Relevant bibliographies by topics / Protein language models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Protein language models'

Author: Grafiati
Published: 26 October 2024
Last updated: 31 July 2025

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Journal articles on the topic "Protein language models"

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Tang, Lin. "Protein language models using convolutions." Nature Methods 21, no. 4 (2024): 550. http://dx.doi.org/10.1038/s41592-024-02252-3.

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Lee, Jin Sub, Osama Abdin, and Philip M. Kim. "Language models for protein design." Current Opinion in Structural Biology 92 (June 2025): 103027. https://doi.org/10.1016/j.sbi.2025.103027.

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Ali, Sarwan, Prakash Chourasia, and Murray Patterson. "When Protein Structure Embedding Meets Large Language Models." Genes 15, no. 1 (2023): 25. http://dx.doi.org/10.3390/genes15010025.

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Protein structure analysis is essential in various bioinformatics domains such as drug discovery, disease diagnosis, and evolutionary studies. Within structural biology, the classification of protein structures is pivotal, employing machine learning algorithms to categorize structures based on data from databases like the Protein Data Bank (PDB). To predict protein functions, embeddings based on protein sequences have been employed. Creating numerical embeddings that preserve vital information while considering protein structure and sequence presents several challenges. The existing literature
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Ferruz, Noelia, and Birte Höcker. "Controllable protein design with language models." Nature Machine Intelligence 4, no. 6 (2022): 521–32. http://dx.doi.org/10.1038/s42256-022-00499-z.

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Li, Xiang, Zhuoyu Wei, Yueran Hu, and Xiaolei Zhu. "GraphNABP: Identifying nucleic acid-binding proteins with protein graphs and protein language models." International Journal of Biological Macromolecules 280 (November 2024): 135599. http://dx.doi.org/10.1016/j.ijbiomac.2024.135599.

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Singh, Arunima. "Protein language models guide directed antibody evolution." Nature Methods 20, no. 6 (2023): 785. http://dx.doi.org/10.1038/s41592-023-01924-w.

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Tran, Chau, Siddharth Khadkikar, and Aleksey Porollo. "Survey of Protein Sequence Embedding Models." International Journal of Molecular Sciences 24, no. 4 (2023): 3775. http://dx.doi.org/10.3390/ijms24043775.

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Derived from the natural language processing (NLP) algorithms, protein language models enable the encoding of protein sequences, which are widely diverse in length and amino acid composition, in fixed-size numerical vectors (embeddings). We surveyed representative embedding models such as Esm, Esm1b, ProtT5, and SeqVec, along with their derivatives (GoPredSim and PLAST), to conduct the following tasks in computational biology: embedding the Saccharomyces cerevisiae proteome, gene ontology (GO) annotation of the uncharacterized proteins of this organism, relating variants of human proteins to d
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Pokharel, Suresh, Pawel Pratyush, Hamid D. Ismail, Junfeng Ma, and Dukka B. KC. "Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction." International Journal of Molecular Sciences 24, no. 21 (2023): 16000. http://dx.doi.org/10.3390/ijms242116000.

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O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a
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Hu, An, Linai Kuang, and Dinghai Yang. "LPBERT: A Protein–Protein Interaction Prediction Method Based on a Pre-Trained Language Model." Applied Sciences 15, no. 6 (2025): 3283. https://doi.org/10.3390/app15063283.

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The prediction of protein–protein interactions is a key task in proteomics. Since protein sequences are easily available and understandable, they have become the primary data source for predicting protein–protein interactions. With the development of natural language processing technology, language models have become a research hotspot in recent years, and protein language models have also been developed accordingly. Compared with single-encoding methods, such as Word2Vec and one-hot, language models specifically designed for proteins are expected to extract more comprehensive information from
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Pang, Yihe, and Bin Liu. "IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models." PLOS Computational Biology 19, no. 11 (2023): e1011657. http://dx.doi.org/10.1371/journal.pcbi.1011657.

