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- Wohlfarth, C., et al.
(författare)
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miR-16 and miR-103 impact 5-HT4 receptor signalling and correlate with symptom profile in irritable bowel syndrome
- 2017
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Irritable bowel syndrome (IBS) is a gut-brain disorder involving alterations in intestinal sensitivity and motility. Serotonin 5-HT4 receptors are promising candidates in IBS pathophysiology since they regulate gut motor function and stool consistency, and targeted 5-HT4R selective drug intervention has been proven beneficial in subgroups of patients. We identified a single nucleotide polymorphism (SNP) (rs201253747) c.*61 T > C within the 5-HT4 receptor gene HTR4 to be predominantly present in diarrhoea-IBS patients (IBS-D). It affects a binding site for the miR-16 family and miR-103/miR-107 within the isoforms HTR4b/i and putatively impairs HTR4 expression. Subsequent miRNA-profiling revealed downregulation of miR-16 and miR-103 in the jejunum of IBS-D patients correlating with symptoms. In vitro assays confirmed expression regulation via three 3'UTR binding sites. The novel isoform HTR4b_2 lacking two of the three miRNA binding sites escapes miR-16/103/107 regulation in SNP carriers. We provide the first evidence that HTR4 expression is fine-tuned by miRNAs, and that this regulation is impaired either by the SNP c.*61 T > C or by diminished levels of miR-16 and miR-103 suggesting that HTR4 might be involved in the development of IBS-D.
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- Johansson, Peter, et al.
(författare)
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Classification of genomic and proteomic data using support vector machines
- 2007
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Ingår i: Fundamentals of Data Mining in Genomics and Proteomics. - Boston, MA : Springer US. - 9781402072604 - 9781475778090 - 9780306478154 ; , s. 187-202
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Bokkapitel (refereegranskat)abstract
- Supervised learning methods are used when one wants to construct a classifier. To use such a method, one has to know the correct classification of at least some samples, which are used to train the classifier. Once a classifier has been trained it can be used to predict the class of unknown samples. Supervised learning methods have been used numerous times in genomic applications and we will only provide some examples here. Different subtypes of cancers such as leukemia (Golub et al., 1999) and small round blue cell tumors (Khan et al., 2001) have been predicted based on their gene expression profiles obtained with microarrays. Microarray data has also been used in the construction of classifiers for the prediction of outcome of patients, such as whether a breast tumor is likely to give rise to a distant metastasis (van’t Veer et al., 2002) or whether a medulloblastoma patient is likely to have a favorable clinical outcome (Pomeroy et al., 2002). Proteomic patterns in serum have been used to identify ovarian cancer (Petricoin et al., 2002a) and prostate cancer (Adam et al., 2002); (Petricoin et al., 2002b).
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