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Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. [Source: Wikipedia ]

iDeep

Submitted by ChenLiang on Sun, 09/10/2017 - 17:07

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 recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g.

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chimiRic

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

Recent technologies like AGO CLIP sequencing and CLASH enable direct transcriptome-wide identification of AGO binding and miRNA target sites, but the most widely used miRNA target prediction algorithms do not exploit these data. Here we use discriminative learning on AGO CLIP and CLASH interactions to train a novel miRNA target prediction model. Our method combines two SVM classifiers, one to predict miRNA-mRNA duplexes and a second to learn a binding model of AGO's local UTR sequence preferences and positional bias in 3'UTR isoforms.

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Average: 4.5 (2 votes)

RegNetwork

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

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places.

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IBRel

Submitted by ChenLiang on Thu, 04/06/2017 - 17:55

Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text.

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Vicinal

Submitted by ChenLiang on Sun, 09/10/2017 - 20:21

Non-coding (nc)RNAs are important structural and regulatory molecules. Accurate determination of the primary sequence and secondary structure of ncRNAs is important for understanding their functions. During cDNA synthesis, RNA 3' end stem-loops can self-prime reverse transcription, creating RNA-cDNA chimeras. We found that chimeric RNA-cDNA fragments can also be detected at 5' end stem-loops, although at much lower frequency.

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rna-wl

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

Thermodynamics-based dynamic programming RNA secondary structure algorithms have been of immense importance in molecular biology, where applications range from the detection of novel selenoproteins using expressed sequence tag (EST) data, to the determination of microRNA genes and their targets. Dynamic programming algorithms have been developed to compute the minimum free energy secondary structure and partition function of a given RNA sequence, the minimum free-energy and partition function for the hybridization of two RNA molecules, etc.

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miRLocator

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

MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify primary miRNAs and precursor miRNAs (pre-miRNAs), very few provide the functionality of locating mature miRNAs within plant pre-miRNAs.

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PCBC

Submitted by ChenLiang on Thu, 04/06/2017 - 18:48

The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples.

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miRBShunter

Submitted by ChenLiang on Sun, 09/10/2017 - 20:25

Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq).

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Average: 5 (1 vote)

MirStress

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

Organisms are often exposed to environmental pressures that affect homeostasis, so it is important to understand the biological basis of stress-response. Various biological mechanisms have evolved to help cells cope with potentially cytotoxic changes in their environment. miRNAs are small non-coding RNAs which are able to regulate mRNA stability. It has been suggested that miRNAs may tip the balance between continued cytorepair and induction of apoptosis in response to stress.

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