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Статті в журналах з теми "Multi-omics Integration"
Sathyanarayanan, Anita, Rohit Gupta, Erik W. Thompson, Dale R. Nyholt, Denis C. Bauer, and Shivashankar H. Nagaraj. "A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping." Briefings in Bioinformatics 21, no. 6 (November 27, 2019): 1920–36. http://dx.doi.org/10.1093/bib/bbz121.
Повний текст джерелаLi, Chuan-Xing, Craig E. Wheelock, C. Magnus Sköld, and Åsa M. Wheelock. "Integration of multi-omics datasets enables molecular classification of COPD." European Respiratory Journal 51, no. 5 (March 15, 2018): 1701930. http://dx.doi.org/10.1183/13993003.01930-2017.
Повний текст джерелаWu, Cen, Fei Zhou, Jie Ren, Xiaoxi Li, Yu Jiang, and Shuangge Ma. "A Selective Review of Multi-Level Omics Data Integration Using Variable Selection." High-Throughput 8, no. 1 (January 18, 2019): 4. http://dx.doi.org/10.3390/ht8010004.
Повний текст джерелаBodein, Antoine, Marie-Pier Scott-Boyer, Olivier Perin, Kim-Anh Lê Cao, and Arnaud Droit. "Interpretation of network-based integration from multi-omics longitudinal data." Nucleic Acids Research 50, no. 5 (December 9, 2021): e27-e27. http://dx.doi.org/10.1093/nar/gkab1200.
Повний текст джерелаWieder, Cecilia, Juliette Cooke, Clement Frainay, Nathalie Poupin, Russell Bowler, Fabien Jourdan, Katerina J. Kechris, Rachel PJ Lai, and Timothy Ebbels. "PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration." PLOS Computational Biology 20, no. 3 (March 25, 2024): e1011814. http://dx.doi.org/10.1371/journal.pcbi.1011814.
Повний текст джерелаColomé-Tatché, M., and F. J. Theis. "Statistical single cell multi-omics integration." Current Opinion in Systems Biology 7 (February 2018): 54–59. http://dx.doi.org/10.1016/j.coisb.2018.01.003.
Повний текст джерелаSubramanian, Indhupriya, Srikant Verma, Shiva Kumar, Abhay Jere, and Krishanpal Anamika. "Multi-omics Data Integration, Interpretation, and Its Application." Bioinformatics and Biology Insights 14 (January 2020): 117793221989905. http://dx.doi.org/10.1177/1177932219899051.
Повний текст джерелаHaidar, Siwar, Julia Hooker, Simon Lackey, Mohamad Elian, Nathalie Puchacz, Krzysztof Szczyglowski, Frédéric Marsolais, Ashkan Golshani, Elroy R. Cober, and Bahram Samanfar. "Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review." Plants 13, no. 19 (September 28, 2024): 2714. http://dx.doi.org/10.3390/plants13192714.
Повний текст джерелаFiocchi, Claudio. "Omics and Multi-Omics in IBD: No Integration, No Breakthroughs." International Journal of Molecular Sciences 24, no. 19 (October 5, 2023): 14912. http://dx.doi.org/10.3390/ijms241914912.
Повний текст джерелаPinu, Farhana R., David J. Beale, Amy M. Paten, Konstantinos Kouremenos, Sanjay Swarup, Horst J. Schirra, and David Wishart. "Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community." Metabolites 9, no. 4 (April 18, 2019): 76. http://dx.doi.org/10.3390/metabo9040076.
Повний текст джерелаДисертації з теми "Multi-omics Integration"
Sathyanarayanan, Anita. "Integration of multi-omics data in cancer." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/225924/1/Anita_Sathyanarayanan_Thesis.pdf.
Повний текст джерелаZandonà, Alessandro. "Predictive networks for multi meta-omics data integration." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/367893.
Повний текст джерелаZandonà, Alessandro. "Predictive networks for multi meta-omics data integration." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2547/1/zandona2017_phdthesis.pdf.
Повний текст джерелаPATRIZI, SARA. "Multi-omics approaches to complex diseases in children." Doctoral thesis, Università degli Studi di Trieste, 2022. http://hdl.handle.net/11368/3015193.
