Interaction-Based Aggregation of mRNA and miRNA Expression Profiles to Differentiate Myelodysplastic Syndrome

No abstract is available for this paper.

• Publication year

  2014

• Venue

  International Joint Conference on Biomedical Engineering Systems and Technologies

• Publication date

  2014-03-03

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1007/978-3-319-26129-4_11
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• miXGENE Tool for Learning from Heterogeneous Gene Expression Data Using Prior Knowledge

  2014cited by this paper
• The mechanics of miRNA-mediated gene silencing: a look under the hood of miRISC

  2012cited by this paper
• Synergistic effect of different levels of genomic data for cancer clinical outcome prediction

  2012cited by this paper
• Deregulation of microRNAs in myelodysplastic syndrome

  2012influential reference
• Evaluation of online miRNA resources for biomedical applications

  2012cited by this paper
• Identifying multi-layer gene regulatory modules from multi-dimensional genomic data

  2012cited by this paper
• Distinctive microRNA expression profiles in CD34+ bone marrow cells from patients with myelodysplastic syndrome

  2011cited by this paper
• mirConnX: condition-specific mRNA-microRNA network integrator

  2011cited by this paper
• MicroRNAs in development and disease.

  2011cited by this paper
• A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules

  2011cited by this paper
• miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes

  2011cited by this paper
• miRBase: integrating microRNA annotation and deep-sequencing data

  2010cited by this paper
• A distinct expression of various gene subsets in CD34+ cells from patients with early and advanced myelodysplastic syndrome.

  2010cited by this paper
• Sparse canonical correlation analysis, with applications to genomic data

  2009influential reference
• Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers

  2009cited by this paper
• Causes and consequences of microRNA dysregulation in cancer

  2009cited by this paper
• Finding microRNA regulatory modules in human genome using rule induction

  2008cited by this paper
• Prediction of both conserved and nonconserved microRNA targets in animals

  2008cited by this paper
• Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing.

  2008cited by this paper
• mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer

  2007cited by this paper
• Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes

  2007influential reference
• Combinatorial microRNA target predictions

  2005cited by this paper
• The microarray revolution: Perspectives from educators

  2004cited by this paper
• Prediction of mammalian microRNA targets.

  2003cited by this paper
• Integrating 'omic' information: a bridge between genomics and systems biology.

  2003cited by this paper
• Gene Selection for Cancer Classification using Support Vector Machines

  2002cited by this paper
• Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors

  2002influential reference
• Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

  1999cited by this paper
• Text Categorization with Support Vector Machines: Learning with Many Relevant Features

  1999cited by this paper
• The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14.

  1993cited by this paper
• Identification of common molecular subsequences.

  1981cited by this paper
• Where is the ?c?

  1978cited by this paper
• The approximation of one matrix by another of lower rank

  1936influential reference

• Classification Techniques to Predict the Risk of Myelodysplastic Syndromes using Deep Learning

  2023cites this paper