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Support Vector Machines (SVM)

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. [Source: Wikipedia ]

MicRooN

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

Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process.

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microRPM

Submitted by ChenLiang on Tue, 01/09/2018 - 19:29

MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants.

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pirnaPre

Submitted by ChenLiang on Sun, 01/08/2017 - 16:35

MOTIVATION: PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are highly abundant in the germline. One important role of piRNAs is to defend genome integrity by guiding PIWI proteins to silence transposable elements (TEs), which have a high potential to cause deleterious effects on their host. The mechanism of piRNA-mediated post-transcriptional silencing was also observed to affect mRNAs, suggesting that piRNAs might play a broad role in gene expression regulation.

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miRNA-ensemble

Submitted by ChenLiang on Mon, 01/09/2017 - 10:36

Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data.

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SARS

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

The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training examples. To address this issue, unlabeled data may be exploited to help construct a computational model. Semisupervised learning deals with methods for exploiting unlabeled data in addition to labeled data automatically to improve learning performance, where no human intervention is assumed.

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FMIMS

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

MicroRNAs (miRNAs) act as a major biomarker of cancer. All miRNAs in human body are not equally important for cancer identification. We propose a methodology, called FMIMS, which automatically selects the most relevant miRNAs for a particular type of cancer. In FMIMS, miRNAs are initially grouped by using a SVM-based algorithm; then the group with highest relevance is determined and the miRNAs in that group are finally ranked for selection according to their redundancy.

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Average: 4.5 (2 votes)

At_miRNA

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

MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown. The identification of closely related microRNAs and target genes can be an essential first step towards the discovery of their combinatorial effects on different cellular states.

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New support vector machine-based method for microRNA target prediction

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

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model.

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mirnaDetect

Submitted by ChenLiang on Thu, 04/06/2017 - 19:28

MicroRNA (miRNA) plays an important role as a regulator in biological processes. Identification of (pre-)miRNAs helps in understanding regulatory processes. Machine learning methods have been designed for pre-miRNA identification. However, most of them cannot provide reliable predictive performances on independent testing datasets. We assumed this is because the training sets, especially the negative training sets, are not sufficiently representative.

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iBFE

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

High-throughput biotechnologies have been widely used to characterize clinical samples from various perspectives e.g., epigenomics, genomics and transcriptomics. However, because of the heterogeneity of these technologies and their outputs, individual analysis of the various types of data is hard to create a comprehensive view of disease subtypes. Integrative methods are of pressing need.

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