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

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 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.

Language: Английский

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

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

Citations

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

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(4), P. 1106 - 1106

Published: April 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.

Language: Английский

Citations

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

N'bognon Angèle D'avilla Achi

et al.

Medical & Biological Engineering & Computing, Journal Year: 2024, Volume and Issue: 62(9), P. 2599 - 2627

Published: April 26, 2024

Language: Английский

Citations

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

et al.

npj Aging, Journal Year: 2024, Volume and Issue: 10(1)

Published: Aug. 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.

Language: Английский

Citations

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

et al.

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 115, P. 108342 - 108342

Published: Jan. 7, 2025

Language: Английский

Citations

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

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(2), P. 95 - 95

Published: Feb. 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.

Language: Английский

Citations

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

et al.

South African Journal of Botany, Journal Year: 2025, Volume and Issue: 180, P. 1 - 11

Published: March 6, 2025

Language: Английский

Citations

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

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112954 - 112954

Published: March 1, 2025

Language: Английский

Citations

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

Quantitative Biology, Journal Year: 2025, Volume and Issue: 13(3)

Published: April 7, 2025

Language: Английский

Citations

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

et al.

Journal of Computing Theories and Applications, Journal Year: 2024, Volume and Issue: 1(4), P. 368 - 385

Published: March 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.

Language: Английский

Citations

3