Evidence map›Paper›PMID 15699571›Full record

ReviewMolecular biotechnology2005

Databases, models, and algorithms for functional genomics: a bioinformatics perspective.

Gautam B Singh, Harkirat Singh

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biotechnology, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Probability landscapes for integrative genomics.Theoretical biology & medical modelling · 2008
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Gautam B SinghComputer Science and Engineering, Oakland University, Rochester, MI 48309, USA. singh@oakland.edu
Harkirat Singh

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A variety of patterns have been observed on the DNA and protein sequences that serve as control points for gene expression and cellular functions. Owing to the vital role of such patterns discovered on biological sequences, they are generally cataloged and maintained within internationally shared databases. Furthermore,the variability in a family of observed patterns is often represented using computational models in order to facilitate their search within an uncharacterized biological sequence. As the biological data is comprised of a mosaic of sequence-levels motifs, it is significant to unravel the synergies of macromolecular coordination utilized in cell-specific differential synthesis of proteins. This article provides an overview of the various pattern representation methodologies and the surveys the pattern databases available for use to the molecular biologists. Our aim is to describe the principles behind the computational modeling and analysis techniques utilized in bioinformatics research, with the objective of providing insight necessary to better understand and effectively utilize the available databases and analysis tools. We also provide a detailed review of DNA sequence level patterns responsible for structural conformations within the Scaffold or Matrix Attachment Regions (S/MARs).

Indexed as

Databases, GeneticModels, GeneticAnimalsComputational BiologyGene Expression ProfilingGene Expression RegulationGenomicsHumansSequence AnalysisSignal TransductionTranscription FactorsTranscription Factors

Identifiers

PMID15699571

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.