Relevant bibliographies by topics / Translational and applied bioinformatics

Academic literature on the topic 'Translational and applied bioinformatics'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Translational and applied bioinformatics"

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Shen, Bairong, Hong-Bin Shen, Tianhai Tian, Qiang Lü, and Guang Hu. "Translational Bioinformatics and Computational Systems Medicine." Computational and Mathematical Methods in Medicine 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/375641.

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Smaïl-Tabbone, Malika, and Bastien Rance. "Contributions from the 2018 Literature on Bioinformatics and Translational Informatics." Yearbook of Medical Informatics 28, no. 01 (August 2019): 190–93. http://dx.doi.org/10.1055/s-0039-1677945.

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Objectives: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.
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Preisig, Carol L. "A systematic approach to companion diagnostic development: A case study for omacetaxine (OM) for the treatment of chronic myelogenous leukemia (CML)." Journal of Clinical Oncology 30, no. 30_suppl (October 20, 2012): 100. http://dx.doi.org/10.1200/jco.2012.30.30_suppl.100.

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100 Background: In the age of genomically informed medicine, therapeutic development carries with it the imperative to employ genomics in patient selection. Physicians expect genomics-based methods to identify treatments likely to be effective, and identify anomalies likely to cause adverse response for a given patient. Companion diagnostics should support such rule-in and rule-out decisions. We demonstrate a systematic approach to companion diagnostics that leverages new methods in translational bioinformatics and clinical economics. CML therapies have been at the forefront of genomically informed medicine. Early TKI inhibitors targeting the BCR-ABL fusion protein are highly effective. With time, however, they induce resistance-creating mutations in many patients. Omacetixine, a translation inhibitor, was expected to help this CML patient subpopulation which has few therapeutic alternatives. We use this well-characterized drug and publicly available data to demonstrate a prospective approach to companion diagnostics. Methods: We used translational bioinformatics, incorporating pathway, cell line, and patient data to identify biologically plausible biomarkers from which alternative companion diagnostic paths were constructed. These alternatives were analyzed using a modified version of the MIT Stratified Medicine Model to assess the clinical economics of each path. Results: From a systematic look at the biology of the disease, the unique mechanism of action of OM and the clinical need, we identified 3 alternative companion diagnostics for OM. Economic analyses quantified the trade-offs of targeting different subpopulations for the indication, clarifying the impact of biomarker selection based on clinical need or biology. Other analyses have shown that eNPV can be halved or doubled based on strategy choice. Conclusions: Combining applied translational bioinformatics and stratified medicine economics provides an effective approach to companion diagnostic selection. This approach can reduce drug-development cost and clinical risk while providing physicians with better genomics-based methods for clinical decision-making.
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Katoh, Masuko, and Masaru Katoh. "Bioinformatics for Cancer Management in the Post-Genome Era." Technology in Cancer Research & Treatment 5, no. 2 (April 2006): 169–75. http://dx.doi.org/10.1177/153303460600500208.

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Human cancer is caused by multiple factors, such as genetic predisposition, chronic persistent inflammation, environmental factors, life style, and aging. Dysregulated proliferation, dysregulated adhesion, resistance to apoptosis, resistance to senescence, and resistance to anti-cancer drugs are features of cancer cells. Accumulation of multiple epigenetic changes and genetic alterations of cancer-associated genes during multi-stage carcinogenesis results in more malignant phenotypes. Post-genome science is characterized by omics data related to genome, transcriptome, proteome, metabolome, interactome, and epigenome as well as by high-throughput technology, such as whole-genome tiling oligonucleotide array, array CGH with 32,433 overlapping BAC clones, transcriptome microarray, mass spectrometry, tissue-based expression array, and cell-based transfection array. Benchtop oncology supplies Desktop oncology with large amounts of omics data produced by high-throughput technology. Desktop oncology establishes knowledge on cancer-related biomarkers, such as predisposition markers, diagnostic markers, prognostic markers, and therapeutic markers, by using bioinformatics and human intelligence of experts for data mining and text mining. Bedside oncology applies the knowledge established by Desktop oncology to determine therapeutics for cancer patients. Antibody drugs (Trastuzumab/Herceptin, Cetuximab/Erbitux, Bevacizumab/Avastin, et cetera), small molecule inhibitors for tyrosine kinases (Gefitinib/Iressa, Erlotinib/Tarceva, Imatinib/Gleevec, et cetera), conventional cytotoxic drugs, and anti-hormonal drugs are used for cancer chemotherapy. Biomarker monitoring contributes to therapeutic optional choice and drug dosage determination for cancer patients. Knowledge on biomarkers is feedforwarded from desktop to bedside in the translational research, and then biomarker monitoring is feedbacked from bedside to desktop in the reverse translational research. Desktop oncology is indispensable for cancer research in the post-genome era. Combination of genetic screening for cancer predisposition in the general population and precise selection of therapeutic options during cancer management could contribute to the realization of personalized prevention and to dramatically improve the prognosis of cancer patients in the future.
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Karollus, Alexander, Žiga Avsec, and Julien Gagneur. "Predicting mean ribosome load for 5’UTR of any length using deep learning." PLOS Computational Biology 17, no. 5 (May 10, 2021): e1008982. http://dx.doi.org/10.1371/journal.pcbi.1008982.