Повний текст джерела“-Omic” technologies can detect the entirety of the molecules in the biological sample of interest, in a non-targeted and non-biased fashion. The integration of multiple types of omics data, known as “multi-omics” or “vertical omics”, can provide a better understanding of how the cause of disease leads to its functional consequences, which is particularly valuable in the study of complex diseases, that are caused by the interaction of multiple genetic and regulatory factors with contributions from the environment. In the present work appropriate multi-omics approaches are applied to two complex conditions that usually first manifest in childhood, have rising incidence and gaps in the knowledge of their molecular pathology, specifically Congenital Lung Malformations and Coeliac Disease. The aims are, respectively, to verify if cancer-associated genomic variants or DNA methylation features exist in the malformed lung tissue and to find common alterations in the methylome and the transcriptome of small intestine epithelial cells of children with CD. The methods used in the Congenital Lung Malformations project are Whole Genome Methylation microarrays and Whole Genome Sequencing, and for the Coeliac Disease the whole genome methylation microarrays and mRNA sequencing. Differentially methylated regions in possibly cancer-related genes were found in each one of the 20 lung malformation samples included. Moreover, 5 malformed samples had at least one somatic missense single nucleotide variant in genes known as lung cancer drivers, and 5 malformed samples had a total of 2 deletions of lung cancer driver tumour suppressor and 10 amplifications of lung cancer driver oncogenes. The data showed that congenital lung malformations can have premalignant genetic and epigenetic features, that are impossible to predict with clinical information only. In the second project, Principal Component Analysis of the whole genome methylation data showed that CD patients divide into two clusters, one of which overlaps with controls. 174 genes were differentially methylated compared to the controls in both clusters. Principal Component Analysis of gene expression data (mRNA-Seq) showed a distribution that is similar to the methylation data, and 442 genes were differentially expressed in both clusters. Six genes, mainly related to interferon response and antigen processing and presentation, were differentially expressed and methylated in both clusters. These results show that the intestinal epithelial cells of individuals with CD are highly variable from a molecular point of view, but they share some fundamental differences that make them able to respond to interferons, process, and present antigens more efficiently than controls. Despite the limitations of the present studies, they have shown that targeted multi-omics approaches can be set up to answer the relevant disease-specific questions by investigating many cellular functions at once, often generating new hypotheses and making unexpected discoveries in the process.
Serra, Angela. "Multi-view learning and data integration for omics data." Doctoral thesis, Universita degli studi di Salerno, 2017. http://hdl.handle.net/10556/2580.
Повний текст джерелаIn recent years, the advancement of high-throughput technologies, combined with the constant decrease of the data-storage costs, has led to the production of large amounts of data from different experiments that characterise the same entities of interest. This information may relate to specific aspects of a phenotypic entity (e.g. Gene expression), or can include the comprehensive and parallel measurement of multiple molecular events (e.g., DNA modifications, RNA transcription and protein translation) in the same samples. Exploiting such complex and rich data is needed in the frame of systems biology for building global models able to explain complex phenotypes. For example, theuseofgenome-widedataincancerresearch, fortheidentificationof groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drugdevelopment.ÂăMoreover, the integration of gene expression data regarding cell treatment by drugs, and information regarding chemical structure of the drugs allowed scientist to perform more accurate drug repositioning tasks. Unfortunately, there is a big gap between the amount of information and the knowledge in which it is translated. Moreover, there is a huge need of computational methods able to integrate and analyse data to fill this gap. Current researches in this area are following two different integrative methods: one uses the complementary information of different measurements for the 7 i i “Template” — 2017/6/9 — 16:42 — page 8 — #8 i i i i i i study of complex phenotypes on the same samples (multi-view learning); the other tends to infer knowledge about