Blar i Institutt for klinisk og molekylær medisin på tidsskrift "BMC Bioinformatics"

Viser treff 1-15 av 15

    • A two-step site and mRNA-level model for predicting microRNA targets 

      Saito, Takaya; Sætrom, Pål (Journal article; Peer reviewed, 2010)
      Background: Despite experiments showing that the number of microRNA (miRNA) target sites is critical for miRNA targeting, most existing methods focus on identifying individual miRNA target sites and do not model contributions ...

    • abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis 

      Ali, Omer; Farooq, Amna; Yang, Mingyi; Jin, Victor X.; Bjørås, Magnar; Wang, Junbai (Peer reviewed; Journal article, 2022)
      Background Transcription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form ...

    • Assessment of composite motif discovery methods 

      Klepper, Kjetil; Sandve, Geir Kjetil; Abul, Osman; Johansen, Jostein; Drabløs, Finn (Journal article; Peer reviewed, 2008)
      Background: Computational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have ...

    • Compo: composite motif discovery using discrete models 

      Sandve, Geir Kjetil; Abul, Osman; Drabløs, Finn (Journal article; Peer reviewed, 2008)
      Background: Computational discovery of motifs in biomolecular sequences is an established field, with applications both in the discovery of functional sites in proteins and regulatory sites in DNA. In recent years there ...

    • Enhanced identification of significant regulators of gene expression 

      Ehsani, Rezvan; Drabløs, Finn (Peer reviewed; Journal article, 2020)
      Background Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact ...

    • An ensemble framework for identifying essential proteins 

      Zhang, Xue; Xiao, W; Acencio, Marcio Luis; Lemke, Ney; Wang, X (Peer reviewed; Journal article, 2016)
      Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy,and the number ...

    • Feature-based classification of human transcription factors into hypothetical sub-classes related to regulatory function. 

      Ehsani, Rezvan; Bahrami, Shahram; Drabløs, Finn Sverre (Peer reviewed; Journal article, 2016)
      Background Transcription factors are key proteins in the regulation of gene transcription. An important step in this process is the opening of chromatin in order to make genomic regions available for transcription. Data ...

    • Finding gene regulatory network candidates using the gene expression knowledge base 

      Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin (Journal article; Peer reviewed, 2014)
      Background: Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ?omics? data can ...

    • Identification of Metabolites from 2D 1H-13C HSQC NMR using Peak Correlation Plots 

      Öman, Tommy; Tessem, May-Britt; Bathen, Tone Frost; Bertilsson, Helena; Angelsen, Anders; Hedenström, Mattias; Andreassen, Trygve (Journal article; Peer reviewed, 2014)
      Background Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent ...

    • Improved benchmarks for computational motif discovery 

      Sandve, Geir Kjetil; Abul, Osman; Walseng, Vegard; Drabløs, Finn (Journal article; Peer reviewed, 2007-06-08)
      Background An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult ...

    • Measures of co-expression for improved function prediction of long non-coding RNAs 

      Ehsani, Rezvan; Drabløs, Finn (Journal article; Peer reviewed, 2018)
      Background Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes ...

    • Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation 

      Smith, DD; Sætrom, Pål; Snøve, Ola; Lundberg, C; Rivas, GE; Glackin, C; Larson, GP (Journal article; Peer reviewed, 2008)
      Background: Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely ...

    • MotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis 

      Klepper, Kjetil; Drabløs, Finn (Journal article; Peer reviewed, 2013)
      Background: Traditional methods for computational motif discovery often suffer from poor performance. In particular, methods that search for sequence matches to known binding motifs tend to predict many non-functional ...

    • The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding 

      Kornacker, K; Rye, Morten Beck; Håndstad, Tony; Drabløs, Finn (Journal article; Peer reviewed, 2012)
      Background: Chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-Seq) is the most frequently used method to identify the binding sites of transcription factors. Active binding sites can be seen ...

    • TopoICSim: A new semantic similarity measure based on gene ontology 

      Ehsani, Rezvan; Drabløs, Finn Sverre (Peer reviewed; Journal article, 2016)
      Background The Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular ...