AIMC Topic: Nucleotide Motifs

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RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

BMC bioinformatics
BACKGROUND: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recog...

V-ELMpiRNAPred: Identification of human piRNAs by the voting-based extreme learning machine (V-ELM) with a new hybrid feature.

Journal of bioinformatics and computational biology
Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this p...

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

Journal of integrative bioinformatics
The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algor...

DiscMLA: An Efficient Discriminative Motif Learning Algorithm over High-Throughput Datasets.

IEEE/ACM transactions on computational biology and bioinformatics
The transcription factors (TFs) can activate or suppress gene expression by binding to specific sites, hence are crucial regulatory elements for transcription. Recently, series of discriminative motif finders have been tailored to offering promising ...

SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.

PloS one
Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs) achieve state-of-the-art performances in genom...

A Further Study on Mining DNA Motifs Using Fuzzy Self-Organizing Maps.

IEEE transactions on neural networks and learning systems
Self-organizing map (SOM)-based motif mining, despite being a promising approach for problem solving, mostly fails to offer a consistent interpretation of clusters with respect to the mixed composition of signal and noise in the nodes. The main reaso...

De novo transcriptome assembly of pummelo and molecular marker development.

PloS one
Pummelo (Citrus grandis) is an important fruit crop worldwide because of its nutritional value. To accelerate the pummelo breeding program, it is essential to obtain extensive genetic information and develop relative molecular markers. Here, we obtai...

An improved poly(A) motifs recognition method based on decision level fusion.

Computational biology and chemistry
Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene...

iM-Seeker: a webserver for DNA i-motifs prediction and scoring via automated machine learning.

Nucleic acids research
DNA, beyond its canonical B-form double helix, adopts various alternative conformations, among which the i-motif, emerging in cytosine-rich sequences under acidic conditions, holds significant biological implications in transcription modulation and t...

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network.

Bioinformatics (Oxford, England)
SUMMARY: Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translati...