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Discovery Projects - Grant ID: DP130104090

Title
Discovery Projects - Grant ID: DP130104090
Funding
ARC | Discovery Projects
Contract (GA) number
DP130104090
Start Date
2013/01/01
End Date
2015/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP130104090

 

  • Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles

    Zhang, Weijia; Le, Thuc Duy; Liu, Lin; Zhou, Zhi-Hua; Li, Jiuyong (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP130104090 (DP130104090)
    Motivation microRNAs (miRNAs) play crucial roles in post-transcriptional gene regulation of both plants and mammals, and dysfunctions of miRNAs are often associated with tumorigenesis and development through the effects on their target messenger RNAs (mRNAs). Identifying miRNA functions is critical for understanding cancer mechanisms and determining the efficacy of drugs. Computational methods analyzing high-throughput data offer great assistance in understanding the diverse and complex relat...

    Ensemble Methods for MiRNA Target Prediction from Expression Data

    Le, Thuc Duy; Zhang, Junpeng; Liu, Lin; Li, Jiuyong (2015)
    Projects: ARC | Discovery Projects - Grant ID: DP130104090 (DP130104090)
    Background microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear...

    Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data

    Xu, Taosheng; Le, Thuc Duy; Liu, Lin; Wang, Rujing; Sun, Bingyu; Li, Jiuyong (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP130104090 (DP130104090)
    Background Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different reg...

    miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships

    Le, Thuc Duy; Zhang, Junpeng; Liu, Lin; Liu, Huawen; Li, Jiuyong (2015)
    Projects: ARC | Discovery Projects - Grant ID: DP130104090 (DP130104090)
    microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem. Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information. A typical procedure for applying and evaluating such a method is i) collecting matched miRNA and mRNA expression profiles in a specific condition, e.g. a cancer dataset from T...

    Identification of miRNA-mRNA regulatory modules by exploring collective group relationships

    Masud Karim, S. M.; Liu, Lin; Le, Thuc Duy; Li, Jiuyong (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP130104090 (DP130104090)
    Background microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation in plants and animals. They regulate a wide range of biological processes by targeting messenger RNAs (mRNAs). Evidence suggests that miRNAs and mRNAs interact collectively in gene regulatory networks. The collective relationships between groups of miRNAs and groups of mRNAs may be more readily interpreted than those between individual miRNAs and mRNAs, and thus are useful for gaining insight into...

    Inferring microRNA and transcription factor regulatory networks in heterogeneous data

    Le, Thuc D; Liu, Lin; Liu, Bing; Tsykin, Anna; Goodall, Gregory J; Satou, Kenji; Li, Jiuyong (2013)
    Projects: ARC | Discovery Projects - Grant ID: DP130104090 (DP130104090), NHMRC | Regulation of the actin cytoskeleton by miR-200 (1008327)
    Background Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving mi...

    From Observational Studies to Causal Rule Mining

    Li, Jiuyong; Le, Thuc Duy; Liu, Lin; Liu, Jixue; Jin, Zhou; Sun, Bingyu; Ma, Saisai (2015)
    Projects: ARC | Discovery Projects - Grant ID: DP130104090 (DP130104090), ARC | Discovery Projects - Grant ID: DP140103617 (DP140103617)
    Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, hence they can only be applied to problems with available domain knowledge and a handful of var...
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  • Scientific Results

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    PUBLICATIONS BY ACCESS MODE

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    Publications in Repositories

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