, including specific examples and data, this paper highlights their theoretical foundations, practical implementations, and the advancements they bring to computational finance. By bridging theoretical approaches with practical applications, we aim to offer insights into the future directions and challenges in financial mathematics.

C→U transition biases in SARS-CoV-2: still rampant 4 years from the start of the COVID-19 pandemic.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the pandemic and post-pandemic periods has shown a remarkable capacity to adapt and evade human immune responses and increase its human-to-human transmissibility. The genome of SARS-CoV-2 is also increasingly scarred by the effects of multiple C→U mutations from host genome editing as a cellular defense mechanism akin to restriction factors for retroviruses. Through the analysis of large data sets of SARS-CoV-2 isolate sequences collected throughout the pandemic period and beyond, we show that C→U transitions have driven a base compositional change over time amounting to a net loss of C bases and accumulation of U's at a rate of approximately 0.2%-0.25%/decade. Most C→U substitutions occurred in the absence of the preferred upstream-base context or targeting of unpaired RNA bases previously associated with the host RNA editing protein, APOBEC 3A. The analyses provide a series of testable hypotheses that can be experimentally investigated in the future.

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data.

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions by leveraging the power of multivariate analyses of large datasets. While deep learning (DL) offers another promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address these challenges and describe an interpretable predictive pipeline for inferring Autism diagnosis using 3D DL applied to minimally processed structural MRI scans. We trained 3D DL models to predict Autism diagnosis using the openly available ABIDE I and II datasets (n = 1329, split into training, validation, and test sets). Importantly, we did not perform transformation to template space, to reduce bias and maximize sensitivity to structural alterations associated with Autism. Our models attained predictive accuracies equivalent to those of previous machine learning (ML) studies, while side-stepping the time- and resource-demanding requirement to first normalize data to a template. Our interpretation step, which identified brain regions that contributed most to accurate inference, revealed regional Autism-related alterations that were highly consistent with the literature, encompassing a left-lateralized network of regions supporting language processing. We have openly shared our code and models to enable further progress towards remaining challenges, such as the clinical heterogeneity of Autism and site effects, and to enable the extension of our method to other neuropsychiatric conditions.

The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.

Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. This review aims to present the most-used visualization techniques to estimate CVD risk. In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

Online landmark replacement for out-of-sample dimensionality reduction methods

A strategy to assist visualization and analysis of large and complex datasets is dimensionality reduction, with which one maps each data point into a low-dimensional manifold. However, various dimensionality reduction techniques are computationally infeasible for large data. Out-of-sample techniques aim to resolve this difficulty; they only apply the dimensionality reduction technique on a small portion of data, referred to as landmarks, and determine the embedding coordinates of the other points using landmarks as references. Out-of-sample techniques have been applied to online settings, or when data arrive as time series. However, existing online out-of-sample techniques use either all the previous data points as landmarks or the fixed set of landmarks and therefore are potentially not good at capturing the geometry of the entire dataset when the time series is non-stationary. To address this problem, we propose an online landmark replacement algorithm for out-of-sample techniques using geometric graphs and the minimal dominating set on them. We mathematically analyse some properties of the proposed algorithm, particularly focusing on the case of landmark multi-dimensional scaling as the out-of-sample technique, and test its performance on synthetic and empirical time-series data.

Categorizing E-cigarette-related tweets using BERT topic modeling

BackgroundSocial media platforms are critical channels for promoting e-cigarettes, particularly among youth, making analysis of their vast and diverse content essential for public health interventions. Prevalence rates of e-cigarette use are high and evidence suggests that social media are popular forums that promote e-cigarette use through direct and indirect marketing techniques. The volume and diverse nature of e-cigarette-related information on social media is challenging and may obfuscate public health prevention messaging. Traditional hand-coding methods are labor-intensive and limit scalability. In contrast, unsupervised machine learning approaches, such as topic modeling, allow for efficient analysis of large datasets, uncovering patterns and trends that manual methods cannot achieve at scale. The present study focused on ascertaining the extent to which themes and topics in tweets related to e-cigarettes can be successfully rendered into useful homogenous units using machine learning. A better understanding of current depictions and discussions around e-cigarette products and use on social media can inform public health counter messaging and policy interventions. MethodsWe used topic modeling (BERTopic) to iteratively derive vape-related tweet clusters and calculate the importance of particular words to these groupings. We conducted a qualitative content analysis to study clustered tweets. We also sought to determine the geographic locations of e-cigarette conversations using automated geoparsing methods, which translate toponyms in textual data into geographic identifiers, to attempt to infer the location of tweets. ResultsWe were able to successfully identify >100,000 tweets in broad thematic categories in English and Spanish. Our correlation and inter-topic map analysis of the machine-derived topics, which examines the relationships between topics, indicated that most of the topics were unique (correlation value < 0.5) and did not overlap with each other. We identified six topics: Flavors and Disposable Vapes, Cannabis, Vape Shops and Refillable Vapes, Vape Culture, Anti-vaping and Quitting, and Spanish Tweets and Vaping Nicotine. Further analysis of these topics using qualitative methods identified themes within each topic. For example, Category 6 (Spanish Tweets and Vaping Nicotine) included four topics focused on the health risks of vaping, personal motivations for vaping, and the regulation of vaping products. Using geoparsing, which automatically detects location information, we found that the United States had the highest number of tweets related to vaping. Discussion/conclusionResults underscore the possibility of leveraging BERTopic modeling to reduce large quantities of data to comprehensively describe and categorize myriad e-cigarette related messages to which social media users are exposed. This data reduction approach can be applied to various social media platforms to describe and categorize e-cigarette posts and thereby triangulate and validate findings. Thematic content analysis of the topics identified through this technique requires supervision and human inputs. Our approach provides a comprehensive understanding of the evolving e-cigarette discourse, informing public health counter-messaging and policy interventions. Moreover, findings support the need for regulation, such as reducing appealing flavors and suggest that social media can be used effectively to support public health messaging (i.e., quitting messages).

Harnessing AI for advancements in cardiovascular disease management and drug discovery

The integration of artificial intelligence (AI) in cardiovascular disease (CVD) management and drug discovery has revolutionized clinical decision-making, offering new avenues for precision medicine and enhanced patient care. This study explores the long-term use of cardiovascular drugs and the role of AI in improving diagnosis, risk prediction, and therapeutic outcomes. While traditional clinical trials often lack extensive follow-up data, particularly concerning the elderly, AI models provide opportunities to fill these gaps through the analysis of large datasets. The research methodology employed in this study involves comprehensive data collection from clinical and biomedical sources, AI model development, rigorous validation, and ethical considerations, ensuring reliable and actionable insights. The findings underscore the potential of AI to transform CVD management and drug discovery, while also highlighting the need for continuous learning and ethical deployment to address challenges such as data privacy and algorithmic transparency.

Development of Machine Learning and Deep Learning Prediction Models for PM2.5 in Ho Chi Minh City, Vietnam

The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM2.5 concentrations in HCMC, Vietnam, utilizing PM2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health.

Methods for calculating gliding-box lacunarity efficiently on large datasets

Lacunarity has proven to be a useful, multifaceted tool for image analysis in several different scientific fields, from geography to virology, which has lent increasing importance to the lacunarity analysis of large datasets. It can be most reliably calculated with the so-called gliding-box method, but the evaluation process can be exceedingly time-consuming and unviable as this algorithm is not designed to operate on large datasets. Here we introduce two novel methods that can calculate gliding-box lacunarity orders of magnitude faster than the original method without any loss of accuracy. We compare these methods with the original as well as with two already existing optimized methods based on runtime memory usage and complexity. The application of all five methods for both 2D and 3D datasets analysis confirms that each of the four optimized methods are orders of magnitude faster than the original one, but each has its advantages and limitations.

The impact of pulmonary tuberculosis on SARS-CoV-2 infection: a nationwide cohort study.

