You are here

Home

Classification

mirnasvm

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are a group of short (approximately 22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking.

Rating: 
Average: 5 (1 vote)

De Novo SVM Classification of Precursor MicroRNAs

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are small ncRNAs participating in diverse cellular and physiological processes through the post-transcriptional gene regulatory pathway. Critically associated with the miRNAs biogenesis, the hairpin structure is a necessary feature for the computational classification of novel precursor miRNAs (pre-miRs). Though many of the abundant genomic inverted repeats (pseudo hairpins) can be filtered computationally, novel species-specific pre-miRs are likely to remain elusive.

Rating: 
Average: 5 (1 vote)

microPred

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

In this article, we show that the classification of human precursor microRNA (pre-miRNAs) hairpins from both genome pseudo hairpins and other non-coding RNAs (ncRNAs) is a common and essential requirement for both comparative and non-comparative computational recognition of human miRNA genes. However, the existing computational methods do not address this issue completely or successfully. Here we present the development of an effective classifier system (named as microPred) for this classification problem by using appropriate machine learning techniques.

Rating: 
Average: 5 (1 vote)

HCS-Analyzer

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

High-throughput screening is a powerful technology principally used by pharmaceutical industries allowing the identification of molecules of interest within large libraries. Originally target based, cellular assays provide a way to test compounds (or other biological material such as small interfering RNA) in a more physiologically realistic in vitro environment. High-content screening (HCS) platforms are now available at lower cost, giving the opportunity for universities or research institutes to access those technologies for research purposes.

Rating: 
Average: 5 (1 vote)

IsomiRage

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

As more small RNA sequencing libraries are becoming available, it clearly emerges that microRNAs (miRNAs) are highly heterogeneous both in length and sequence. In comparison to canonical miRNAs, miRNA isoforms (termed as "isomiRs") might exhibit different biological properties, such as a different target repertoire, or enhanced/reduced stability. Nonetheless, this layer of information has remained largely unexplored due to the scarcity of small RNA NGS-datasets and the absence of proper analytical tools.

Rating: 
Average: 5 (1 vote)

CoRAL

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules.

Rating: 
Average: 5 (1 vote)

PlantMiRNAPred

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are a set of short (21-24 nt) non-coding RNAs that play significant roles as post-transcriptional regulators in animals and plants. While some existing methods use comparative genomic approaches to identify plant precursor miRNAs (pre-miRNAs), others are based on the complementarity characteristics between miRNAs and their target mRNAs sequences. However, they can only identify the homologous miRNAs or the limited complementary miRNAs.

Rating: 
Average: 5 (1 vote)

miRFam

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g.

Rating: 
Average: 5 (1 vote)

BlockClust

Submitted by ChenLiang on Tue, 01/09/2018 - 17:38

Non-coding RNAs (ncRNAs) play a vital role in many cellular processes such as RNA splicing, translation, gene regulation. However the vast majority of ncRNAs still have no functional annotation. One prominent approach for putative function assignment is clustering of transcripts according to sequence and secondary structure. However sequence information is changed by post-transcriptional modifications, and secondary structure is only a proxy for the true 3D conformation of the RNA polymer.

Rating: 
Average: 5 (1 vote)

HeteroMirPred

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs.

Rating: 
Average: 5 (1 vote)

Pages

Subscribe to Classification