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Principal Component Analysis (PCA)

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. [Source: Wikipedia ]

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.

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novoMiRank

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

Small non-coding RNAs play a key role in many physiological and pathological processes. Since 2004, miRNA sequences have been catalogued in miRBase, which is currently in its 21st version. We investigated sequence and structural features of miRNAs annotated in the miRBase and compared them between different versions of this reference database.

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HomoTarget

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

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified.

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MinDist

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

The computational search for novel microRNA (miRNA) precursors often involves some sort of structural analysis with the aim of identifying which type of structures are prone to being recognized and processed by the cellular miRNA-maturation machinery. A natural way to tackle this problem is to perform clustering over the candidate structures along with known miRNA precursor structures. Mixed clusters allow then the identification of candidates that are similar to known precursors.

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CKDdb

Submitted by ChenLiang on Thu, 04/06/2017 - 17:37

Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications.

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MMCOP

Submitted by ChenLiang on Tue, 01/09/2018 - 18:52

A clear identification of the primary site of tumor is of great importance to the next targeted site-specific treatments and could efficiently improve patient's overall survival. Even though many classifiers based on gene expression had been proposed to predict the tumor primary, only a few studies focus on using DNA methylation profiles to develop classifiers, and none of them compares the performance of classifiers based on different profiles.

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