Bibliographies thématiques / Analisi dati omici

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Littérature scientifique sur le sujet « Analisi dati omici »

Auteur : Grafiati
Publié le 9 mars 2023
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Analisi dati omici"

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Lobato-Delgado, Barbara, Torres Blanca María Priego, and Morillo Daniel Sanchez. "Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis." Cancers 14, no. 13 (2022): 3215. https://doi.org/10.3390/cancers14133215.

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ISIMOV, A. M., G. B. ADMANOVA, A. T. SARZHIGITOVA, N. K. KEMALOVA та B. BAKYTZHANKYZY. "PYTHON КАК ИНСТРУМЕНТ ДЛЯ РЕКОНСТРУКЦИИ И АНАЛИЗА МЕТАБОЛИЧЕСКОЙ МОДЕЛИ В МАСШТАБЕ ГЕНОМА". МИКРОБИОЛОГИЯ ЖӘНЕ ВИРУСОЛОГИЯ, № 4(43) (19 грудня 2023): 32–62. http://dx.doi.org/10.53729/mv-as.2023.04.03.

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The cost reduction of high-throughput technologies has led to the development of data analysis methods that deal with the increasing availability of omics datasets. Constraint-based genome-scale metabolic modeling is an example of such a method. It uses omics data and biochemical networks, a mechanistic framework to combine omics data, to describe steady-state metabolic flux distributions. Therefore, a system of mass balance equations is created that can be solved via linear programming. To date, most software for constraint-based genome-scale modeling has relied on MATLAB, a popular proprieta
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Mrs., Shreevarshini M. R., M. Usha Rani Mrs., and Shreemanth Mr. "One Stop Shop Effective Service Based on Cloud Computing." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Refereed Multidisciplinary Research Journal 2, no. 8 (2023): 69–79. https://doi.org/10.5281/zenodo.13986445.

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HiOmics is a cloud-based platform designed to simplify the analysis and visualization of large-scale omics data generated by high-throughput sequencing technology. It offers a user-friendly interface built with Element Plus and utilizes Docker containers to ensure reliable and reproducible results. HiOmics employs Workflow Description Language (WDL) and the Cromwell engine to create flexible and portable data analysis workflows. To address data format compatibility, HiOmics includes DataCheck, a Golang-based tool for verification and conversion. Leveraging the object storage and batch computin
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João, C. Sequeira, Rocha Miguel, Madalena Alves M., and F. Salvador Andreia. "UPIMAPI, reCOGnizer and KEGGCharter: Bioinformatics tools for functional annotation and visualization of (meta)-omics datasets." Computational and Structural Biotechnology Journal 20 (April 11, 2022): 1798–810. https://doi.org/10.1016/j.csbj.2022.03.042.

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Omics and meta-omics technologies are powerful approaches to explore microorganisms&rsquo; functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and&nbsp;<em>meta</em>-omics analyses, together with knowledgebases encompassing information on genes, proteins, taxonomic and functional annotation, among other types of information, are valuable resources for analyzing omics data. Although several&nbsp;bioinformatics&nbsp;resources are available for&nbsp;<em>meta</em>-omics analyses, many require significant compu
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Hendri Putrananda and Melladia. "UJI AKURASI FOTO UDARA WAHANA UNMANNED AERIAL VEHICLE (UAV) DI PULAU ANGSO DUO SUMATERA BARAT." Journal of Scientech Research and Development 2, no. 2 (2020): 025–33. http://dx.doi.org/10.56670/jsrd.v2i2.12.

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Foto udara Pulau Angso Duo dapat berperan sebagai bahan dasar untuk memperoleh berbagai informasi spasial yang bisa bermanfaat bagi pengelola pulau dan pemerintah setempat baik dalam upaya perencanaan konservasi maupun pembangunan. Informasi-informasi spasial yang dapat diperoleh misalnya lebar dan panjang garis pantai, luas daratan, tutupan vegetasi, penggunaan lahan, sebaran objek bangunan, dan lain sebagainya. Meskipun demikian namun informasi-informasi spasial tersebut belum bisa diperoleh sebelum dilakukan uji akurasi objek yang bertujuan untuk mengetahui tingkat ketelitian foto udara. Be
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Hidayati, Nur. "Implementasi Pembelajaran PAI Bagi Tunalaras di Lembaga Pembinaan Khusus Anak Kelas I Tangerang." TARBAWI : Jurnal Pendidikan Agama Islam 6, no. 02 (2021): 111–22. http://dx.doi.org/10.26618/jtw.v6i02.4395.