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The 5’ untranslated region plays a key role in regulating mRNA translation and consequently protein abundance. Therefore, accurate modeling of 5’UTR regulatory sequences shall provide insights into translational control mechanisms and help interpret genetic variants. Recently, a model was trained on a massively parallel reporter assay to predict mean ribosome load (MRL)—a proxy for translation rate—directly from 5’UTR sequence with a high degree of accuracy. However, this model is restricted to sequence lengths investigated in the reporter assay and therefore cannot be applied to the majority of human sequences without a substantial loss of information. Here, we introduced frame pooling, a novel neural network operation that enabled the development of an MRL prediction model for 5’UTRs of any length. Our model shows state-of-the-art performance on fixed length randomized sequences, while offering better generalization performance on longer sequences and on a variety of translation-related genome-wide datasets. Variant interpretation is demonstrated on a 5’UTR variant of the gene HBB associated with beta-thalassemia. Frame pooling could find applications in other bioinformatics predictive tasks. Moreover, our model, released open source, could help pinpoint pathogenic genetic variants.
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Miao, Chenyun, Yun Chen, Xiaojie Fang, Ying Zhao, Ruye Wang, and Qin Zhang. "Identification of the shared gene signatures and pathways between polycystic ovary syndrome and endometrial cancer: An omics data based combined approach." PLOS ONE 17, no. 7 (July 13, 2022): e0271380. http://dx.doi.org/10.1371/journal.pone.0271380.

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Objective Polycystic ovary syndrome (PCOS) is a common endocrine disorder with high incidence. Recently it has been implicated as a significant risk factor for endometrial cancer (EC). Our study aims to detect shared gene signatures and biological mechanism between PCOS and EC by bioinformatics analysis. Methods Bioinformatics analysis based on GEO database consisted of data integration, network construction and functional enrichment analysis was applied. In addition, the pharmacological methodology and molecular docking was also performed. Results Totally 10 hub common genes, MRPL16, MRPL22, MRPS11, RPL26L1, ESR1, JUN, UBE2I, MRPL17, RPL37A, GTF2H3, were considered as shared gene signatures for EC and PCOS. The GO and KEGG pathway analysis of these hub genes showed that “mitochondrial translational elongation”, “ribosomal subunit”, “structural constituent of ribosome” and “ribosome” were highly correlated. Besides, associated transcription factors (TFs) and miRNAs network were constructed. We identified candidate drug molecules including fenofibrate, cinnarizine, propanil, fenthion, clindamycin, chloramphenicol, demeclocycline, hydrochloride, azacitidine, chrysene and artenimol according to these hub genes. Molecular docking analysis verified a good binding interaction of fenofibrate against available targets (JUN, ESR1, UBE2I). Conclusion Gene signatures and regulatory biological pathways were identified through bioinformatics analysis. Moreover, the molecular mechanisms of these signatures were explored and potential drug molecules associated with PCOS and EC were screened out.
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Valenta, Annette L., Eta S. Berner, Suzanne A. Boren, Gloria J. Deckard, Christina Eldredge, Douglas B. Fridsma, Cynthia Gadd, et al. "AMIA Board White Paper: AMIA 2017 core competencies for applied health informatics education at the master’s degree level." Journal of the American Medical Informatics Association 25, no. 12 (October 26, 2018): 1657–68. http://dx.doi.org/10.1093/jamia/ocy132.