The interaction between COVID-19 and tuberculosis (TB) is not yet fully understood, and large-scale research on the mortality outcome of such dual infection has been limited. This study aimed to investigate the impact of PTB on mortality among patients with COVID-19 within a Korean population by conducting an extensive analysis of a nationwide large dataset. We investigated the mortality and disease severity among COVID-19 patients who had PTB in South Korea. This study analyzed 462,444 out of 566,494 COVID-19 patients identified between January 2020 and December 2021. A total of 203 COVID-19 with PTB patients and 812 matched COVID-19 without PTB were analyzed using 1:4 propensity score matching. COVID-19 patients with PTB exhibited higher in-hospital mortality (odds ratio (OR) 3.02, 95% confidence interval (CI) 1.45-6.27, p-value = 0.003) and were at increased risk of requiring conventional oxygen therapy (OR 1.57, 95% CI 1.10-2.25, p-value = 0.013) as well as high flow nasal cannula (HFNC) or noninvasive ventilation (NIV) oxygen therapy (OR 1.91, 95 CI 1.10-3.32, p-value = 0.022) compared to those without PTB. Compared to matched COVID-19 without PTB, co-infected patients showed increased mortality rates across various timeframes, including during hospitalization, and at 30 day and 90 day intervals. In-hospital mortality rates were particularly elevated among women, individuals with malignancy, and those with lower incomes. Furthermore, the increased in-hospital mortality among PTB patients persisted irrespective of the timing of TB diagnosis or vaccination status against COVID-19. We suggest that physicians be aware of the risk of mortality and severity among COVID-19 patients with PTB; coinfection with COVID-19 is a critical situation that remains to be further explored and needs more attention in countries with an intermediate to high PTB burden.

RAIChU: automating the visualisation of natural product biosynthesis

Natural products are molecules that fulfil a range of important ecological functions. Many natural products have been exploited for pharmaceutical and agricultural applications. In contrast to many other specialised metabolites, the products of modular nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) systems can often (partially) be predicted from the DNA sequence of the biosynthetic gene clusters. This is because the biosynthetic pathways of NRPS and PKS systems adhere to consistent rulesets. These universal biosynthetic rules can be leveraged to generate biosynthetic models of biosynthetic pathways. While these principles have been largely deciphered, software that leverages these rules to automatically generate visualisations of biosynthetic models has not yet been developed. To enable high-quality automated visualisations of natural product biosynthetic pathways, we developed RAIChU (Reaction Analysis through Illustrating Chemical Units), which produces depictions of biosynthetic transformations of PKS, NRPS, and hybrid PKS/NRPS systems from predicted or experimentally verified module architectures and domain substrate specificities. RAIChU also boasts a library of functions to perform and visualise reactions and pathways whose specifics (e.g., regioselectivity, stereoselectivity) are still difficult to predict, including terpenes, ribosomally synthesised and posttranslationally modified peptides and alkaloids. Additionally, RAIChU includes 34 prevalent tailoring reactions to enable the visualisation of biosynthetic pathways of fully maturated natural products. RAIChU can be integrated into Python pipelines, allowing users to upload and edit results from antiSMASH, a widely used BGC detection and annotation tool, or to build biosynthetic PKS/NRPS systems from scratch. RAIChU’s cluster drawing correctness (100%) and drawing readability (97.66%) were validated on 5000 randomly generated PKS/NRPS systems, and on the MIBiG database. The automated visualisation of these pathways accelerates the generation of biosynthetic models, facilitates the analysis of large (meta-) genomic datasets and reduces human error. RAIChU is available at https://github.com/BTheDragonMaster/RAIChU and https://pypi.org/project/raichu.Scientific contributionRAIChU is the first software package capable of automating high-quality visualisations of natural product biosynthetic pathways. By leveraging universal biosynthetic rules, RAIChU enables the depiction of complex biosynthetic transformations for PKS, NRPS, ribosomally synthesised and posttranslationally modified peptide (RiPP), terpene and alkaloid systems, enhancing predictive and analytical capabilities. This innovation not only streamlines the creation of biosynthetic models, making the analysis of large genomic datasets more efficient and accurate, but also bridges a crucial gap in predicting and visualising the complexities of natural product biosynthesis.