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Intrinsically disordered proteins (IDPs) and regions (IDRs) are a class of functionally important proteins and regions that lack stable three-dimensional structures under the native physiologic conditions. They participate in critical biological processes and thus are associated with the pathogenesis of many severe human diseases. Identifying the IDPs/IDRs and their functions will be helpful for a comprehensive understanding of protein structures and functions, and inform studies of rational drug design. Over the past decades, the exponential growth in the number of proteins with sequence info
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Dissertations / Theses on the topic "Protein language models"

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Meynard, Barthélémy. "Language Models towards Conditional Generative Modelsof Proteins Sequences." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS195.

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Nous commençons par examiner ce qui rend un modèle génératif efficace pour les séquences de protéines. Dans notre première étude, "Interpretable Pairwise Distillations for Generative Protein Sequence Models" nous comparons les modèles de réseaux de neurones complexes à des modèles de distributions pair à pair plus simples. Cette comparaison révèle que les modèles plus simples peuvent égaler de près la performance des modèles plus complexes dans la prédiction de l'effet des mutations sur les protéines. Cette découverte remet en question l'hypothèse selon laquelle les modèles plus complexes sont
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Vander, Meersche Yann. "Étude de la flexibilité des protéines : analyse à grande échelle de simulations de dynamique moléculaire et prédiction par apprentissage profond." Electronic Thesis or Diss., Université Paris Cité, 2024. http://www.theses.fr/2024UNIP5147.

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Les protéines sont essentielles aux processus biologiques. Comprendre leur dynamique est crucial pour élucider leurs fonctions biologiques et leurs interactions. Cependant, mesurer expérimentalement cette flexibilité reste un défi en raison des limitations techniques et des coûts associés. Cette thèse vise à approfondir la compréhension des propriétés dynamiques des protéines et à proposer des méthodes bioinformatique permettant de prédire leur flexibilité directement à partir de leur séquence. Ces travaux s'organisent en quatre axes principaux : 1) Prédiction de la flexibilité des protéines e
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Hladiš, Matej. "Réseaux de neurones en graphes et modèle de langage des protéines pour révéler le code combinatoire de l'olfaction." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5024.

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Les mammifères identifient et interprètent une myriade de stimuli olfactifs par un mécanisme de codage complexe reposant sur la reconnaissance des molécules odorantes par des centaines de récepteurs olfactifs (RO). Ces interactions génèrent des combinaisons uniques de récepteurs activés, appelées code combinatoire, que le cerveau humain interprète comme la sensation que nous appelons l'odeur. Jusqu'à présent, le grand nombre de combinaisons possibles entre les récepteurs et les molécules a empêché une étude expérimentale à grande échelle de ce code et de son lien avec la perception des odeurs.
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Books on the topic "Protein language models"

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KC, Dukka B., ed. Large Language Models (LLMs) in Protein Bioinformatics. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4623-6.

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Bovington, Sue. Tigris/Thames. [Sue Bovington], 2011.

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Beatriz, Solís Leree, ed. La Ley televisa y la lucha por el poder en México. Universidad Autónoma Metropolitana, Unidad Xochimilco, 2009.

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Yoshikawa, Saeko. William Wordsworth and Modern Travel. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781789621181.001.0001.

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This book explores William Wordsworth’s pervasive influence on the tourist landscapes of the Lake District throughout the age of transport revolutions, popular tourism, and the Great 1914-18 War. It reveals how Wordsworth’s response to railways was not a straightforward matter of opposition and protest; his ideas were taken up by advocates and opponents of railways, and through their controversies had a surprising impact on the earliest motorists as they sought a language to describe the liberty and independence of their new mode of travel. Once the age of motoring was underway, the outbreak o
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Tucker Edmonds, Joseph L. Other Black Church. The Rowman & Littlefield Publishing Group, 2020. https://doi.org/10.5040/9781978719972.

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The Other Black Church: Alternative Christian Movements and the Struggle for Black Freedom examines the movements led by Father Divine, Charles Mason, and Albert Cleage (later known as Jaramogi Abebe Agyeman) as alternative Christian movements in the middle of the twentieth century that radically re-envisioned the limits and possibilities of Black citizenship. These movements not only rethink the value and import of Christian texts and reimagined the role of the Black Christian prophetic tradition, but they also outlined a new model of protest that challenged the language and logic of Black es
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Hardiman, David. The Nonviolent Struggle for Indian Freedom, 1905-19. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190920678.001.0001.

