A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data DOI Creative Commons
Oussama M’hamdi, Sándor Takács,

Gábor Palotás

et al.

Plants, Journal Year: 2024, Volume and Issue: 13(5), P. 746 - 746

Published: March 6, 2024

The tomato as a raw material for processing is globally important and pivotal in dietary agronomic research due to its nutritional, economic, health significance. This study explored the potential of machine learning (ML) predicting quality, utilizing data from 48 cultivars 28 locations Hungary over 5 seasons. It focused on °Brix, lycopene content, colour (a/b ratio) using extreme gradient boosting (XGBoost) artificial neural network (ANN) models. results revealed that XGBoost consistently outperformed ANN, achieving high accuracy °Brix (R² = 0.98, RMSE 0.07) content 0.87, 0.61), excelling prediction with R² 0.93 0.03. ANN lagged behind particularly prediction, showing negative value −0.35. Shapley additive explanation’s (SHAP) summary plot analysis indicated both models are effective tomatoes, highlighting different aspects data. SHAP highlighted models’ efficiency (especially predictions) underscored significant influence cultivar choice environmental factors like climate soil. These findings emphasize importance selecting fine-tuning appropriate ML model enhancing precision agriculture, underlining XGBoost’s superiority handling complex quality assessment.

Language: Английский

Multimodal biomedical AI DOI Open Access
Julián Acosta, Guido J. Falcone, Pranav Rajpurkar

et al.

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(9), P. 1773 - 1784

Published: Sept. 1, 2022

Language: Английский

Citations

601
Applications of multi‐omics analysis in human diseases DOI Creative Commons

Chongyang Chen,

Jing Wang,

Donghui Pan

et al.

MedComm, Journal Year: 2023, Volume and Issue: 4(4)

Published: July 31, 2023

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell proteomics and metabolomics, spatial so on, which play a great role in promoting study human diseases. Most current reviews focus on describing development multi-omics technologies, data integration, particular disease; however, few them provide comprehensive systematic introduction multi-omics. This review outlines existing technical categories multi-omics, cautions for experimental design, focuses integrated analysis methods especially approach machine learning deep integration corresponding tools, medical researches (e.g., cancer, neurodegenerative diseases, aging, drug target discovery) as well open-source tools databases, finally, discusses challenges future directions precision medicine. With algorithms, important disease research, also provided detailed introduction. will guidance researchers, who are just entering into research.

Language: Английский

Citations

194
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine DOI Open Access
Xiujing He, Xiaowei Liu,

Fengli Zuo

et al.

Seminars in Cancer Biology, Journal Year: 2022, Volume and Issue: 88, P. 187 - 200

Published: Dec. 31, 2022

Language: Английский

Citations

164
Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions DOI Creative Commons
Shams Forruque Ahmed, Md. Sakib Bin Alam,

Shaila Afrin

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 102, P. 102060 - 102060

Published: Sept. 29, 2023

The Internet of Medical Things (IoMT) has created a wide range opportunities for knowledge exchange in numerous industries. include patient empowerment, healthcare collaboration, medical education and training, remote monitoring telemedicine, customized treatment plans, data sharing innovation, continuous learning, supply chain management, public health initiatives, wearable devices, quality improvement initiatives. However, the adoption IoMT faces challenges regarding interoperability, privacy, security, regulatory, infrastructure costs. This paper aims to address implications fusion IoMT, as well associated security their potential solutions, which are lacking literature. Data collected from devices direct impact on accuracy predictions because its quality, quantity, relevance. With an 99.53% 99.99%, Epilepsy seizure detector-based Naive Bayes (ESDNB) algorithm is found be most effective detecting epileptic seizures networks. way stored must also undergo major revolution, all phases—collection, protection, storage—need improved. standardization architecture measures may improve detection threats compromises. Methods detect malware cross platforms avenue future research that can effectively tackle heterogeneity systems. Cryptography blockchain technology have shown promising ways increase IoMT-based system. findings this review will assist variety stakeholders ecosystem.

Language: Английский

Citations

118
Using artificial intelligence to improve public health: a narrative review DOI Creative Commons
David B. Olawade,

Ojima J. Wada,

Aanuoluwapo Clement David-Olawade

et al.