The use of AI in Predicting Surgical Complications

Significant obstacles exist for both patient outcomes and healthcare systems in the form of surgical complications. New approaches to anticipate these issues are being investigated by artificial intelligence (AI) researchers using machine learning, quantile regression forest, and Bayesian optimisation. This paper examines the predictive potential of AI for early postoperative problems, specifically in the context of coronary artery bypass graft surgeries. AI models offer insights into patient hazards through the analysis of large datasets, facilitating better surgical planning and early treatments. Implementing AI is fraught with difficulties, including data quality, clinician trust, and legal barriers, despite encouraging outcomes. In addition to outlining future research possibilities, this paper looks at the promise and constraints of AI in lowering surgical complications. Keywords: Artificial intelligence, surgical complications, coronary artery bypass graft, machine learning

Розвиток систем обліково-аналітичного забезпечення в умовах цифрової трансформації інформаційної індустрії

The article analyzes the current state and development of accounting and analytical systems in the context of the digital transformation of the information industry, a critical issue for enterprises in this sector. The study examines the main trends and challenges companies face due to the active implementation of digital technologies in accounting and analytical activities. It characterizes the critical aspects of how digital transformation impacts the processes of data collection, processing, and analysis within the modern information industry. The article proposes new approaches to organizing accounting and analytical systems that meet the digital economy’s demands. It explores the potential of modern information technologies to enhance the effectiveness of management decisions based on the analysis of large data sets, which contributes to increasing enterprises’ competitiveness in the digital environment. Additionally, the article highlights the necessity of integrating advanced technologies into financial control and management processes. The research identifies prospects for developing accounting and analytical systems, considering global digital trends. Recommendations are developed to improve the adaptability of information industry enterprises to the rapidly changing digital environment. Through a comprehensive analysis, the article underscores the importance of adapting to and leveraging digital transformation to enhance operational efficiency and maintain a competitive edge. The findings suggest that the ongoing digital revolution in accounting and analytics presents opportunities and challenges, requiring businesses to innovate and continuously adapt their systems and processes. This will ensure they remain resilient and competitive in an increasingly digitized global market. In the future, research in this field should focus on integrating the latest digital technologies, such as artificial intelligence and blockchain, which can significantly improve the accuracy and speed of data processing. Also, an important direction is the development of new analytical forecasting models based on big data, which will help to make management decisions more effectively. Keywords: Digital transformation, accounting systems, analytical systems, information industry, big data, competitiveness, financial control, management efficiency, modern information technologies, digital economy.

FluoAnalysis: An Open-Source MATLAB Toolbox for Analysis of Calcium Imaging Measurements of Oscillatory Astrocytic and Neuronal Networks.

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications.

Bayesian inference of sample-specific coexpression networks.

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.

Cervical cancer screening efficacy using SurePath, ThinPrep and conventional cytology: A large data set analysis from the Japan Cancer Society.

Over the past decade, liquid-based cytology has replaced conventional cytology for cervical cancer screening in many countries, including Japan. We aimed to evaluate the efficacy of liquid-based cytology using a large database and compare two major liquid-based cytology platforms, SurePath and ThinPrep, to conventional cytology. Cervical cancer screening data were collected from the Japan Cancer Society between 2015 and 2019. The efficacy of liquid-based and conventional cytology in detecting cervical intraepithelial neoplasia (CIN) was evaluated. Detection rates and positive predictive values were compared using a Poisson regression model. We collected data of 3,918,149 participants, including 2,248,202 conventional cytology, 874,807 SurePath and 795,140 ThinPrep smears. The detection rate of CIN2 or more was 1.14 times higher using SurePath than that using conventional cytology (95% confidence interval [CI], 1.09-1.20; p < 0.001). Contrastingly, the detection rate of CIN2 or more was 0.91 times lower using ThinPrep (95% CI, 0.86-0.96; p < 0.001). The detection rates of CIN3 or more did not differ significantly between SurePath and conventional cytology (detection rate ratio, 1.04; 95% CI, 0.97-1.12; p = 0.224). The positive predictive value ratios of CIN2 or more were 0.80 using SurePath (95% CI, 0.76-0.84; p < 0.001) and 0.83 using ThinPrep (95% CI, 0.79-0.87; p < 0.001) compared with conventional cytology. Liquid-based cytology, particularly SurePath, was useful for detecting CIN2 or higher in population-based cervical cancer screening. Further widespread use of liquid-based cytology methods would lead to efficient detection of cervical precancerous lesions.

JmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data

We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets.By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes.With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale healthcare datasets significantly. In summary, jmBIG empowers researchers to gain deeper insights into disease progression and treatment response, fostering evidence-based decision-making and paving the way for personalized healthcare interventions that can positively impact patient outcomes on a larger scale.