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Abstract This White Paper presents the foundational domains with examples of key aspects of competencies (knowledge, skills, and attitudes) that are intended for curriculum development and accreditation quality assessment for graduate (master’s level) education in applied health informatics. Through a deliberative process, the AMIA Accreditation Committee refined the work of a task force of the Health Informatics Accreditation Council, establishing 10 foundational domains with accompanying example statements of knowledge, skills, and attitudes that are components of competencies by which graduates from applied health informatics programs can be assessed for competence at the time of graduation. The AMIA Accreditation Committee developed the domains for application across all the subdisciplines represented by AMIA, ranging from translational bioinformatics to clinical and public health informatics, spanning the spectrum from molecular to population levels of health and biomedicine. This document will be periodically updated, as part of the responsibility of the AMIA Accreditation Committee, through continued study, education, and surveys of market trends.
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Klein, Joshua, Luis Carvalho, and Joseph Zaia. "Application of network smoothing to glycan LC-MS profiling." Bioinformatics 34, no. 20 (May 22, 2018): 3511–18. http://dx.doi.org/10.1093/bioinformatics/bty397.

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Abstract Motivation Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub-structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity. Results We developed an algorithm for assigning glycan compositions from LC-MS data by exploring biosynthetic network relationships among glycans. Our algorithm optimizes a set of likelihood scoring functions based on glycan chemical properties but uses network Laplacian regularization and optionally prior information about expected glycan families to smooth the likelihood and thus achieve a consistent and more representative solution. Our method was able to identify as many, or more glycan compositions compared to previous approaches, and demonstrated greater sensitivity with regularization. Our network definition was tailored to N-glycans but the method may be applied to glycomics data from other glycan families like O-glycans or heparan sulfate where the relationships between compositions can be expressed as a graph. Availability and implementation Built Executable http://www.bumc.bu.edu/msr/glycresoft/ and Source Code: https://github.com/BostonUniversityCBMS/glycresoft. Supplementary information Supplementary data are available at Bioinformatics online.
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Sims, A. H. "Bioinformatics and breast cancer: what can high-throughput genomic approaches actually tell us?" Journal of Clinical Pathology 62, no. 10 (January 27, 2009): 879–85. http://dx.doi.org/10.1136/jcp.2008.060376.

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High-throughput genomic technology has rapidly become a major tool for the study of breast cancer. Gene expression profiling has been applied to many areas of research from basic science to translational studies, with the potential to identify new targets for treatment, mechanisms of resistance and to improve on current tools for the analysis of prognosis. However, the sheer scale of the data generated along with the number of different protocols, platforms and analysis methods can make these studies difficult for clinicians to comprehend. Similarly, computational scientists and statisticians that may be called upon to analyse the data generated are often unaware of the processes involved in sample collection or the relevance and impact of genetics and pathological characteristics. There is a pressing need for better understanding of the challenges and limitations of microarray approaches, both in experimental design and data analysis. Holistic, whole-genome approaches are still relatively new and critics have been quick to highlight non-overlapping results from groups testing similar hypotheses. However, it is often subtle differences in the experimental design and technology that underpin the variation between these studies. Rather than indicating that the data are meaningless, this suggests that many findings are real, but highly context dependent. This review explores both the current state and potential of bioinformatics to bring meaning to high-throughput genomic approaches in the understanding of breast cancer.
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Fan, Jason, Xuan Cindy Li, Mark Crovella, and Mark D. M. Leiserson. "Matrix (factorization) reloaded: flexible methods for imputing genetic interactions with cross-species and side information." Bioinformatics 36, Supplement_2 (December 2020): i866—i874. http://dx.doi.org/10.1093/bioinformatics/btaa818.

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Abstract Motivation Mapping genetic interactions (GIs) can reveal important insights into cellular function and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker’s yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data are scarce. Results In this article, we address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein–protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker’s and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data-scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost. Availability Implementations of models and experiments are available at: https://github.com/lrgr/EMF. Supplementary information Supplementary data are available at Bioinformatics online.
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Dissertations / Theses on the topic "Translational and applied bioinformatics"

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Siangphoe, Umaporn. "META-ANALYSIS OF GENE EXPRESSION STUDIES." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/4040.

