ReviewAvicenna journal of medical biotechnology
Review of Different Sequence Motif Finding Algorithms.
Review in Avicenna journal of medical biotechnology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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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.
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.
Who cites it
26 citing papers in PubMed.
- Revisiting motif finding: do bi-objective metaheuristics surpass single-objective metaheuristics?BMC bioinformatics · 2025Article
- PTF-Vāc: An explainable and generative deep co-learning encoder-decoder system for ab initio discovery of plant transcription factor binding sites.Plant communications · 2025Article
- De-motif sampling: an approach to decompose hierarchical motifs with applications in T cell recognition.Briefings in bioinformatics · 2025Article
- DNA-Protein Binding is Dominated by Short Anchoring Elements.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Mining of Root-Specific Expression Genes and Their Core Cis-Regulatory Elements in Plants.International journal of molecular sciences · 2025Article
- Pro-Opiomelanocortin and Melanocortin Receptor 3 and 4 Mutations in Genetic Obesity.Biomolecules · 2025Review
- Optimizing resource utilization for large scale problems through architecture aware scheduling.Scientific reports · 2024Article
- FaSTPACE: a fast and scalable tool for peptide alignment and consensus extraction.NAR genomics and bioinformatics · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- BIOMAPP::CHIP: large-scale motif analysis.BMC bioinformatics · 2024Article
- The good, the bad and the ugly of transposable elements annotation tools.Genetics and molecular biology · 2024Article
- Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.Frontiers in genetics · 2024Article
- KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.Journal of translational medicine · 2023Article
- Epitranscriptomic subtyping, visualization, and denoising by global motif visualization.Nature communications · 2023Article
- Article
- A Network-Based Approach for Improving Annotation of Transcription Factor Functions and Binding Sites inGenes · 2023Article
- Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks.Frontiers in cell and developmental biology · 2023Article
- PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences.Biology · 2022Article
- proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms.Nucleic acids research · 2022Article
- ggmotif: An R Package for the extraction and visualization of motifs from MEME software.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The DNA motif discovery is a primary step in many systems for studying gene function. Motif discovery plays a vital role in identification of Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Over the past decades, different algorithms were used to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approaches that many of them are time-consuming and easily trapped in a local optimum. Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome these problems. This paper presents a general classification of motif discovery algorithms with new sub-categories that facilitate building a successful motif discovery algorithm. It also presents a summary of comparison between them.
Indexed as
Identifiers
31057715PMC6490410What OpenQuestion holds
Registered trials
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.