the phenotype of interest by integrating and comparing the experiments relating to it with respect to those of different phenotypes already known through comparative methods (meta-analysis). Meta-analysis can be thought as an integrative study of previous results, usually performed aggregating the summary statistics from different studies. Due to its nature, meta-analysis usually involves homogeneous data. On the other hand, multi-view learning is a more flexible approach that considers the fusion of different data sources to get more stable and reliable estimates. Based on the type of data and the stage of integration, new methodologies have been developed spanning a landscape of techniques comprising graph theory, machine learning and statistics. Depending on the nature of the data and on the statistical problem to address, the integration of heterogeneous data can be performed at different levels: early, intermediate and late. Early integration consists in concatenating data from different views in a single feature space. Intermediate integration consists in transforming all the data sources in a common feature space before combining them. In the late integration methodologies, each view is analysed separately and the results are then combined. The purpose of this thesis is twofold: the former objective is the definition of a data integration methodology for patient sub-typing (MVDA) and the latter is the development of a tool for phenotypic characterisation of nanomaterials (INSIdEnano). In this PhD thesis, I present the methodologies and the results of my research. MVDA is a multi-view methodology that aims to discover new statistically relevant patient sub-classes. Identify patient subtypes of a specific diseases is a challenging task especially in the early diagnosis. This is a crucial point for the treatment, because not allthe patients affected bythe same diseasewill have the same prognosis or need the same drug treatment. This problem is usually solved by using transcriptomic data to identify groups of patients that share the same gene patterns. The main idea underlying this research work is that to combine more omics data for the same patients to obtain a better characterisation of their disease profile. The proposed methodology is a late integration approach i i “Template” — 2017/6/9 — 16:42 — page 9 — #9 i i i i i i based on clustering. It works by evaluating the patient clusters in each single view and then combining the clustering results of all the views by factorising the membership matrices in a late integration manner. The effectiveness and the performance of our method was evaluated on six multi-view cancer datasets related to breast cancer, glioblastoma, prostate and ovarian cancer. The omics data used for the experiment are gene and miRNA expression, RNASeq and miRNASeq, Protein Expression and Copy Number Variation. In all the cases, patient sub-classes with statistical significance were found, identifying novel sub-groups previously not emphasised in literature. The experiments were also conducted by using prior information, as a new view in the integration process, to obtain higher accuracy in patients’ classification. The method outperformed the single view clustering on all the datasets; moreover, it performs better when compared with other multi-view clustering algorithms and, unlike other existing methods, it can quantify the contribution of single views in the results. The method has also shown to be stable when perturbation is applied to the datasets by removing one patient at a time and evaluating the normalized mutual information between all the resulting clusterings. These observations suggest that integration of prior information with genomic features in sub-typing analysis is an effective strategy in identifying disease subgroups. INSIdE nano (Integrated Network of Systems bIology Effects of nanomaterials) is a novel tool for the systematic contextualisation of the effects of engineered nanomaterials (ENMs) in the biomedical context. In the recent years, omics technologies have been increasingly used to thoroughly characterise the ENMs molecular mode of action. It is possible to contextualise the molecular effects of different types of perturbations by comparing their patterns of alterations. While this approach has been successfully used for drug repositioning, it is still missing to date a comprehensive contextualisation of the ENM mode of action. The idea behind the tool is to use analytical strategies to contextualise or position the ENM with the respect to relevant phenotypes that have been studied in literature, (such as diseases, drug treatments, and other chemical exposures) by comparing their patterns of molecular alteration. This could greatly increase the knowledge on the ENM molecular effects and in turn