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Combining effect sizes from individual studies using random-effects models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistency of sample qualities and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. We proposed two new methods for random effects estimation and measurements for model variation and strength of the study heterogeneity. We then developed a statistical technique to test for significance of random effects and identify heterogeneous genes. We also proposed another meta-analytic approach that incorporates informative weights in the random effects meta-analysis models. We compared the proposed methods with the standard and existing meta-analytic techniques in the classical and Bayesian frameworks. We demonstrate our results through a series of simulations and application in gene expression neurodegenerative diseases.
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Podowski, Raf M. "Applied bioinformatics for gene characterization /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-818-5/.

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Vyas, Hiten. "Information management applied to bioinformatics." Thesis, Loughborough University, 2006. https://dspace.lboro.ac.uk/2134/12906.

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Bioinformatics, the discipline concerned with biological information management is essential in the post-genome era, where the complexity of data processing allows for contemporaneous multi level research including that at the genome level, transcriptome level, proteome level, the metabolome level, and the integration of these -omic studies towards gaining an understanding of biology at the systems level. This research is also having a major impact on disease research and drug discovery, particularly through pharmacogenomics studies. In this study innovative resources have been generated via the use of two case studies. One was of the Research & Development Genetics (RDG) department at AstraZeneca, Alderley Park and the other was of the Pharmacogenomics Group at the Sanger Institute in Cambridge UK. In the AstraZeneca case study senior scientists were interviewed using semi-structured interviews to determine information behaviour through the study scientific workflows. Document analysis was used to generate an understanding of the underpinning concepts and fonned one of the sources of context-dependent information on which the interview questions were based. The objectives of the Sanger Institute case study were slightly different as interviews were carried out with eight scientists together with the use of participation observation, to collect data to develop a database standard for one process of their Pharmacogenomics workflow. The results indicated that AstraZeneca would benefit through upgrading their data management solutions in the laboratory and by development of resources for the storage of data from larger scale projects such as whole genome scans. These studies will also generate very large amounts of data and the analysis of these will require more sophisticated statistical methods. At the Sanger Institute a minimum information standard was reported for the manual design of primers and included in a decision making tree developed for Polymerase Chain Reactions (PCRs). This tree also illustrates problems that can be encountered when designing primers along with procedures that can be taken to address such issues.
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Andrade, Jorge. "Grid and High-Performance Computing for Applied Bioinformatics." Doctoral thesis, Stockholm : Bioteknologi, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4573.

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Zheng, Chunfang. "Genome rearrangement algorithms applied to comparative maps." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27313.

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The Hannenhalli-Pevzner algorithm for computing the evolutionary distance between two genomes is very efficient when the genomes are signed and totally ordered. But in real comparative maps, the data suffer from problems such as coarseness, missing data, no signs, paralogy, order conflicts and mapping noise. In this thesis we have developed a suite of algorithms for genome rearrangement analysis in the presence of noise and incomplete information. For coarseness and missing data, we represent each chromosome as a partial order, summarized by a directed acyclic graph (DAG). We augment each DAG to a directed graph (DG) in which all possible linearizations are embedded. The chromosomal DGs representing two genomes are combined to produce a single bicoloured graph. The major contribution of the thesis is an algorithm for extracting a maximal decomposition of some subgraph into alternating coloured cycles, determining an optimal sequence of rearrangements, and hence the genomic distance. Also based on this framework, we have proposed an algorithm to solve all the above problems of comparative maps simultaneously by adding heuristic preprocessing to the exact algorithm approach. We have applied this to the comparison of maize and sorghum genomic maps on the GRAMENE database. A further contribution treats the inflation of genome distance by high levels of noise due to incorrectly resolved paralogy and error at the mapping, sequencing and alignment levels. We have developed an algorithm to remove the noise by maximizing strips and tested its robustness as noise levels increase.
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Abrams, Zachary. "A Translational Bioinformatics Approach to Parsing and Mapping ISCN Karyotypes: A Computational Cytogenetic Analysis of Chronic Lymphocytic Leukemia (CLL)." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461078174.

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Bentele, Kajetan. "Mechanisms of translational regulation in bacteria." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2013. http://dx.doi.org/10.18452/16839.

