Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

Biology, Journal Year: 2023, Volume and Issue: 12(7), P. 1033 - 1033

Published: July 22, 2023

The emergence and rapid development of deep learning, specifically transformer-based architectures attention mechanisms, have had transformative implications across several domains, including bioinformatics genome data analysis. analogous nature sequences to language texts has enabled the application techniques that exhibited success in fields ranging from natural processing genomic data. This review provides a comprehensive analysis most recent advancements transformer mechanisms transcriptome focus this is on critical evaluation these techniques, discussing their advantages limitations context With swift pace learning methodologies, it becomes vital continually assess reflect current standing future direction research. Therefore, aims serve as timely resource for both seasoned researchers newcomers, offering panoramic view elucidating state-of-the-art applications field. Furthermore, paper serves highlight potential areas investigation by critically evaluating studies 2019 2023, thereby acting stepping-stone further research endeavors.

Language: Английский

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An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil

Mathematics, Journal Year: 2023, Volume and Issue: 11(14), P. 3069 - 3069

Published: July 12, 2023

This comprehensive overview focuses on the issues presented by pandemic due to COVID-19, understanding its spread and wide-ranging effects of government-imposed restrictions. The examines utility autoregressive integrated moving average (ARIMA) models, which are often overlooked in forecasting perceived limitations handling complex dynamic scenarios. Our work applies ARIMA models a case study using data from Recife, capital Pernambuco, Brazil, collected between March September 2020. research provides insights into implications adaptability predictive methods context global pandemic. findings highlight models’ strength generating accurate short-term forecasts, crucial for an immediate response slow down disease’s rapid spread. Accurate timely predictions serve as basis evidence-based public health strategies interventions, greatly assisting management. model selection involves automated process optimizing parameters autocorrelation partial plots, well various precise measures. performance chosen is confirmed when comparing forecasts with real reported after forecast period. successfully both recovered COVID-19 cases across preventive plan phases Recife. However, model’s observed extend future. By end period, error substantially increased, it failed detect stabilization deceleration cases. highlights challenges associated such under-reporting recording delays. Despite these limitations, emphasizes potential while emphasizing need further enhance long-term predictions.

Language: Английский

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Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data

Mathematics, Journal Year: 2023, Volume and Issue: 11(6), P. 1432 - 1432

Published: March 15, 2023

Long short-term memory neural networks have been proposed as a means of creating accurate models from large time series data originating various fields. These can further be utilized for prediction, control, or anomaly-detection algorithms. However, finding the optimal hyperparameters to maximize different performance criteria remains challenge both novice and experienced users. Hyperparameter optimization algorithms often resource-intensive time-consuming task, particularly when impact on network is not comprehended known. Teacher forcing denotes procedure that involves feeding ground truth output previous time-step input current during training, while testing back predicted values. This paper presents comprehensive examination long networks, with without teacher forcing, prediction performance. The study includes two variations in modes, using configurations (i.e., multi-input single-output multi-output) well-known chemical process simulation dataset. Furthermore, this demonstrates applicability modified approach state monitoring system. Over 100,000 experiments were conducted varying multiple operation revealing direct each tested hyperparameter training procedures.

Language: Английский

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Innovative applications of artificial intelligence during the COVID-19 pandemic

Infectious Medicine, Journal Year: 2024, Volume and Issue: 3(1), P. 100095 - 100095

Published: Feb. 21, 2024

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of management and response. In the present review, we discuss possibilities AI technology in addressing global posed by pandemic. First, outline multiple impacts current on public health, economy, society. Next, focus innovative applications advanced areas such as prediction, detection, control, drug discovery treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, omics data to forecast disease spread patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems support risk assessment, decision-making, social sensing, thereby improving epidemic control health policies. Furthermore, high-throughput virtual screening enables accelerate identification therapeutic candidates opportunities repurposing. Finally, future research directions combating COVID-19, emphasizing importance interdisciplinary collaboration. Though promising, barriers related model generalization, quality, infrastructure readiness, ethical risks must be addressed fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise stakeholders is imperative developing robust, responsible, human-centered solutions against emergencies.

Language: Английский

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Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 25282 - 25300

Published: Jan. 1, 2025

Language: Английский

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Generative adversarial networks based synthetic biology: A promising approach to sars-cov-2 mutations prediction

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110395 - 110395

Published: March 3, 2025

Language: Английский

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An Epidemiological Analysis for Assessing and Evaluating COVID-19 Based on Data Analytics in Latin American Countries

Biology, Journal Year: 2023, Volume and Issue: 12(6), P. 887 - 887

Published: June 20, 2023

This research provides a detailed analysis of the COVID-19 spread across 14 Latin American countries. Using time-series and epidemic models, we identify diverse outbreak patterns, which seem not to be influenced by geographical location or country size, suggesting influence other determining factors. Our study uncovers significant discrepancies between number recorded cases real epidemiological situation, emphasizing crucial need for accurate data handling continuous surveillance in managing epidemics. The absence clear correlation size confirmed cases, as well with fatalities, further underscores multifaceted influences on impact beyond population size. Despite decreased real-time reproduction indicating quarantine effectiveness most countries, note resurgence infection rates upon resumption daily activities. These insights spotlight challenge balancing public health measures economic social core findings provide novel insights, applicable guiding control strategies informing decision-making processes combatting pandemic.

Language: Английский

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An effective deep learning-based approach for splice site identification in gene expression

Science Progress, Journal Year: 2024, Volume and Issue: 107(3)

Published: July 1, 2024

A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating ultimate product. Since non-coding introns disrupt eukaryotic genes, entails elimination of joining exons create functional molecule. Nevertheless, accurately finding splice sequence sites using various molecular biology techniques other biological approaches is complex time-consuming. paper presents precise reliable computer-aided diagnosis (CAD) technique for rapid correct identification site sequences. The proposed deep learning-based framework uses long short-term memory (LSTM) extract distinct patterns from RNA sequences, enabling accurate point mutation mapping. network employs one-hot encodings find sequential that effectively identify sites. thorough ablation study traditional machine learning, one-dimensional convolutional neural networks (1D-CNNs), recurrent (RNNs) models was conducted. LSTM outperformed existing state-of-the-art approaches, improving accuracy 3% 2% acceptor donor datasets.

Language: Английский

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Country-Based COVID-19 DNA Sequence Classification in Relation with International Travel Policy

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 1916 - 1916

Published: Feb. 26, 2024

As viruses evolve rapidly, variations in their DNA may arise due to environmental factors. This study examines the classification of COVID-19 sequences based on country origin and analyzes primary correlation with country’s international travel policy. Focusing from nine ASEAN countries, we conducted a two-class distinguish individual countries mixed others. The were initially dissected into 200 base pair units, deep-learning method was employed construct model. Our results showcase capacity differentiate varying accuracy for each country. Additionally, index policy, which reflects how implemented levels restrictions regarding inbound travel, several months before sequence collection date, moderately correlated within finding suggests preliminary insight that pandemic management might influence variation virus, determining whether these will distinctly those other or exhibit similarities.

Language: Английский

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A Hybrid Metaheuristic Aware Modified Mobile Net with Enriched Feature Extraction for Covid-19 Severity Detection and Classification

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: 136(2), P. 1047 - 1077

Published: May 1, 2024

Language: Английский

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