Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques DOI Creative Commons
Jorge F. Beltrán,

Lisandra Herrera-Belén,

Alejandro J. Yáñez

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 7, 2024

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor the etiology of various cancers. Traditionally, identifying these is both time-consuming and costly. With advancements computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities. Here, first time, we propose an innovative approach that combines Generative Adversarial Networks (GANs) with supervised to enhance accuracy generalizability viral oncoprotein prediction. Our methodology evaluated multiple models, including Random Forest, Multilayer Perceptron, Light Gradient Boosting Machine, eXtreme Boosting, Support Vector Machine. In ten-fold cross-validation our training dataset, GAN-enhanced Forest model demonstrated superior performance metrics: 0.976 accuracy, F1 score, 0.977 precision, sensitivity, 1.0 AUC. During independent testing, this achieved 0.982 These results establish new tool, VirOncoTarget, accessible via web application. We anticipate VirOncoTarget will be valuable resource researchers, enabling rapid reliable prediction advancing understanding their role biology.

Язык: Английский

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review DOI Creative Commons
Sanghyuk Roy Choi, Minhyeok Lee

Biology, Год журнала: 2023, Номер 12(7), С. 1033 - 1033

Опубликована: Июль 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.

Язык: Английский

Процитировано

64
Integrating Drone Imagery and AI for Improved Construction Site Management through Building Information Modeling DOI Creative Commons
Wonjun Choi, Seunguk Na, Seokjae Heo

и другие.

Buildings, Год журнала: 2024, Номер 14(4), С. 1106 - 1106

Опубликована: Апрель 15, 2024

In the rapidly advancing field of construction, digital site management and Building Information Modeling (BIM) are pivotal. This study explores integration drone imagery into construction process, aiming to create BIM models with enhanced object recognition capabilities. Initially, research sought achieve photorealistic rendering point cloud (PCMs) using blur/sharpen filters generative adversarial network (GAN) models. However, these techniques did not fully meet desired outcomes for rendering. The then shifted investigating additional methods, such as fine-tuning algorithms real-world datasets, improve accuracy. study’s findings present a nuanced understanding limitations potential pathways achieving in PCM, underscoring complexity task laying groundwork future innovations this area. Although faced challenges attaining original goal detection, it contributes valuable insights that may inform technological development management.

Язык: Английский

Процитировано

10
Neuroimage analysis using artificial intelligence approaches: a systematic review DOI
Eric Jacob Bacon, Dianning He,

N'bognon Angèle D'avilla Achi

и другие.

Medical & Biological Engineering & Computing, Год журнала: 2024, Номер 62(9), С. 2599 - 2627

Опубликована: Апрель 26, 2024

Язык: Английский

Процитировано

5
Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation DOI Creative Commons

Denis Sidorenko,

Stefan Pushkov,

Akhmed Sakip

и другие.

npj Aging, Год журнала: 2024, Номер 10(1)

Опубликована: Авг. 8, 2024

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, exploring architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic methylation along with metadata, predicting age identifying dual-purpose therapeutic targets potentially implicated aging age-associated diseases. In this study, we introduce Precious2GPT, architecture that integrates Conditional Diffusion (CDiffusion) decoder-only Multi-omics Pretrained Transformer (MoPT) models gene expression DNA data. Precious2GPT excels synthetic generation, outperforming Generative Adversarial Networks (CGANs), CDiffusion, MoPT. demonstrate is capable generating representative captures tissue- age-specific information from real transcriptomics methylomics Notably, surpasses other prediction accuracy using the generated it can generate beyond 120 years age. Furthermore, showcase potential model signatures colorectal cancer case study.

Язык: Английский

Процитировано

4
ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer DOI
Debasish Swapnesh Kumar Nayak,

Ruchika Das,

Santanu Sahoo

и другие.

Computational Biology and Chemistry, Год журнала: 2025, Номер 115, С. 108342 - 108342

Опубликована: Янв. 7, 2025

Язык: Английский

Процитировано

0
Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures DOI Creative Commons

Francesco Bruni Prenestino,

Enrico Barbierato, Alice Gatti

и другие.

Future Internet, Год журнала: 2025, Номер 17(2), С. 95 - 95

Опубликована: Фев. 19, 2025

Generating high-quality synthetic data is essential for advancing machine learning applications in financial time series, where scarcity and privacy concerns often pose significant challenges. This study proposes a novel hybrid architecture that combines variational autoencoders (VAEs) with Markov Chain Monte Carlo (MCMC) sampling to enhance the generation of robust sequential data. The model leverages Gated Recurrent Unit (GRU) layers capturing long-term temporal dependencies MCMC effective latent space exploration, ensuring high variability accuracy. Experimental evaluations on datasets Google, Tesla, Nestlé stock prices demonstrate model’s superior performance preserving statistical patterns, as validated by quantitative metrics (discriminative predictive scores), tests (Kolmogorov–Smirnov), t-Distributed Stochastic Neighbour Embedding (t-SNE) visualisations. experiments reveal scalability, maintaining fidelity even under augmented dataset sizes missing scenarios. These findings position proposed framework computationally efficient structurally simple alternative Generative Adversarial Network (GAN)-based methods, suitable real-world data-driven modelling.

Язык: Английский

Процитировано

0
Genome-wide analysis and expression pattern profiling of the DUF789 gene family in soybean (Glycine max L.) DOI
Madiha Zaynab, Yasir Sharif, Jallat Khan

и другие.

South African Journal of Botany, Год журнала: 2025, Номер 180, С. 1 - 11

Опубликована: Март 6, 2025

Язык: Английский

Процитировано

0
A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra DOI
R. Santoveña, Carlos Dafonte, M. Manteiga

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112954 - 112954

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Revolutionizing multi‐omics analysis with artificial intelligence and data processing DOI Open Access
Ali Yetgin

Quantitative Biology, Год журнала: 2025, Номер 13(3)

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0
A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis DOI Creative Commons
Timileyin Opeyemi Akande,

Oluwaseyi Omotayo Alabı,

Julianah B. Oyinloye

и другие.

Journal of Computing Theories and Applications, Год журнала: 2024, Номер 1(4), С. 368 - 385

Опубликована: Март 21, 2024

Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and engineering (CAE) systems. This review explores integration CAD CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights challenges traditional CAD/CAE, such as manual simulation limitations, proposes learning, especially models, a solution. The study aims to automate enhance wheel design, improve CAE simulations, predict mechanical characteristics, optimize performance metrics. employs architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), adversarial (GANs) learn from diverse designs generate optimized solutions. anticipated outcomes include more efficient processes, improved accuracy, adaptable solutions, facilitating into existing CAD/CAE expected transform practices by offering insights potential these technologies.

Язык: Английский

Процитировано

3