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Much of the recent surge in writing about the practice of nonviolent forms of resistance has focused on movements that occurred after the end of the Second World War, many of which have been extremely successful. Although the fact that such a method of civil resistance was developed in its modern form by Indians is acknowledged in this writing, there has not until now been an authoritative history of the role of Indians in the evolution of the phenomenon.The book argues that while nonviolence is associated above all with the towering figure of Mahatma Gandhi, 'passive resistance' was already b
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McNally, Michael D. Defend the Sacred. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691190907.001.0001.

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From North Dakota's Standing Rock encampments to Arizona's San Francisco Peaks, Native Americans have repeatedly asserted legal rights to religious freedom to protect their sacred places, practices, objects, knowledge, and ancestral remains. But these claims have met with little success in court because Native American communal traditions don't fit easily into modern Western definitions of religion. This book explores how, in response to this situation, Native peoples have creatively turned to other legal means to safeguard what matters to them. To articulate their claims, Native peoples have
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Meddings, Jennifer, Vineet Chopra, and Sanjay Saint. Preventing Hospital Infections. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780197509159.001.0001.

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This book provides a detailed, step-by-step description of a model quality improvement intervention for hospitals, pinpointing the obstacles and showing how to surmount them. This second edition has been carefully updated, with new material describing some technical aspects of infection prevention, new tools for use by front-line providers, and results of recent large collaborative infection prevention studies. In easy-to-read, user-friendly language, it explains why clinicians neglect or actively oppose quality changes—from physicians who distrust change, to nurses who want to protect their t
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Halvorsen, Tar, and Peter Vale. One World, Many Knowledges: Regional experiences and cross-regional links in higher education. African Minds, 2016. http://dx.doi.org/10.47622/978-0-620-55789-4.

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Various forms of academic co-operation criss-cross the modern university system in a bewildering number of ways, from the open exchange of ideas and knowledge, to the sharing of research results, and frank discussions about research challenges. Embedded in these scholarly networks is the question of whether a global template for the management of both higher education and national research organisations is necessary, and if so, must institutions slavishly follow the high-flown language of the global knowledge society or risk falling behind in the ubiquitous university ranking system? Or are th
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Book chapters on the topic "Protein language models"

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Xu, Xiaotong, and Alexandre M. J. J. Bonvin. "Ranking Protein–Protein Models with Large Language Models and Graph Neural Networks." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4623-6_4.

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Pokharel, Suresh, Pawel Pratyush, Meenal Chaudhari, et al. "A Survey of Pretrained Protein Language Models." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4623-6_1.

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Liu, Yufan, and Boxue Tian. "CLAPE: Protein–Ligand Binding Site Prediction via Protein Language Models." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4623-6_18.

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Xu, Yaoyao, Xinjian Zhao, Xiaozhuang Song, Benyou Wang, and Tianshu Yu. "Boosting Protein Language Models with Negative Sample Mining." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70381-2_13.

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U, Kaicheng, Sophia Meixuan Zhang, Suresh Pokharel, et al. "Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein–Protein Interaction Analysis." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4623-6_15.

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Zhao, Junming, Chao Zhang, and Yunan Luo. "Contrastive Fitness Learning: Reprogramming Protein Language Models for Low-N Learning of Protein Fitness Landscape." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-1-0716-3989-4_55.

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Zhang, Yichuan, Yongfang Qin, Mahdi Pourmirzaei, Qing Shao, Duolin Wang, and Dong Xu. "Enhancing Structure-Aware Protein Language Models with Efficient Fine-Tuning for Various Protein Prediction Tasks." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4623-6_2.

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Pratyush, Pawel, Suresh Pokharel, Hamid D. Ismail, Soufia Bahmani, and Dukka B. KC. "LMPTMSite: A Platform for PTM Site Prediction in Proteins Leveraging Transformer-Based Protein Language Models." In Methods in Molecular Biology. Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-4196-5_16.

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Ghazikhani, Hamed, and Gregory Butler. "A Study on the Application of Protein Language Models in the Analysis of Membrane Proteins." In Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23210-7_14.

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Wu, Tianqi, Weihang Cheng, and Jianlin Cheng. "Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks." In Methods in Molecular Biology. Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-4196-5_3.