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Diese Arbeit untersucht den Zusammenhang zwischen Mechanismen der translationalen Regulation und der Genomorganisation in Bakterien. Der erste Teil der Arbeit analysiert die Beziehung zwischen der Translationseffizienz von Genen und der Häufigkeit bestimmter Codons am Genanfang. Es ist bekannt, dass die Häufigkeitsverteilung der Codons am Anfang der Gene bei einigen Organismen eine andere ist als sonst im Genom. Durch die systematische Analyse von ungefähr 400 bakteriellen Genomen, evolutionären Simulationen und experimentellen Untersuchungen sind wir zu dem Schluss gekommen, dass die beobachtete Abweichung der Codonhäufigkeiten wohl eine Konsequenz der Notwendigkeit ist, RNA Sekundärstruktur in der Nähe des Translationsstarts zu vermeiden und somit eine effiziente Initiation der Translation zu gewährleisten. Im zweiten Teil der Arbeit untersuchen wir den Einfluss der Genreihenfolge innerhalb eines Operons auf die Fitness von E. coli. In bakteriellen Genomen vereint ein Operon funktionell zusammengehörige Gene, die in einer mRNA zusammen transkribiert werden und somit in der Expression stark korreliert sind. Daneben kann die translationale Kopplung, d. h. die Interdependenz der Translationseffizienz zwischen benachbarten Genen innerhalb einer solchen mRNA, eine bestimmte Proteinstöchiometrie weiter stabilisieren. Mithilfe eines Modells für die translationale Kopplung sowie für den Chemotaxis Signalweg konnten wir zeigen, dass die native Genreihenfolge eine der Permutationen ist, die am meisten zur Robustheit der Chemotaxis beitragen. Die translationale Kopplung ist daher ein wichtiger Faktor, der die Anordnung der Gene innerhalb des Chemotaxis Operon bestimmt. Diese Arbeit zeigt, dass die Anforderungen einer effizienten Genexpression sowie die Robustheit wichtiger zellulärer Funktionen einen Einfluss auf die Organisation eines Genoms haben können: einerseits bei der Wahl der Codons am Anfang der Gene, andererseits auf die Ordnung der Gene innerhalb eines Operons.
This work investigates the relationship between mechanisms of translational regulation and genome organization in bacteria. The first part analyzes the connection between translational efficiency and codon usage at the beginning of genes. It is known for some organisms that usage of synonymous codons at the gene start deviates from the codon usage elsewhere in the genome. By analyzing about 400 bacterial genomes, evolutionary simulations and experimental investigations, we conclude that the observed deviation of codon usage at the beginning of genes is most likely a consequence of the need to suppress mRNA structure around the ribosome binding site, thereby allowing efficient initiation of translation. We investigate further driving forces for genome organization by studying the impact of gene order within an operon on the fitness of bacterial cells. Operons group functionally related genes which are transcribed together as single mRNAs in E. coli and other bacteria. Correlation of protein levels is thus to a large extent attributed to this coupling on the transcriptional level. In addition, translational coupling, i.e. the interdependence of translational efficiency between neighboring genes within such a mRNA, can stabilize a desired stoichiometry between proteins. Here, we study the role of translational coupling in robustness of E. coli chemotaxis. By employing a model of translational coupling and simulating the underlying signal transduction network we show that the native gene order ranks among the permutations contributing most to robustness of chemotaxis. We therefore conclude that translational coupling is an important determinant of the gene order within the chemotaxis operon. Both these findings show that requirements for efficient gene expression and robustness of cellular function have a pronounced impact on the genomic organization, influencing the local codon usage at the beginning of genes and the order of genes within operons.
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Moreno, Cabrera José Marcos. "A translational bioinformatics approach to improve genetic diagnostics of hereditary cancer using next-generation sequencing data." Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/672364.