i i “Template” — 2017/6/9 — 16:42 — page 10 — #10 i i i i i i contribute to the definition of relevant pathways of toxicity as well as help in predicting the potential involvement of ENM in pathogenetic events or in novel therapeutic strategies. The main hypothesis is that suggestive patterns of similarity between sets of phenotypes could be an indication of a biological association to be further tested in toxicological or therapeutic frames. Based on the expression signature, associated to each phenotype, the strength of similarity between each pair of perturbations has been evaluated and used to build a large network of phenotypes. To ensure the usability of INSIdE nano, a robust and scalable computational infrastructure has been developed, to scan this large phenotypic network and a web-based effective graphic user interface has been built. Particularly, INSIdE nano was scanned to search for clique sub-networks, quadruplet structures of heterogeneous nodes (a disease, a drug, a chemical and a nanomaterial) completely interconnected by strong patterns of similarity (or anti-similarity). The predictions have been evaluated for a set of known associations between diseases and drugs, based on drug indications in clinical practice, and between diseases and chemical, based on literature-based causal exposure evidence, and focused on the possible involvement of nanomaterials in the most robust cliques. The evaluation of INSIdE nano confirmed that it highlights known disease-drug and disease-chemical connections. Moreover, disease similarities agree with the information based on their clinical features, as well as drugs and chemicals, mirroring their resemblance based on the chemical structure. Altogether, the results suggest that INSIdE nano can also be successfully used to contextualise the molecular effects of ENMs and infer their connections to other better studied phenotypes, speeding up their safety assessment as well as opening new perspectives concerning their usefulness in biomedicine. [edited by author]
L’avanzamento tecnologico delle tecnologie high-throughput, combinato con il costante decremento dei costi di memorizzazione, ha portato alla produzione di grandi quantit`a di dati provenienti da diversi esperimenti che caratterizzano le stesse entit`a di interesse. Queste informazioni possono essere relative a specifici aspetti fenotipici (per esempio l’espressione genica), o possono includere misure globali e parallele di diversi aspetti molecolari (per esempio modifiche del DNA, trascrizione dell’RNA e traduzione delle proteine) negli stessi campioni. Analizzare tali dati complessi `e utile nel campo della systems biology per costruire modelli capaci di spiegare fenotipi complessi. Ad esempio, l’uso di dati genome-wide nella ricerca legata al cancro, per l’identificazione di gruppi di pazienti con caratteristiche molecolari simili, `e diventato un approccio standard per una prognosi precoce piu` accurata e per l’identificazione di terapie specifiche. Inoltre, l’integrazione di dati di espressione genica riguardanti il trattamento di cellule tramite farmaci ha permesso agli scienziati di ottenere accuratezze elevate per il drug repositioning. Purtroppo, esiste un grosso divario tra i dati prodotti, in seguito ai numerosi esperimenti, e l’informazione in cui essi sono tradotti. Quindi la comunit`a scientifica ha una forte necessit`a di metodi computazionali per poter integrare e analizzate tali dati per riempire questo divario. La ricerca nel campo delle analisi multi-view, segue due diversi metodi di analisi integrative: uno usa le informazioni complementari di diverse misure per studiare fenotipi complessi su diversi campioni (multi-view learning); l’altro tende ad inferire conoscenza sul fenotipo di interesse di una entit`a confrontando gli esperimenti ad essi relativi con quelli di altre entit`a fenotipiche gi`a note in letteratura (meta-analisi). La meta-analisi pu`o essere pensata come uno studio comparativo dei risultati identificati in un particolare esperimento, rispetto a quelli di studi precedenti. A causa della sua natura, la meta-analisi solitamente coinvolge dati omogenei. D’altra parte, il multi-view learning `e un approccio piu` flessibile che considera la fusione di diverse sorgenti di dati per ottenere stime piu` stabili e affidabili. In base al tipo di dati e al livello di integrazione, nuove metodologie sono state sviluppate a partire da tecniche basate sulla teoria dei grafi, machine learning e statistica. In base alla natura dei dati e al problema statistico da risolvere, l’integrazione di dati eterogenei pu`o essere effettuata a diversi livelli: early, intermediate e late integration. Le tecniche di early integration consistono nella concatenazione dei dati delle diverse viste in un unico spazio delle feature. Le tecniche