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 11

Published: Oct. 26, 2023

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, public health, the widespread employment only began recently, with advent COVID-19. This review examines advances health potential challenges that lie ahead. Some ways aided delivery are via spatial modeling, risk prediction, misinformation control, surveillance, disease forecasting, pandemic/epidemic diagnosis. implementation not universal due to factors including limited infrastructure, lack technical understanding, data paucity, ethical/privacy issues.

Language: Английский

Citations

114
Applications of transformer-based language models in bioinformatics: a survey DOI Creative Commons
Shuang Zhang, Rui Fan, Yuti Liu

et al.

Bioinformatics Advances, Journal Year: 2023, Volume and Issue: 3(1)

Published: Jan. 1, 2023

Abstract Summary The transformer-based language models, including vanilla transformer, BERT and GPT-3, have achieved revolutionary breakthroughs in the field of natural processing (NLP). Since there are inherent similarities between various biological sequences languages, remarkable interpretability adaptability these models prompted a new wave their application bioinformatics research. To provide timely comprehensive review, we introduce key developments by describing detailed structure transformers summarize contribution to wide range research from basic sequence analysis drug discovery. While applications diverse multifaceted, identify discuss common challenges, heterogeneity training data, computational expense model interpretability, opportunities context We hope that broader community NLP researchers, bioinformaticians biologists will be brought together foster future development inspire novel unattainable traditional methods. Supplementary information data available at Bioinformatics Advances online.

Language: Английский

Citations

97
Web-based multi-omics integration using the Analyst software suite DOI
Jessica Ewald,

Guangyan Zhou,

Yao Lü

et al.

Nature Protocols, Journal Year: 2024, Volume and Issue: 19(5), P. 1467 - 1497

Published: Feb. 14, 2024

Language: Английский

Citations

71
Advancing CAR T cell therapy through the use of multidimensional omics data DOI
Jingwen Yang, Yamei Chen, Ying Jing

et al.

Nature Reviews Clinical Oncology, Journal Year: 2023, Volume and Issue: 20(4), P. 211 - 228

Published: Jan. 31, 2023

Language: Английский

Citations

68
Missing data in multi-omics integration: Recent advances through artificial intelligence DOI Creative Commons
Javier E. Flores, Daniel Claborne, Zachary D. Weller

et al.

Frontiers in Artificial Intelligence, Journal Year: 2023, Volume and Issue: 6

Published: Feb. 9, 2023

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these is only possible an integrated, multi-omic perspective. This has presented the need for development integration approaches that are able to capture complex, often non-linear, define biological adapted challenges combining heterogenous data across 'omic views. A principal challenge missing because all biomolecules not measured in samples. Due either cost, instrument sensitivity, or other experimental factors, sample may be one techologies. Recent methodological developments artificial intelligence statistical learning have greatly facilitated analyses multi-omics data, however many techniques assume access completely observed data. subset methods incorporate mechanisms handling partially samples, focus this review. We describe recently developed approaches, noting their primary use cases highlighting each method's approach additionally provide overview traditional workflows limitations; we discuss potential avenues further as well how issue its current solutions generalize beyond context.

Language: Английский

Citations

67
Artificial intelligence in plant breeding DOI Creative Commons
Muhammad Amjad Farooq, Shang Gao, Muhammad Adeel Hassan

et al.

Trends in Genetics, Journal Year: 2024, Volume and Issue: 40(10), P. 891 - 908

Published: Aug. 7, 2024

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) renowned for its prowess big data analysis and pattern recognition, revolutionizing numerous scientific domains including We explore the wider potential of AI tools various facets breeding, collection, unlocking genetic diversity within genebanks, bridging genotype–phenotype gap facilitate This will enable development cultivars tailored projected future environments. Moreover, also hold promise refining traits by improving precision gene-editing systems predicting effects gene variants on phenotypes. Leveraging AI-enabled breeding can augment efficiency programs holds optimizing cropping at grassroots level. entails identifying optimal inter-cropping crop-rotation models agricultural sustainability field.

Language: Английский

Citations

35