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This PhD thesis has been carried out with the aim of improving, from a bioinformatic-based approach, the genetic diagnostics of hereditary cancer. More specifically, the aims were: 1. To perform a comprehensive evaluation of tools suitable for detecting CNVs from NGS panel data at single-exon resolution. 2. To select the best candidate tool to implement in the genetic diagnostics pipeline of the ICO-IGTP program on hereditary cancer. 3. After implementing it, to evaluate the impact of including the selected NGS CNV detection tool as a first-tier screening step prior to MLPA validation. 4. To develop a tool to identify false positives produced by germline NGS CNV detection tools. 5. To develop a web-based tool to support the entire diagnostic process during the laboratory routine.
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Chou, Hsin-Jung. "Transcriptome-Wide Analysis of Roles for Transfer RNA Modifications in Translational Regulation." eScholarship@UMMS, 2017. https://escholarship.umassmed.edu/gsbs_diss/943.

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Covalent nucleotide modifications in RNAs affect numerous biological processes, and novel functions are continually being revealed even for well-known modifications. Among all RNA species, transfer RNAs (tRNAs) are highly enriched with diverse modifications, which are known to play roles in decoding and tRNA stability, charging, and cellular trafficking. However, studies of tRNA modifications have been limited in a small scale and performed by groups with different methodologies. To systematically compare the functions of a large set of noncoding RNA modifications in translational regulation, I carried out ribosome profiling in 57 budding yeast mutants lacking nonessential genes involved in tRNA modifications. Deletion mutants with enzymes known to modify the anticodon loop or non-tRNA substrates such as rRNA exhibited the most dramatic translational perturbations, including altered dwell time of ribosomes on relevant codons, and altered ribosome density in protein-coding regions or untranslated regions of specific genes. Several mutants that result in loss of tRNA modifications in locations away from the anticodon loop also exhibited altered dwell time of ribosomes on relevant codons. Translational upregulation of the nutrient-responsive transcription factor Gcn4 was observed in roughly half of the mutants, consistent with the previous studies of Gcn4 in response to numerous tRNA perturbations. This work also discovered unexpected roles for tRNA modifying enzymes in rRNA 2’-O-methylation, and in transcriptional regulation of TY retroelements. Taken together, this work revealed the importance and novel functions of tRNA modifications, and provides a rich resource for discovery of additional links between tRNA modifications and gene regulation.
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Raiford, Douglas W. III. "Algorithmic Techniques Employed in the Isolation of Codon Usage Biases in Prokaryotic Genomes." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1211902424.

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Books on the topic "Translational and applied bioinformatics"

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Wei, Dong-Qing, Yilong Ma, William C. S. Cho, Qin Xu, and Fengfeng Zhou, eds. Translational Bioinformatics and Its Application. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1045-7.

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Markowitz, Joseph, ed. Translational Bioinformatics for Therapeutic Development. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0849-4.

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Selzer, Paul M., Richard J. Marhöfer, and Andreas Rohwer, eds. Applied Bioinformatics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-72800-9.

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Selzer, Paul M., Richard J. Marhöfer, and Oliver Koch. Applied Bioinformatics. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68301-0.

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Applied bioinformatics: An introduction. Berlin: Springer, 2008.

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Taguchi, Y.-h. Unsupervised Feature Extraction Applied to Bioinformatics. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22456-1.

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Applied categorical data analysis and translational research. 2nd ed. Hoboken, N.J: Wiley, 2010.

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Singh, Kamlesh, and Suman Sigroha. Translational Research and Applied Psychology in India. B1/I-1 Mohan Cooperative Industrial Area, Mathura Road New Delhi 110 044: SAGE Publications Pvt Ltd, 2019. http://dx.doi.org/10.4135/9789353287795.

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Applied ontology: An introduction. Frankfurt: Ontos Verlag, 2008.

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Applied statistics for network biology: Methods in systems biology. Weinheim, Germany: Wiley-Blackwell, 2011.

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Book chapters on the topic "Translational and applied bioinformatics"

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Tenenbaum, Jessica D., Nigam H. Shah, and Russ B. Altman. "Translational Bioinformatics." In Biomedical Informatics, 721–54. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4474-8_25.

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Tenenbaum, Jessica D., Nigam H. Shah, and Russ B. Altman. "Translational Bioinformatics." In Biomedical Informatics, 867–911. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58721-5_26.

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Wang, Yan, and Ji-Guang Wang. "Genome-Wide Association Studies of Hypertension and Several Other Cardiovascular Diseases." In Translational Bioinformatics, 1–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_1.

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Yu, Peng, Ming Liu, and Hong Jiang. "Progress of Epigenetic Changes in Heart Failure." In Translational Bioinformatics, 281–92. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_10.

