Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0026986 (myelodysplastic syndrome)
 14,926 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Mitochondrial disorders (MIDs) occasionally manifest as polyneuropathy either as the dominant feature or as one of many other manifestations (inherited mitochondrial neuropathy). MIDs in which polyneuropathy is the dominant feature, include NARP syndrome due to the transition m.8993T>, CMT2A due to MFN2 mutations, CMT2K and CMT4A due to GDAP1 mutations, and axonal/demyelinating neuropathy with external ophthalmoplegia due to POLG1 mutations. MIDs in which polyneuropathy is an inconstant feature among others is the MELAS syndrome, MERRF syndrome, LHON, Mendelian PEO, KSS, Leigh syndrome, MNGIE, SANDO; MIRAS, MEMSA, AHS, MDS (hepato-cerebral form), IOSCA, and ADOA syndrome. In the majority of the cases polyneuropathy presents in a multiplex neuropathy distribution. Nerve conduction studies may reveal either axonal or demyelinated or mixed types of neuropathies. If a hereditary neuropathy is due to mitochondrial dysfunction, the management of these patients is at variance from non-mitochondrial hereditary neuropathies. Patients with mitochondrial hereditary neuropathy need to be carefully investigated for clinical or subclinical involvement of other organs or systems. Supportive treatment with co-factors, antioxidants, alternative energy sources, or lactate lowering agents can be tried. Involvement of other organs may require specific treatment. Mitochondrial neuropathies should be included in the differential diagnosis of hereditary neuropathies.
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PMID:Inherited mitochondrial neuropathies. 2140 91

Existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on molecular profiles. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) with multi-study and multi-platform data on hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensional representation, comparison of molecular disease subtypes identified from separate t-SNE representations, and characterization of the obtained clusters based on pathway databases and additional data. In this manner, we performed an integrative analysis across multi-omics acute myeloid leukemia studies. Our approach indicated new molecular subtypes with differential survival and drug responsiveness among samples lacking fusion genes, including a novel myelodysplastic syndrome-like cluster and a cluster characterized with CEBPA mutations and differential activity of the S-adenosylmethionine-dependent DNA methylation pathway. In summary, integration across multiple studies can help to identify novel molecular disease subtypes and generate insight into disease biology.
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PMID:Data-driven characterization of molecular phenotypes across heterogeneous sample collections. 3132 28

Visualization algorithms have been widely used for intuitive interrogation of genomic data and popularly used tools include MDS, t-SNE, and UMAP. However, these algorithms are not tuned for the visualization of binary data and none of them consider the hubness of observations for the visualization. In order to address these limitations, here we propose hubViz, a novel tool for hub-centric visualization of binary data. We evaluated the performance of hubViz with its application to the gene expression data measured in multiple brain regions of rats exposed to cocaine, the single-cell RNA-seq data of peripheral blood mononuclear cells treated with interferon beta, and the literature mining data to investigate relationships among diseases. We further evaluated the performance of hubViz using simulation studies. We showed that hubViz provides effective visual inspection by locating the hub in the center and the contrasting elements in the opposite sides around the center. We believe that hubViz and its software can be powerful tools that can improve visualizations of various genomic data. The hubViz is implemented as an R package hubviz, which is publicly available at https://dongjunchung.github.io/hubviz/.
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PMID:hubViz: A Novel Tool for Hub-centric Visualization. 3275 73