di intermediate integration consistono nella trasformazione di tutte le sorgenti dati in un unico spazio comune prima di combinarle. Nelle tecniche di late integration, ogni vista `e analizzata separatamente e i risultati sono poi combinati. Lo scopo di questa tesi `e duplice: il primo obbiettivo `e la definizione di una metodologia di integrazione dati per la sotto-tipizzazione dei pazienti (MVDA) e il secondo `e lo sviluppo di un tool per la caratterizzazione fenotipica dei nanomateriali (INSIdEnano). In questa tesi di dottorato presento le metodologie e i risultati della mia ricerca. MVDA `e una tecnica multi-view con lo scopo di scoprire nuove sotto tipologie di pazienti statisticamente rilevanti. Identificare sottotipi di pazienti per una malattia specifica `e un obbiettivo con alto rilievo nella pratica clinica, soprattutto per la diagnosi precoce delle malattie. Questo problema `e generalmente risolto usando dati di trascrittomica per identificare i gruppi di pazienti che condividono gli stessi pattern di alterazione genica. L’idea principale alla base di questo lavoro di ricerca `e quello di combinare piu` tipologie di dati omici per gli stessi pazienti per ottenere una migliore caratterizzazione del loro profilo. La metodologia proposta `e un approccio di tipo late integration basato sul clustering. Per ogni vista viene effettuato il clustering dei pazienti rappresentato sotto forma di matrici di membership. I risultati di tutte le viste vengono poi combinati tramite una tecnica di fattorizzazione di matrici per ottenere i metacluster finali multi-view. La fattibilit`a e le performance del nostro metodo sono stati valutati su sei dataset multi-view relativi al tumore al seno, glioblastoma, cancro alla prostata e alle ovarie. I dati omici usati per gli esperimenti sono relativi alla espressione dei geni, espressione dei mirna, RNASeq, miRNASeq, espressione delle proteine e della Copy Number Variation. In tutti i dataset sono state identificate sotto-tipologie di pazienti con rilevanza statistica, identificando nuovi sottogruppi precedentemente non noti in letteratura. Ulteriori esperimenti sono stati condotti utilizzando la conoscenza a priori relativa alle macro classi dei pazienti. Tale informazione `e stata considerata come una ulteriore vista nel processo di integrazione per ottenere una accuratezza piu` elevata nella classificazione dei pazienti. Il metodo proposto ha performance migliori degli algoritmi di clustering clussici su tutti i dataset. MVDA ha ottenuto risultati migliori in confronto a altri algoritmi di integrazione di tipo ealry e intermediate integration. Inoltre il metodo `e in grado di calcolare il contributo di ogni singola vista al risultato finale. I risultati mostrano, anche, che il metodo `e stabile in caso di perturbazioni del dataset effettuate rimuovendo un paziente alla volta (leave-one-out). Queste osservazioni suggeriscono che l’integrazione di informazioni a priori e feature genomiche, da utilizzare congiuntamente durante l’analisi, `e una strategia vincente nell’identificazione di sotto-tipologie di malattie. INSIdE nano (Integrated Network of Systems bIology Effects of nanomaterials) `e un tool innovativo per la contestualizzazione sistematica degli effetti delle nanoparticelle (ENMs) in contesti biomedici. Negli ultimi anni, le tecnologie omiche sono state ampiamente applicate per caratterizzare i nanomateriali a livello molecolare. E’ possibile contestualizzare l’effetto a livello molecolare di diversi tipi di perturbazioni confrontando i loro pattern di alterazione genica. Mentre tale approccio `e stato applicato con successo nel campo del drug repositioning, una contestualizzazione estensiva dell’effetto dei nanomateriali sulle cellule `e attualmente mancante. L’idea alla base del tool `e quello di usare strategie comparative di analisi per contestualizzare o posizionare i nanomateriali in confronto a fenotipi rilevanti che sono stati studiati in letteratura (come ad esempio malattie dell’uomo, trattamenti farmacologici o esposizioni a sostanze chimiche) confrontando i loro pattern di alterazione molecolare. Questo potrebbe incrementare la conoscenza dell’effetto molecolare dei nanomateriali e contribuire alla definizione di nuovi pathway tossicologici oppure identificare eventuali coinvolgimenti dei nanomateriali in eventi patologici o in nuove strategie terapeutiche. L’ipotesi alla base `e che l’identificazione di pattern di similarit`a tra insiemi di fenotipi potrebbe essere una indicazione di una associazione biologica che deve essere successivamente testata in ambito tossicologico o terapeutico. Basandosi sulla firma di espressione genica, associata ad ogni fenotipo, la similarit`a tra ogni coppia di perturbazioni `e stata valuta e usata per costruire una grande network di interazione tra fenotipi. Per