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Zhang, Baoli, Xue Yang, Ning Feng, and Hong Jiang. "Progress of Genetics in Inherited Cardiomyopathies-Induced Heart Failure." In Translational Bioinformatics, 293–332. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_11.

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Dai, Shuiping. "Warfarin and Its Pharmacogenomic Study." In Translational Bioinformatics, 333–36. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_12.

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Zhu, Feng, and Kai Huang. "Gene Therapy and Genomic Application in Heart Disease." In Translational Bioinformatics, 337–74. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_13.

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Hao, Lili, Shiyu Chen, Jing Ma, Deyong Xiao, and Duan Ma. "Cardiac Transcriptome Profile in Heart Diseases." In Translational Bioinformatics, 31–63. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_2.

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Wang, Lu Qian, Kailash Singh, Aung Moe Zaw, and Billy Kwok Chong Chow. "The Emerging Role of Epigenetics." In Translational Bioinformatics, 65–101. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_3.

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Shi, Hui, Ying Yu, Minghui Li, and Ruizhen Chen. "Mitochondria Genome Mutations and Cardiovascular Diseases." In Translational Bioinformatics, 103–26. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1429-2_4.

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Conference papers on the topic "Translational and applied bioinformatics"

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Juric, Radmila, Eton Williams, and Inhwa Kim. "Semantic Overlapping in Translational Bioinformatics Applied to the Matching between Clinical Trial Eligibility Criteria and Patient Needs." In Workshop on COMP2CLINIC: Biomedical Researchers & Clinicians Closing The Gap Between Translational Research And Healthcare Practice. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009406503150322.

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TENENBAUM, JESSICA D., SUBHA MADHAVAN, ROBERT R. FREIMUTH, JOSHUA C. DENNY, and LEWIS FREY. "TRANSLATIONAL BIOINFORMATICS 101." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9789814749411_0050.

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Pollettini, Juliana Tarossi, and Alessandra Alaniz Macedo. "Poster: Chronic disease prevention: A Translational Bioinformatics approach." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729911.

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Ritchie, Marylyn D., Jason H. Moore, and Ju Han Kim. "Translational Bioinformatics: Biobanks in the Precision Medicine Era." In Pacific Symposium on Biocomputing 2020. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/9789811215636_0067.

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Tikhonov, D. A., L. I. Kulikova, A. T. Kopylov, V. R. Rudnev, and A. L. Kaysheva. "Analysis of Protein Molecule Structure with Post-Translational Modifications in Oncopathology." In Mathematical Biology and Bioinformatics. Pushchino: IMPB RAS - Branch of KIAM RAS, 2020. http://dx.doi.org/10.17537/icmbb20.33.

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HERVE, Lionel, Cédric Allier, Pierre Blandin, Fabrice Navarro, Mathilde Menneteau, Thomas Bordy, Olivier Cioni, and Sophie Morales. "Multispectral Total-variation Reconstruction Applied to Lens-free Microscopy." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jth3a.28.

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Kann, Maricel G. "Invited: A protein domain-centric approach to translational bioinformatics." In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2012. http://dx.doi.org/10.1109/iccabs.2012.6182622.

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Kim, Dokyoon, Ju Han Kim, and Jason H. Moore. "Translational Bioinformatics: Integrating Electronic Health Record and Omics Data." In Pacific Symposium on Biocomputing 2021. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811232701_0036.

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Wang, Yu W., Nicholas Reder, Soyoung Kang, Sara Javid, Suzanne M. Dintzis, and Jonathan T. Liu. "Multiplexed Molecular Imaging with Topically Applied SERS Nanoparticles for Endoscopy and Surgical Guidance." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jtu3a.65.

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Giaretta, Alberto. "Noise in transcriptional, splicing and translational regulation." In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2020. http://dx.doi.org/10.1109/cibcb48159.2020.9277724.

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Reports on the topic "Translational and applied bioinformatics"

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Barash, Itamar, and Robert Rhoads. Translational Mechanisms Governing Milk Protein Levels and Composition. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7696526.bard.