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Penelitian ini bertujuan untuk mengetahui implementasi pembelajaran PAI bagi tunalaras di Lembaga Pembinaan Khusus Anak Klas I Tangerang, apa faktor pendukung dan penghambat nya, serta bagaimana upaya mengatasi faktor penghambat nya. Metode yang digunakan dalam penelitian ini adalah metode penelitian kualitatif dengan jenis penelitian deskriptif. Teknik pengumpulan data melalui observasi, wawancara, dan dokumentasi. Teknik analisis data dengan teknik triangulasi, yang meliputi reduksi data, display data, dan kesimpulan. Hasil penelitian ini yaitu: 1) Implementasi pembelajaran PAI bagi tunalara
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Rusli, Rusli, and Fithry Muthmainnah. "KAJIAN FONOLOGIS TERHADAP KEKELIRUAN PELAFALAN FONEM PADA SISWA SD TINGKAT AWAL." ELEMENTARY: Jurnal Inovasi Pendidikan Dasar 5, no. 2 (2025): 286–95. https://doi.org/10.51878/elementary.v5i2.5860.

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This study examines the mispronunciation of Indonesian phonemes by second-grade students of SD Nahdlatul Ulama Kaplongan, Indramayu, when reading phonemes, words, phrases, and sentences. This research was carried out with a qualitative descriptive approach, through a data collection process carried out through direct observation and documentation of eight students. The results of the analysis showed that there were various types of phonological errors, such as substitution, distortion, and sound omission. The results of the analysis showed that most of the pronunciation mistakes made by studen
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Yang, Mingxuan, Decheng Huang, Haodong Zhang, and Wenxuan Zheng. "AI-Enabled Precision Medicine: Optimizing Treatment Strategies Through Genomic Data Analysis." Journal of Computer Technology and Applied Mathematics 1, no. 3 (2024): 73–84. https://doi.org/10.5281/zenodo.13380619.

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This research investigates how Artificial Intelligence (AI) can be used in precision medicine to improve treatment strategies by analysing genomic data. We explore sophisticated machine learning methods for examining complicated genomic datasets, such as deep learning models and ensemble techniques. The study focuses on overcoming obstacles in analysing complex genomic data and introduces innovative methods for combining multi-omics data. We create predictive models using AI technology to forecast patient prognosis with better accuracy in predicting disease progression and treatment results. T
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Raees N Bibi, Gustianingsih, and Alemina Br. Perangin-angin. "Phonological Disorder in Speaking Among Stroke Patients: A Neurolinguistics Study." Modality Journal: International Journal of Linguistics and Literature 4, no. 2 (2024): 186–96. https://doi.org/10.30983/mj.v4i2.8891.

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The objective of this research is to identify the types of phonological disorders in Indonesian-speaking stroke patients and to determine the disturbed dominant hemisphere based on the structure of these phonological disorders. The data are obtained from the utterances of stroke patients who experience difficulty in producing speech. Data are collected using the listening method related to language disorders in stroke patients with basic recording techniques, followed by the technique of free listening without speech involvement. Data analysis uses the matching method, with the basic technique
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Destri Susilowati and Jatmika Nurhadi. "Realisasi Tuturan Anak Down Syndrome dalam Interaksi Pembelajaran di Sekolah Luar Biasa." Jurnal Onoma: Pendidikan, Bahasa, dan Sastra 10, no. 1 (2024): 483–93. http://dx.doi.org/10.30605/onoma.v10i1.3329.