assicurare l’utilizzo di INSIdE nano, `e stata sviluppata una infrastruttura computazionale robusta e scalabile, allo scopo di analizzare tale network. Inoltre `e stato realizzato un sito web che permettesse agli utenti di interrogare e visualizzare la network in modo semplice ed efficiente. In particolare, INSIdE nano `e stato analizzato cercando tutte le possibili clique di quattro elementi eterogenei (un nanomateriale, un farmaco, una malattia e una sostanza chimica). Una clique `e una sotto network completamente connessa, dove ogni elemento `e collegato con tutti gli altri. Di tutte le clique, sono state considerate come significative solo quelle per le quali le associazioni tra farmaco e malattia e farmaco e sostanze chimiche sono note. Le connessioni note tra farmaci e malattie si basano sul fatto che il farmaco `e prescritto per curare tale malattia. Le connessioni note tra malattia e sostanze chimiche si basano su evidenze presenti in letteratura del fatto che tali sostanze causano la malattia. Il focus `e stato posto sul possibile coinvolgimento dei nanomateriali con le malattie presenti in tali clique. La valutazione di INSIdE nano ha confermato che esso mette in evidenza connessioni note tra malattie e farmaci e tra malattie e sostanze chimiche. Inoltre la similarit`a tra le malattie calcolata in base ai geni `e conforme alle informazioni basate sulle loro informazioni cliniche. Allo stesso modo le similarit`a tra farmaci e sostanze chimiche rispecchiano le loro similarit`a basate sulla struttura chimica. Nell’insieme, i risultati suggeriscono che INSIdE nano pu`o essere usato per contestualizzare l’effetto molecolare dei nanomateriali e inferirne le connessioni rispetto a fenotipi precedentemente studiati in letteratura. Questo metodo permette di velocizzare il processo di valutazione della loro tossicit`a e apre nuove prospettive per il loro utilizzo nella biomedicina. [a cura dell'autore]
XV n.s.
Lu, Yingzhou. "Multi-omics Data Integration for Identifying Disease Specific Biological Pathways." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83467.
Повний текст джерелаMaster of Science
Zampieri, Guido. "Prioritisation of candidate disease genes via multi-omics data integration." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421826.
Повний текст джерелаLa scoperta dei geni legati alle malattie nell'uomo è una sfida pressante in biologia molecolare, in vista del pieno raggiungimento della medicina di precisione. Le tecnologie di nuova generazione forniscono una quantità di informazioni biologiche senza precedenti, ma allo stesso tempo rivelano numeri enormi di geni malattia candidati e pongono nuove sfide a molteplici livelli di analisi. L'integrazione di dati multi-omici è attualmente la strategia principale per prioritizzare geni malattia candidati. In particolare, i metodi basati su kernel sono una potente risorsa per l'integrazione della conoscenza biologica, tuttavia il loro utilizzo è spesso precluso dalla loro limitata scalabilità. In questa tesi, proponiamo un nuovo metodo kernel scalabile per la prioritizzazione di geni, che applica un nuovo approccio di multiple kernel learning basato su una prospettiva semi-supervisionata e sull'ottimizzazione della distribuzione dei margini in problemi binari. Il nostro metodo è ottimizzato per fare fronte a condizioni fortemente sbilanciate in cui si disponga di pochi geni malattia noti e siano richieste predizioni su larga scala. Significativamente, è capace di gestire sia un gran numero di candidati sia un numero arbitrario di sorgenti di informazione. Attraverso la simulazione di casi studio reali, mostriamo che il nostro metodo supera in prestazioni un'ampia gamma di metodi allo stato dell'arte ed è dotato di migliore scalabilità rispetto a metodi kernel esistenti per dati genomici. Applichiamo il metodo proposto per studiare il potenziale ruolo per la predizione di geni malattia dei riarrangiamenti metabolici causati da perturbazioni genetiche. A questo scopo, utilizziamo modelli del metabolismo basati su vincoli per generare informazione sui geni a scala genomica, che viene analizzata tramite apprendimento automatico. Inoltre, compariamo modelli basati su vincoli ed il nostro metodo basato su kernel come strategie di integrazione alternative per dati omici come profili trascrizionali. Valutazioni sperimentali su vari cancri dimostrano come i riarrangiamenti metabolici ricostruiti in silico possano essere utili per prioritizzare i geni associati, nonostante l'accuratezza dipenda fortemente dalla tipologia di cancro. Malgrado queste fluttuazioni, le predizioni basate su modelli metabolici sono largamente complentari a quelle basate su espressione genica o annotazioni di pathway, evidenziando il potenziale di questo approccio per identificare nuovi geni implicati nel cancro.
