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Autor:
Giulia Paciello, Elisa Ficarra
Publikováno v:
BMC Bioinformatics
Background Latest Next Generation Sequencing technologies opened the way to a novel era of genomic studies, allowing to gain novel insights into multifactorial pathologies as cancer. In particular gene fusion detection and comprehension have been dee
Publikováno v:
BMC Bioinformatics
Background Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evalu
Publikováno v:
BMC Bioinformatics
Background Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly
Autor:
Sangwoo Kim, Doheon Lee
Publikováno v:
BMC Bioinformatics
Background Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes
Autor:
Charles Coutant, Roberto Incitti, Benoît Charles, Kai Yan, Lajos Pusztai, Euler Guimarães Horta, Fabrice Andre, René Natowicz, Philippe Guinot, Roman Rouzier
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 9, Iss 1, p 149 (2008)
BMC Bioinformatics, Vol 9, Iss 1, p 149 (2008)
Background DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patient
Autor:
Birte Hellwig, Mathias Gehrmann, Wiebke Schormann, Jörg Rahnenführer, Jan G. Hengstler, Marcus Schmidt
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 276 (2010)
BMC bioinformatics, 11: 276
BMC Bioinformatics
BMC bioinformatics, 11: 276
BMC Bioinformatics
Background A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from
Autor:
Andrew D. Smith, Hiroyuki Aburatani, Atsushi Niida, Michael Q. Zhang, Seiya Imoto, Tetsu Akiyama
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 10, Iss 1, p 71 (2009)
BMC Bioinformatics, Vol 10, Iss 1, p 71 (2009)
Background Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data ha
Autor:
Abdul Rawoof, Vinod Kumar Verma, Mohammed M. Idris, Rimpi Khurana, Lekha Dinesh Kumar, Rekha A Nair, Ganesh Mahidhara, Alan Richard Clarke, Shrish Tiwari
Publikováno v:
BMC Bioinformatics
Background Given the estimate that 30% of our genes are controlled by microRNAs, it is essential that we understand the precise relationship between microRNAs and their targets. OncomiRs are microRNAs (miRNAs) that have been frequently shown to be de
Publikováno v:
BMC Bioinformatics
BMC Bioinformatics, Vol 12, Iss 1, p 471 (2011)
BMC Bioinformatics, Vol 12, Iss 1, p 471 (2011)
Background DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previ
Publikováno v:
BMC Bioinformatics
Background Many calssifiers which are constructed with chosen gene markers have been proposed to forecast the prognosis of patients who suffer from breast cancer. However, few of them has been applied in clinical practice because of the bad generaliz