Publications by authors named "Thuc D Le"

Article Synopsis
  • RNA-sequencing technology aids in understanding miRNA regulation in diseases like cancer, but existing methods often struggle with sample-specific inference due to sample heterogeneity.
  • A new framework called Scan has been developed, integrating 27 network inference methods to effectively analyze tissue and cell-specific miRNA regulation from both bulk and single-cell RNA-sequencing data.
  • Scan enhances prediction accuracy by using prior miRNA target information and allows the construction of correlation networks, ultimately improving our understanding of miRNA regulation on an individual sample level.
View Article and Find Full Text PDF
Article Synopsis
  • Acute myeloid leukemia (AML) displays a variety of genetic mutations and DNA methylation changes, with a study focusing on FLT3 mutant versus wild-type cases to explore the causes behind differentiation blockage in AML.
  • The analysis utilized TCGA-LAML data, employing tools like cBioPortal and ChAMP to assess gene expression and methylation, revealing significant correlations and biological processes associated with altered gene activity in AML.
  • Key findings included a global hypo-methylated status in certain genes linked to critical transcription pathways, particularly the homeobox gene superfamily, and the overexpression of WT1 in FLT3 mutants, suggesting a mechanism for regulating methylation and advancing understanding of AML's biology for potential therapy development.
View Article and Find Full Text PDF

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e.

View Article and Find Full Text PDF
Article Synopsis
  • * The SCOM framework identifies and analyzes the competition among ncRNAs, highlighting that malignant tumors commonly share key ncRNAs rather than specific interactions, including their roles in drug resistance and immune regulation.
  • * SCOM and its accompanying web-based database, SCOMdb, serve as valuable tools for researchers to explore ncRNA regulation and identify potential biomarkers for cancer diagnostics and prognostics.
View Article and Find Full Text PDF

Summary: MicroRNA (miRNA) sponges influence the capability of miRNA-mediated gene silencing by competing for shared miRNA response elements and play significant roles in many physiological and pathological processes. It has been proved that computational or dry-lab approaches are useful to guide wet-lab experiments for uncovering miRNA sponge regulation. However, all of the existing tools only allow the analysis of miRNA sponge regulation regarding a group of samples, rather than the miRNA sponge regulation unique to individual samples.

View Article and Find Full Text PDF

Preeclampsia is a pregnancy-specific disease that can have serious effects on the health of both mothers and their offspring. Predicting which women will develop preeclampsia in early pregnancy with high accuracy will allow for improved management. The clinical symptoms of preeclampsia are well recognized, however, the precise molecular mechanisms leading to the disorder are poorly understood.

View Article and Find Full Text PDF

Background: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level.

View Article and Find Full Text PDF
Article Synopsis
  • The article explores the complex interactions of competing endogenous RNA (ceRNA) and microRNA (miRNA) sponges, which are groups that compete with each other to influence biological processes, particularly in cancer.* -
  • It reviews current methods and databases for identifying miRNA sponge modules, highlighting their significance in understanding cancer mechanisms and the competitive effects of these interactions.* -
  • The authors evaluate the effectiveness of these methods using pan-cancer datasets and discuss future challenges and directions for improving the computational inference of miRNA sponge modules.*
View Article and Find Full Text PDF

Background: Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced.

View Article and Find Full Text PDF

Motivation: Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data.

Results: We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3.

View Article and Find Full Text PDF

The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced.

View Article and Find Full Text PDF

Motivation: Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes.

View Article and Find Full Text PDF

Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward.

View Article and Find Full Text PDF

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system.

View Article and Find Full Text PDF

Motivation: Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth.

View Article and Find Full Text PDF
Article Synopsis
  • Identifying groups of cancer driver genes is crucial for understanding cancer progression, as single genes may not be sufficient, leading to the hypothesis that these genes operate together.
  • The method called DriverGroup was developed to detect these driver gene groups through a three-stage process: constructing a gene network, finding critical nodes within that network, and identifying driver gene groups from those nodes.
  • DriverGroup outperformed other existing methods in analyzing gene interactions and was successfully applied to datasets related to breast cancer, revealing important driver groups that correlate with cancer and patient survival outcomes.
View Article and Find Full Text PDF

Summary: The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs.

View Article and Find Full Text PDF

Motivation: microRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA-mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process.

View Article and Find Full Text PDF

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage.

View Article and Find Full Text PDF

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity.

View Article and Find Full Text PDF
Article Synopsis
  • Polygenic risk scores can predict the health traits of individuals but often rely on information from unrelated individuals, missing out on valuable familial data.
  • A study using 5,000 individuals with first-degree relatives showed comparable prediction accuracy to a much larger group of 220,000 unrelated individuals, indicating that family ties can enhance predictive power.
  • Particularly for lifestyle traits, the accuracy improved significantly when considering family information, suggesting that utilizing familial data could advance personalized health interventions.
View Article and Find Full Text PDF

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data.

View Article and Find Full Text PDF

After publication of this supplement article [1], it was brought to our attention that the Fig. 3 was incorrect. The correct Fig.

View Article and Find Full Text PDF

Background: Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically.

View Article and Find Full Text PDF

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers.

View Article and Find Full Text PDF