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Penelitian ini bertujuan untuk mendeskripsikan wujud tindak tutur ilokusi dan kendala tutur anak down syndrome dalam interaksi pembelajaran di SPLB C YPLB Kota Bandung. Penelitian ini merupakan penelitian dengan pendekatan kualitatif. Subjek penelitian merupakan siswa down syndrome tingkat Sekolah Dasar di SPLB C YPLB Kota Bandung. Teknik pengumpulan data melalui teknik simak bebas libat cakap (SBLC). Analisis data menggunakan reduksi data, penyajian data, dan penarikan simpulan. Hasil penelitian realisasi tuturan anak down syndrome dalam interaksi pembelajaran di SPLB C YPLB Kota Bandung dite
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Thèses sur le sujet "Analisi dati omici"

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Berti, Elisa. "Applicazione del metodo QDanet_PRO alla classificazione di dati omici." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9411/.

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Il presente lavoro di tesi si pone nell'ambito dell'analisi dati attraverso un metodo (QDanet_PRO), elaborato dal Prof. Remondini in collaborazine coi Dott. Levi e Malagoli, basato sull'analisi discriminate a coppie e sulla Teoria dei Network, che ha come obiettivo la classificazione di dati contenuti in dataset dove il numero di campioni è molto ridotto rispetto al numero di variabili. Attraverso questo studio si vogliono identificare delle signature, ovvero un'insieme ridotto di variabili che siano in grado di classificare correttamente i campioni in base al comportamento delle variabili st
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MASPERO, DAVIDE. "Computational strategies to dissect the heterogeneity of multicellular systems via multiscale modelling and omics data analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/368331.

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L'eterogeneità pervade i sistemi biologici e si manifesta in differenze strutturali e funzionali osservate sia tra diversi individui di uno stesso gruppo (es. organismi o patologie), sia fra gli elementi costituenti di un singolo individuo (es. cellule). Lo studio dell’eterogeneità dei sistemi biologici e, in particolare, di quelli multicellulari è fondamentale per la comprensione meccanicistica di fenomeni fisiologici e patologici complessi (es. il cancro), così come per la definizione di strategie prognostiche, diagnostiche e terapeutiche efficaci. Questo lavoro è focalizzato sullo svilupp
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Tellaroli, Paola. "Three topics in omics research." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3423912.

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The rather generic title of this Thesis is due to the fact that several aspects of biological phenomena have been investigated. Most of this work was addressed at the investigation of the limitations of one of the essential tools for analyzing gene expression data: cluster analysis. With several hundred of clustering methods in existence, there is clearly no shortage of clustering algorithms but, at the same time, satisfactory answers to some basic questions are still to come. In particular, we present a novel algorithm for the clustering of static data and a new strategy for the clustering of
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Ayati, Marzieh. "Algorithms to Integrate Omics Data for Personalized Medicine." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1527679638507616.

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Zuo, Yiming. "Differential Network Analysis based on Omic Data for Cancer Biomarker Discovery." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78217.

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Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often le
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Lu, Yingzhou. "Multi-omics Data Integration for Identifying Disease Specific Biological Pathways." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83467.

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Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single om
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Elhezzani, Najla Saad R. "New statistical methodologies for improved analysis of genomic and omic data." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/new-statistical-methodologies-for-improved-analysis-of-genomic-and-omic-data(eb8d95f4-e926-4c54-984f-94d86306525a).html.

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We develop statistical tools for analyzing different types of phenotypic data in genome-wide settings. When the phenotype of interest is a binary case-control status, most genome-wide association studies (GWASs) use randomly selected samples from the population (hereafter bases) as the control set. This approach is successful when the trait of interest is very rare; otherwise, a loss in the statistical power to detect disease-associated variants is expected. To address this, we propose a joint analysis of the three types of samples; cases, bases and controls. This is done by modeling the bases
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Hafez, Khafaga Ahmed Ibrahem 1987. "Bioinformatics approaches for integration and analysis of fungal omics data oriented to knowledge discovery and diagnosis." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671160.

