Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications

Sustainability, Год журнала: 2024, Номер 16(17), С. 7651 - 7651

Опубликована: Сен. 3, 2024

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production global food security. To combat these challenges, the integration of bioinformatics educational tools AI applications provide a synergistic approach identify analyze stress-responsive genes, regulatory networks molecular markers associated with stress tolerance. Bioinformatics offer robust framework for data collection, storage initial analysis, while enhance pattern recognition, predictive modeling real-time processing capabilities. This review uniquely integrates applications, highlighting their combined role in managing abiotic plants crops. The novelty is demonstrated by multiomics algorithms, providing deeper insights into response pathways, biomarker discovery recognition. Key include resistance gene network inference, omics plant monitoring through fusion remote sensing AI-assisted phenomics. Challenges such as handling big data, model interpretability, overfitting experimental validation remain there, but future prospects involve developing user-friendly platforms, establishing common standards, interdisciplinary collaboration harnessing mitigation strategies Educational initiatives, collaborations trainings are essential equip next generation researchers required skills utilize advanced effectively. convergence holds vast accelerating development stress-resilient crops, optimizing agricultural practices ensuring security under increasing environmental pressures. Moreover, this integrated crucial advancing sustainable agriculture amidst growing challenges.

Язык: Английский

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Confronting The Data Deluge: How Artificial Intelligence Can Be Used in the Study of Plant Stress

Computational and Structural Biotechnology Journal, Год журнала: 2024, Номер 23, С. 3454 - 3466

Опубликована: Сен. 17, 2024

Язык: Английский

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Can a plant biologist fix a thermostat?

New Phytologist, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Summary The shift to reductionist biology at the dawn of genome era yielded a ‘parts list’ plant genes and nascent understanding complex biological processes. Today, with genomics in full swing, advances high‐definition enabled precise temporal spatial analyses systems down single‐cell level. These insights, coupled artificial intelligence‐driven silico design, are propelling development first synthetic plants. By integrating approaches, researchers not only reimagining plants as sources food, fiber, fuel but also ‘environmental thermostats’ capable mitigating impacts changing climate.

Язык: Английский

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Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine

FEBS Open Bio, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

Medical digital twins (MDTs) are virtual representations of patients that simulate the biological, physiological, and clinical processes individuals to enable personalized medicine. With increasing complexity omics data, particularly multiomics, there is a growing need for advanced computational frameworks interpret these data effectively. Foundation models (FMs), large‐scale machine learning pretrained on diverse types, have recently emerged as powerful tools improving interpretability decision‐making in precision This review discusses integration FMs into MDT systems, their role enhancing multiomics data. We examine current challenges, recent advancements, future opportunities leveraging analysis MDTs, with focus application

Язык: Английский

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Artificial intelligence-driven plant bio-genomics research: a new era

Tropical Plants, Год журнала: 2025, Номер 4(1), С. 0 - 0

Опубликована: Янв. 1, 2025

Язык: Английский

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Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 30, 2024

Single-cell sequencing transformed biology and medicine, providing an unprecedented high-resolution view at the cellular level. However, vast variability inherent in single-cell data impedes its utility for in-depth downstream analysis. Inspired by foundation models natural language processing, recent advancements have led to development of Large Language Models (scLLMs). These are designed discern universal patterns across diverse datasets, thereby enhancing signal-to-noise ratio. Despite their potential, multiple studies indicate existing scLLMs do not perform well zero-short settings, highlighting a pressing need more effective adaptation techniques. This research proposes several techniques preserving original model parameters while selectively updating newly introduced tensors. approach aims overcome limitations associated with traditional fine-tuning practices, such as catastrophic forgetting computational inefficiencies. We introduce two Parameter-Efficient Fine-Tuning (PEFT) strategies specifically tailored refine cell type identification. Our investigations utilizing scGPT demonstrate that PEFT can enhance performance, added benefit up 90% reduction parameter training compared conventional methodologies. work paves way new direction leveraging greater efficiency efficacy biology.

Язык: Английский

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Application of machine learning and genomics for orphan crop improvement

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Янв. 24, 2025

Orphan crops are important sources of nutrition in developing regions and many tolerant to biotic abiotic stressors; however, modern crop improvement technologies have not been widely applied orphan due the lack resources available. There representatives across major types conservation genes between these related species can be used improvement. Machine learning (ML) has emerged as a promising tool for Transferring knowledge from using machine improve accuracy efficiency crops. Here, authors review transferring breeding.

Язык: Английский

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Data‐driven resources and computational tools in non‐model plant species

Physiologia Plantarum, Год журнала: 2025, Номер 177(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Decoding G-Quadruplexes Sequence in Vitis vinifera: Regulatory Region Enrichment, Drought Stress Adaptation, and Sugar–Acid Metabolism Modulation

Plants, Год журнала: 2025, Номер 14(8), С. 1180 - 1180

Опубликована: Апрель 10, 2025

G-quadruplexes play a crucial role in transcription, translation, and DNA replication plant genomes. Here, we comprehensively examined the prevalence functions of Vitis vinifera. A total 467,813 were identified grapevine genome, with enrichment promoter (0.54/kbp) near transcription start sites (TSSs, 1.00/kbp), showed conservative strand preference. The G-quadruplex density centromeres exhibited heterogeneity. differentially expressed genes (DEGs) under two-day drought stress manifested high TSS regions. upregulated DEGs template strand-biased enrichment, while downregulated displayed coding dominance linked to metal ion homeostasis sugar–acid metabolism pathways, respectively. enriched key genes, including pyruvate kinase sucrose synthase. number transferase VINV was higher than that CWINV NINV genes. This study revealed as regulatory elements response berry development, providing abundant genetic targets for precision breeding quality improvement grapevines.

Язык: Английский

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Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician

Asia-Pacific Journal of Ophthalmology, Год журнала: 2024, Номер 13(4), С. 100084 - 100084

Опубликована: Июль 1, 2024

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling to understand, generate, derive meaning from language. NLP's potential applications in medical field are extensive vary extracting data Electronic Health Records -one its most well-known frequently exploited uses- investigating relationships among genetics, biomarkers, drugs, diseases for proposal new medications. NLP can be useful clinical decision support, patient monitoring, or image analysis. Despite vast potential, real-world application still limited due various challenges constraints, evolution predominantly continues within research domain. However, with increasingly widespread use NLP, particularly availability large language models, such as ChatGPT, it crucial professionals aware status, uses, limitations these technologies.

Язык: Английский

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