Xiao, Hui. "Network-based approaches for multi-omic data integration." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289716.
Повний текст джерелаJagtap, Surabhi. "Multilayer Graph Embeddings for Omics Data Integration in Bioinformatics." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST014.
Повний текст джерелаBiological systems are composed of interacting bio-molecules at different molecular levels. With the advent of high-throughput technologies, omics data at their respective molecular level can be easily obtained. These huge, complex multi-omics data can be useful to provide insights into the flow of information at multiple levels, unraveling the mechanisms underlying the biological condition of interest. Integration of different omics data types is often expected to elucidate potential causative changes that lead to specific phenotypes, or targeted treatments. With the recent advances in network science, we choose to handle this integration issue by representing omics data through networks. In this thesis, we have developed three models, namely BraneExp, BraneNet, and BraneMF, for learning node embeddings from multilayer biological networks generated with omics data. We aim to tackle various challenging problems arising in multi-omics data integration, developing expressive and scalable methods capable of leveraging rich structural semantics of realworld networks
DI, NANNI NOEMI. "A network diffusion method for the integration of multi-omics data with applications in precision medicine." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1315930.
Повний текст джерелаКниги з теми "Multi-omics Integration"
Alkhateeb, Abedalrhman, and Luis Rueda, eds. Machine Learning Methods for Multi-Omics Data Integration. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-36502-7.
Повний текст джерелаNing, Kang, ed. Methodologies of Multi-Omics Data Integration and Data Mining. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1.
Повний текст джерелаAlkhateeb, Alkhateeb. Machine Learning Methods for Multi-Omics Data Integration. Springer International Publishing AG, 2023.
Знайти повний текст джерелаNing, Kang. Methodologies of Multi-Omics Data Integration and Data Mining: Techniques and Applications. Springer, 2023.
Знайти повний текст джерелаMethodologies of Multi-Omics Data Integration and Data Mining: Techniques and Applications. Springer, 2024.
Знайти повний текст джерелаXie, Shang-Qian, Jiang Libo, Lidan Sun, and Yuehua Cui, eds. The Development and Application of Multi-Omics Integration Approaches to Dissecting Complex Traits in Plants. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88976-135-7.
Повний текст джерелаIntegrative Multi-Omics in Biomedical Research. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-2583-9.
Повний текст джерелаЧастини книг з теми "Multi-omics Integration"
AlOmari, Hania, Abedalrhman Alkhateeb, and Bassam Hammo. "Multi-Omics Databases." In Machine Learning Methods for Multi-Omics Data Integration, 151–66. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_9.
Повний текст джерелаLê Cao, Kim-Anh, and Zoe Marie Welham. "Multi-omics and biological systems." In Multivariate Data Integration Using R, 3–10. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003026860-2.
Повний текст джерелаNing, Kang, and Yuxue Li. "Introduction to Multi-Omics." In Methodologies of Multi-Omics Data Integration and Data Mining, 1–10. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1_1.
Повний текст джерелаZhong, Chaofang, and Hong Bai. "TCM Related Multi-Omics Data Integration Techniques." In Traditional Chinese Medicine and Diseases, 25–45. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4771-1_3.
Повний текст джерелаZhou, Guangyan, Shuzhao Li, and Jianguo Xia. "Network-Based Approaches for Multi-omics Integration." In Computational Methods and Data Analysis for Metabolomics, 469–87. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3_23.
Повний текст джерелаLi, Yuxue, and Kang Ning. "Biomedical Applications: The Need for Multi-Omics." In Methodologies of Multi-Omics Data Integration and Data Mining, 13–31. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1_2.