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Original objectives: The long-term goal of the research is to achieve higher protein content in the milk of ruminants by modulating the translational apparatus of the mammary gland genetically, nutritionally, or pharmacologically. The short-term objectives are to obtain a better understanding of 1) the role of amino acids (AA) as regulators of translation in bovine and mouse mammary epithelial cells and 2) the mechanism responsible for the synergistic enhancement of milk-protein mRNA polyadenylation by insulin and prolactin. Background of the topic: In many cell types and tissues, individual AA affect a signaling pathway which parallels the insulin pathway to modulate rates and levels of protein synthesis. Diverse nutritional and hormonal conditions are funneled to mTOR, a multidomain serine/threonine kinase that regulates a number of components in the initiation and elongation stages of translation. The mechanism by which AA signal mTOR is largely unknown. During the current grant period, we have studied the effect of essential AA on mechanisms involved in protein synthesis in differentiated mammary epithelial cells cultured under lactogenic conditions. We also studied lactogenic hormone regulation of milk protein synthesis in differentiated mammary epithelial cells. In the first BARD grant (2000-03), we discovered a novel mechanism for mRNA-specific hormone-regulated translation, namely, that the combination of insulin plus prolactin causes cytoplasmic polyadenylation of milk protein mRNAs, which leads to their efficient translation. In the current BARD grant, we have pursued the signaling pathways of this novel hormone action. Major conclusions/solutions/achievements: The positive and negative signaling from AA to the mTOR pathway, combined with modulation of insulin sensitization, mediates the synthesis rates of total and specific milk proteins in mammary epithelial cells. The current in vitro study revealed cryptic negative effects of Lys, His, and Thr on cellular mechanisms regulating translation initiation and protein synthesis in mammary epithelial cells that could not be detected by conventional in vivo analyses. We also showed that a signaling pathway involving Jak2 and Stat5, previously shown to lead from the prolactin receptor to transcription of milk protein genes, is also used for cytoplasmic polyadenylation of milk protein mRNAs, thereby stabilizing these mRNAs and activating them for translation. Implications: In vivo, plasma AA levels are affected by nutritional and hormonal effects as well as by conditions of exercise and stress. The amplitude in plasma AA levels resembles that applied in the current in vitro study. Thus, by changing plasma AA levels in the epithelial cell microenvironment or by sensitizing the mTOR pathway to their presence, it should be possible to modulate the rate of milk protein synthesis. Furthermore, knowledge that phosphorylation of Stat5 is required for enhanced milk protein synthesis in response to lactogenic opens the possibility for pharmacologic approaches to increase the phosphorylation of Stat5 and, thereby, milk protein production.
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Zhao, Bingyu, Saul Burdman, Ronald Walcott, Tal Pupko, and Gregory Welbaum. Identifying pathogenic determinants of Acidovorax citrulli toward the control of bacterial fruit blotch of cucurbits. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598168.bard.

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The specific objectives of this BARD proposal were: Use a comparative genomics approach to identify T3Es in group I, II and III strains of A. citrulli. Determine the bacterial genes contributing to host preference. Develop mutant strains that can be used for biological control of BFB. Background to the topic: Bacterial fruit blotch (BFB) of cucurbits, caused by Acidovoraxcitrulli, is a devastating disease that affects watermelon (Citrulluslanatus) and melon (Cucumismelo) production worldwide, including both Israel and USA. Three major groups of A. citrullistrains have been classified based on their virulence on host plants, genetics and biochemical properties. The host selection could be one of the major factors that shape A. citrullivirulence. The differences in the repertoire of type III‐ secreted effectors (T3Es) among the three A. citrulligroups could play a major role in determining host preferential association. Currently, there are only 11 A. citrulliT3Es predicted by the annotation of the genome of the group II strain, AAC00‐1. We expect that new A. citrulliT3Es can be identified by a combination of bioinformatics and experimental approaches, which may help us to further define the relationship of T3Es and host preference of A. citrulli. Implications, both scientific and agricultural: Enriching the information on virulence and avirulence functions of T3Es will contribute to the understanding of basic aspects of A. citrulli‐cucurbit interactions. In the long term, it will contribute to the development of durable BFB resistance in commercial varieties. In the short term, identifying bacterial genes that contribute to virulence and host preference will allow the engineering of A. citrullimutants that can trigger SAR in a given host. If applied as seed treatments, these should significantly improve the effectiveness and efficacy of BFB management in melon and atermelon production.
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