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Conference papers on the topic "Protein language models"

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Amjad, Maheera, Ayesha Munir, Usman Zia, and Rehan Zafar Paracha. "Pre-trained Language Models for Decoding Protein Language: a Survey." In 2024 4th International Conference on Digital Futures and Transformative Technologies (ICoDT2). IEEE, 2024. http://dx.doi.org/10.1109/icodt262145.2024.10740205.

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Liu, Xu, Yiming Li, Fuhao Zhang, et al. "ComLMEss: Combining multiple protein language models enables accurate essential protein prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822205.

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Sun, Xin, and Yuhao Wu. "Combinative Bio-feature Proteins Generation via Pre-trained Protein Large Language Models." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821839.

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Brabec, Matyáš, and David Hoksza. "Integrating Structural Features with Protein Language Models to Predict Protein-Ligand Binding Sites." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10973773.

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Liang, Po-Yu, Xueting Huang, Tibo Duran, Andrew J. Wiemer, and Jun Bai. "Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821777.

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Jiang, Yanfeng, Ning Sun, Zhengxian Lu, et al. "MEFold: Memory-Efficient Optimization for Protein Language Models via Chunk and Quantization." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651470.

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Chen, Xiang, Lin Zhang, Zi Liu, and Xuan Xiao. "DTI Prediction Network Based on Protein Language Models and Graph Neural Networks." In 2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI). IEEE, 2024. https://doi.org/10.1109/iccbd-ai65562.2024.00025.

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Kim, Yunsoo. "Foundation Model for Biomedical Graphs: Integrating Knowledge Graphs and Protein Structures to Large Language Models." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-srw.30.

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Shen, Yiqing, Zan Chen, Michail Mamalakis, et al. "A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821894.

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Zhang, Jun, Zhiqiang Yan, Hao Zeng, and Zexuan Zhu. "PAIR: protein-aptamer interaction prediction based on language models and contrastive learning framework." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822859.

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Reports on the topic "Protein language models"

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Shani, Uri, Lynn Dudley, Alon Ben-Gal, Menachem Moshelion, and Yajun Wu. Root Conductance, Root-soil Interface Water Potential, Water and Ion Channel Function, and Tissue Expression Profile as Affected by Environmental Conditions. United States Department of Agriculture, 2007. http://dx.doi.org/10.32747/2007.7592119.bard.

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Constraints on water resources and the environment necessitate more efficient use of water. The key to efficient management is an understanding of the physical and physiological processes occurring in the soil-root hydraulic continuum.While both soil and plant leaf water potentials are well understood, modeled and measured, the root-soil interface where actual uptake processes occur has not been sufficiently studied. The water potential at the root-soil interface (yᵣₒₒₜ), determined by environmental conditions and by soil and plant hydraulic properties, serves as a boundary value in soil and p
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Wu, Jyun-Jie. Improving Predictive Efficiency and Literature Quality Assessment for Lung Cancer Complications Post-Proton Therapy Through Large Language Models and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.8.0103.

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Melnyk, Iurii. JUSTIFICATION OF OCCUPATION IN GERMAN (1938) AND RUSSIAN (2014) MEDIA: SUBSTITUTION OF AGGRESSOR AND VICTIM. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11101.

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The article is dedicated to the examination and comparison of the justification of occupation of a neighboring country in the German (1938) and Russian (2014) media. The objective of the study is to reveal the mechanics of the application of the classical manipulative method of substituting of aggressor and victim on the material of German and Russian propaganda in 1938 and in 2014 respectively. According to the results of the study, clear parallels between the two information strategies can be traced at the level of the condemnation of internal aggression against a national minority loyal to
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Yatsymirska, Mariya. KEY IMPRESSIONS OF 2020 IN JOURNALISTIC TEXTS. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11107.

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The article explores the key vocabulary of 2020 in the network space of Ukraine. Texts of journalistic, official-business style, analytical publications of well-known journalists on current topics are analyzed. Extralinguistic factors of new word formation, their adaptation to the sphere of special and socio-political vocabulary of the Ukrainian language are determined. Examples show modern impressions in the media, their stylistic use and impact on public opinion in a pandemic. New meanings of foreign expressions, media terminology, peculiarities of translation of neologisms from English into
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel se
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