Повний текст джерелаFriedel, Caroline C. "Computational Integration of HSV-1 Multi-omics Data." In Methods in Molecular Biology, 31–48. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2895-9_3.
Повний текст джерелаLim, Byeonghwi, Do-Young Kim, Young-Jun Seo, Ji-Yeong Lee, and Jun-Mo Kim. "Systems Biology and Integration of Multi-Omics Data." In Bioinformatics in Veterinary Science, 163–83. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7395-4_8.
Повний текст джерелаPoetsch, Ansgar, and Yuxue Li. "-Omics Technologies and Big Data." In Methodologies of Multi-Omics Data Integration and Data Mining, 33–54. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1_3.
Повний текст джерелаTang, Min, Yi Liu, and Xun Gong. "Multi-Omics Data Mining Techniques: Algorithms and Software." In Methodologies of Multi-Omics Data Integration and Data Mining, 55–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1_4.
Повний текст джерелаТези доповідей конференцій з теми "Multi-omics Integration"
Gao, Peipei, Ling Du, Sibo Qiao, and Nan Yin. "Uncertainty-induced Incomplete Multi-Omics Integration Network for Cancer Diagnosis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 4415–22. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822063.
Повний текст джерелаPeriyasamy, Madhavan. "AI-Driven Multi-Omics Integration for Enhanced Drug Discovery Pipelines." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI), 1553–58. IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894291.
Повний текст джерелаPang, Shanchen, Jiarui Wu, Wenhao Wu, Hengxiao Li, Ruiqian Wang, Yulin Zhang, and Shudong Wang. "scKADE: Single-Cell Multi-Omics Integration with Kolmogorov-Arnold Deep Embedding." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 633–38. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822086.
Повний текст джерелаSibilio, 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), 6107–12. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822251.
Повний текст джерелаMehrabian, Hatef, Sangeetha Mahadevan, Michael Sharpnack, Christina Moon, and Lauri Diehl. "Integration of spatial transcriptomics and immunofluorescence staining to enable colocalized multi-omics analysis in chronic liver disease." In Digital and Computational Pathology, edited by John E. Tomaszewski and Aaron D. Ward, 38. SPIE, 2025. https://doi.org/10.1117/12.3047319.
Повний текст джерелаKarthik, Akshay, and Michael Donovan. "A Novel Deep Learning-Based Multi-Model Ensemble Approach for the Prediction of Non-Small Cell Lung Cancer (NSCLC) Metastasis via Integration of Multi-omics Data." In 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA), 1–6. IEEE, 2024. https://doi.org/10.1109/ictiia61827.2024.10761308.
Повний текст джерелаMishra, Soumya Ranjan, Sachikanta Dash, Sasmita Padhy, Naween Kumar, and Yajnaseni Dash. "Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 1447–53. IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10828970.
Повний текст джерелаWang, Bo, Wei Liu, Jiawei Luo, Xiangtao Chen, and Chee Keong Kwoh. "SMMGCL: a novel multi-level graph contrastive learning framework for integrating spatial multi-omics data." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1213–18. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822097.
Повний текст джерелаNikshya, J. Ebens, M. Saravana Karthikeyan, Shalini Prasad, R. Santhana Krishnan, S. Balamurugan, and J. Relin Francis Raj. "A Machine Learning Framework for Integrating Multi-Omics Data for Early Leukemia Detection." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 1348–56. IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714596.
Повний текст джерелаDuan, Xin, Chuanxin Hu, Manyu Yun, and Haiyan Liu. "Integrating Hidden Features of ELM Auto-Encoder for Cancer Multi-Omics Data Clustering." In 2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 374–77. IEEE, 2024. https://doi.org/10.1109/icaice63571.2024.10864066.
Повний текст джерелаЗвіти організацій з теми "Multi-omics Integration"
Wheeler, Travis. Machine learning approaches for integrating multi-omics data to expand microbiome annotation. Office of Scientific and Technical Information (OSTI), April 2024. http://dx.doi.org/10.2172/2331432.
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