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Aquesta tesi presenta una sèrie de recursos bioinformàtics desenvolupats per a donar suport en l'anàlisi de dades de NGS i altres òmics en el camp d'estudi i diagnòstic d'infeccions fúngiques. Hem dissenyat tècniques de computació per identificar nous biomarcadors i determinar potencial trets de resistència, pronosticant les característiques de les seqüències d'ADN/ARN, i planejant estratègies optimitzades de seqüenciació per als estudis de hoste-patogen transcriptomes (Dual RNA-seq). Hem dissenyat i desenvolupat tambe una solució bioinformàtica composta per un component de costat de servidor
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Li, Yichao. "Algorithmic Methods for Multi-Omics Biomarker Discovery." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1541609328071533.

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Dupuis, Louise. "Causal network analysis of single cell multi-omics data applied to a cellular therapy against multiple sclerosis." Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS628.pdf.

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Les travaux de cette thèse se situent à l'intersection de la découverte causale, de l'analyse de données single-cell multi-omiques et de l'immunologie, en particulier de la sclérose en plaques. Son objectif principal est d'adapter et d'étendre les algorithmes de découverte causale tels que MIIC (Multivariate Information-base Inductive Causation) qui reconstruisent les réseaux causaux à partir de données d'observation pour les adapter aux données single-cell multi-omiques, et de les appliquer à l'étude des mécanismes sous-jacents à une thérapie potentielle contre la sclérose en plaques. Cette r
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Livres sur le sujet "Analisi dati omici"

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Integrating Omics Data. Cambridge University Press, 2015.

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Tseng, George, Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.

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Tseng, George C., Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.

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Suman, Shankar, Shivam Priya, and Akanksha Nigam, eds. Breast Cancer: Current Trends in Molecular Research. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97816810895221120101.

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Breast cancer is one of the most common cancer types worldwide, and is a leading cause of cancer related deaths in women. In this book, medical experts review our current understanding of the molecular biology and characteristics of breast cancer. The topics covered in this book provide comprehensive knowledge of mechanisms underlying breast carcinogenesis, and are intended for a wide audience including scientists, teachers, and students. 11 chapters present information about several topics on breast cancer, including the role of cell growth and proliferation pathways, androgen and cytokine si
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Chapitres de livres sur le sujet "Analisi dati omici"

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Ghantasala, Saicharan, Shabarni Gupta, Vimala Ashok Mani, et al. "Omics: Data Processing and Analysis." In Biomarker Discovery in the Developing World: Dissecting the Pipeline for Meeting the Challenges. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2837-0_3.

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Österlund, Tobias, Marija Cvijovic, and Erik Kristiansson. "Integrative Analysis of Omics Data." In Systems Biology. Wiley-VCH Verlag GmbH & Co. KGaA, 2017. http://dx.doi.org/10.1002/9783527696130.ch1.

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Yu, Xiang-Tian, and Tao Zeng. "Integrative Analysis of Omics Big Data." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8_7.

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Dunkler, Daniela, Fátima Sánchez-Cabo, and Georg Heinze. "Statistical Analysis Principles for Omics Data." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_5.

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Han, Maozhen, Na Zhang, Zhangjie Peng, et al. "Multi-Omics Data Analysis for Inflammation Disease Research: Correlation Analysis, Causal Analysis and Network Analysis." In Methodologies of Multi-Omics Data Integration and Data Mining. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1_6.

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Lü, Jinhu, and Pei Wang. "Data-Driven Statistical Approaches for Omics Data Analysis." In Modeling and Analysis of Bio-molecular Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9144-0_9.

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Chen, Yi-An, Lokesh P. Tripathi, and Kenji Mizuguchi. "Data Warehousing with TargetMine for Omics Data Analysis." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9442-7_3.

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Awais Attique, Syed, Qurat Ul Ain, Mourad Ben Said, and Muhammad Sohail Sajid. "Bioinformatics and Data Analysis in Veterinary Parasitology." In Omics Approaches in Veterinary Parasitology. CRC Press, 2024. http://dx.doi.org/10.1201/9781032651071-9.

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Zhou, Guangyan, Shuzhao Li, and Jianguo Xia. "Network-Based Approaches for Multi-omics Integration." In Computational Methods and Data Analysis for Metabolomics. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3_23.

