Machine learning

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data...

The term "charisma" is recognized as sociology's most successful export to common speech. While sociologists habitually dismiss popular uses of the word, we address its vernacularity head on as a worthy object of study and as a potential resource for conceptual development. Using machine learning, we locate "charisma" within the wider discursive field out of which it arises (and continues to arise) across four corpora; namely: Weber's major writings; social scientific research (123,531 JSTOR articles); and social media ("X") posts containing of "charisma" (n=77,161) and its 2023 variant, "rizz" (n=85,869)...

PURPOSE: We developed a predictive model to assess the risk of major bleeding (MB) within 6 months of primary venous thromboembolism (VTE) in cancer patients receiving anticoagulant treatment. We also sought to describe the prevalence and incidence of VTE in cancer patients, and to describe clinical characteristics at baseline and bleeding events during follow-up in patients receiving anticoagulants. METHODS: This observational, retrospective, and multicenter study used natural language processing and machine learning (ML), to analyze unstructured clinical data from electronic health records from nine Spanish hospitals between 2014 and 2018...

Lower-grade gliomas (LGGs), despite their generally indolent clinical course, are characterized by invasive growth patterns and genetic heterogeneity, which can lead to malignant transformation, underscoring the need for improved prognostic markers and therapeutic strategies. This study utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq to identify a novel cell type, referred to as "Prol," characterized by increased proliferation and linked to a poor prognosis in patients with LGG, particularly under the context of immunotherapy interventions...

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions...

Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent feelings of sadness and hopelessness, affecting millions globally. The precise molecular mechanisms underlying MDD remain elusive, necessitating comprehensive investigations. Our study integrates transcriptomic analysis, functional assays, and computational modeling to explore the molecular landscape of MDD, focusing on the DLPFC. We identify key genomic alterations and co-expression modules associated with MDD, highlighting potential therapeutic targets...

The combination of wearable sensors with machine learning enables intelligent perception in human-machine interaction and healthcare, but achieving high sensitivity and a wide working range in flexible strain sensors for signal acquisition and accurate recognition remains challenging. Herein, we introduced carboxymethyl cellulose (CMC) into a carbon nanotubes (CNTs)/MXene hybrid network, forming tight anchoring among the conductive materials and, thus, bringing enhanced interaction. The silicone-rubber-encapsulated CMC-anchored CNTs/MXene (CCM) strain sensor exhibits an excellent sensitivity (maximum gauge factor up to 71 294), wide working range (200%), ultralow detection limit (0...

We have reviewed the article "Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights" by Chmiel et al. with keen interest. The authors have made significant strides in leveraging machine learning to predict urinary stone composition, a crucial factor in the management and treatment of urolithiasis. While the study presents innovative methodologies and insightful findings, there are several areas where the approach and interpretation could be refined to enhance the robustness and applicability of the results...

This research has introduced an innovative approach that proficiently forecasts the alterations in ultraviolet-visible spectroscopy (UV-Vis) of polymer solutions during the aging effect. This method combines readily accessible feature descriptors with classical machine learning (ML) algorithms. Traditional spectral measurements, while precise in analyzing physical properties, are limited by their cost and efficiency. Therefore, this paper introduces a method that utilizes wavelength and the blue ( B ), green ( G ), and red ( R ) color values of the solutions as input features...

Cancer's persistent growth often relies on its ability to maintain telomere length and tolerate the accumulation of DNA damage. This study explores a computational approach to identify compounds that can simultaneously target both G-quadruplex (G4) structures and poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged attack on cancer cells. We employed a hybrid virtual screening (VS) protocol, combining the power of machine learning with traditional structure-based methods. PyRMD, our AI-powered tool, was first used to analyze vast chemical libraries and to identify potential PARP1 inhibitors based on known bioactivity data...

The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features...

Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD-causing variants did not consider the effect of mutations from the perspective of a protein three-dimensional environment...

OBJECTIVE: Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients. DESIGN: An observational cohort study. SETTING: Queen Elizabeth University Hospital. PARTICIPANTS: Patients who were referred via the USOC pathway between January 2019 and May 2021. MAIN OUTCOME MEASURES: Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data...

BACKGROUND: Intracortical inhibitory/faciliatory measures are affected after stroke; however, the evidence is conflicting. OBJECTIVE: This meta-analysis aimed to investigate the changes in motor threshold (MT), motor evoked potential (MEP), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF), and identify sources of study variability using a machine learning approach. METHODS: We identified studies that objectively evaluated corticospinal excitability and intracortical inhibition/facilitation after stroke using transcranial magnetic stimulation...

BACKGROUND: Heart failure (HF) is the leading cause of morbidity and mortality worldwide. Stemness refers to the self-renewal and differentiation ability of cells. However, little is known about the heart's stemness properties. Thus, the current study aims to identify putative stemness-related biomarkers to construct a viable prediction model of HF and characterize the immune infiltration features of HF. METHODS: HF datasets from the Gene Expression Omnibus (GEO) database were adopted as the training and validation cohorts while stemness-related genes were obtained from GeneCards and previously published papers...

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions...

This study presents a comparative analysis of various Machine Learning (ML) techniques for predicting water consumption using a comprehensive dataset from Kocaeli Province, Turkey. Accurate prediction of water consumption is crucial for effective water resource management and planning, especially considering the significant impact of the COVID-19 pandemic on water usage patterns. A total of four ML models, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (GBM), were evaluated...

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks...

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition...

Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively higher fish density and water turbidity in intensive RAS culture produce major challenges in acquiring high-quality underwater image data. Additionally, the manual image annotation involved in model training can be subjective, time-consuming, and labor-intensive...