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Mühlberger, Irmgard, Julia Wilflingseder, Andreas Bernthaler, Raul Fechete, Arno Lukas, and Paul Perco. "Computational Analysis Workflows for Omics Data Interpretation." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_17.

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Actes de conférences sur le sujet "Analisi dati omici"

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Sibilio, Pasquale, Federica Conte, and Paola Paci. "Beyond the network-based multi-omics data integration in COPD: a pathway-centric analysis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822251.

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Bartling, Craig, Angela Minard-Smith, Kate H. Kucharzyk, Jennifer Busch-Harris, and Larry Mullins. "Interpreting Omic Data for Microbially Influenced Corrosion: Lessons from a Case Study Involving a Seawater Injection System." In CORROSION 2017. NACE International, 2017. https://doi.org/10.5006/c2017-09445.

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Abstract Microbiologically influenced corrosion (MIC) is a significant source of pitting corrosion affecting oil and gas pipelines, wells, and a variety of surface facilities. Understanding of MIC is greatly enhanced through DNA and protein sequencing technologies. This paper highlights the need to understand the methods used to generate the data, the data quality, and the limitations associated with data interpretation through a case study involving the metagenomics and proteomic analysis of pig envelope debris and seawater samples from various locations within a seawater injection system sus
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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.

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Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictio
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Kovatch, Patricia, Anthony Costa, Zachary Giles, Eugene Fluder, Hyung Min Cho, and Svetlana Mazurkova. "Big omics data experience." In SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis. ACM, 2015. http://dx.doi.org/10.1145/2807591.2807595.

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Klabukov, Il'ya. "ELEMENTS FOR SYSTEMS MEDICINE OF CHOLANGIOPATHIES." In XIV International Scientific Conference "System Analysis in Medicine". Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2020. http://dx.doi.org/10.12737/conferencearticle_5fe01d9b506245.44352217.

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The approach to system analysis of bile duct dysfunctions based on analysis of multi-omics data of cholangiocytes is considered. There is suggested that changes in intercellular interactions in tissues of the bile duct cause phenotypic manifestations of the cholangiopathies in the changes in cholangiocyte regulation, which can be evaluated by analysis of changes in the molecular composition of the bile.
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Dimitrakopoulos, Georgios Ν., Konstantinos Lazaros, Aristidis G. Vrahatis, et al. "A Machine Learning approach combining omics data for Alzheimer’s Disease analysis." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.342d.

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Alzheimer’s disease (AD) is a complex neurological disorder whose underlying mechanisms remain elusive to this day. Molecular biology methodologies, especially techniques like single-cell RNA sequencing (scRNA-seq), offer unparalleled granularity in deciphering the disease’s cellular intricacies. However, despite the potential of scRNA-seq, comprehensive machine-learning analyses are yet to be fully harnessed. Emphasizing the multi-omics machine-learning-based approaches, which integrate diverse single-cell omics datasets, could highlight novel therapeutic targets and deepen our understanding
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Sunghoon Choi, Soo-yeon Park, Hoejin Kim, Oran Kwon, and Taesung Park. "Analysis for doubly repeated omics data from crossover design." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822782.

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Xing, Wei, Jon Smith, Mike Gavrielides, Steve Hindmarsh, Adam Huffman, and Hai H. Wang. "Nautilus: A Precision-Guided Open Data Architecture for Big Omics Data Analysis." In 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2019. http://dx.doi.org/10.1109/icaibd.2019.8836977.

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Ma, Yingning. "Cluster analysis for cancer omics data using Neural Network with data augmentation." In SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning. ACM, 2022. http://dx.doi.org/10.1145/3556384.3556388.

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Jain, Yashita, and Shanshan Ding. "Integrative Sufficient Dimension Reduction Methods for Multi-Omics Data Analysis." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2017. http://dx.doi.org/10.1145/3107411.3108225.

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Rapports d'organisations sur le sujet "Analisi dati omici"

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Huang, Lei, Meng Song, Hui Shen, et al. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advan
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Wrinn, Michael. Platform for efficient large-scale storage and analysis of multi-omics data in plant and microbial systems. Final Technical Report. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1659436.

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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD
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