ORCID Profile
0000-0002-1880-3624
Current Organisation
University of South Australia
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Publisher: Cold Spring Harbor Laboratory
Date: 08-07-2020
DOI: 10.1101/2020.07.07.192724
Abstract: 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. The recent development of single cell data and the booming of the exploration of cell trajectories using “pseudo-time” concept have inspired us to develop a pseudo-time based method to infer the miRNA-mRNA relationships characterising a biological process by taking into account the temporal aspect of the process. We have developed a novel approach, called pseudo-time causality (PTC), to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition (EMT), a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA-mRNA interactions in both single cell and bulk data. The results suggest that utilising the pseudo-temporal information from the data helps reveal the gene regulation in a biological process much better than using the static information. R scripts and datasets can be found at github.com/AndresMCB/PTC
Publisher: American Physical Society (APS)
Date: 03-03-2011
Publisher: Oxford University Press (OUP)
Date: 18-10-2021
DOI: 10.1093/BIOINFORMATICS/BTAA899
Abstract: 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. The recent development of single cell data and the booming of the exploration of cell trajectories using ‘pseudotime’ concept have inspired us to develop a pseudotime-based method to infer the miRNA–mRNA relationships characterizing a biological process by taking into account the temporal aspect of the process. We have developed a novel approach, called pseudotime causality, to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition, a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA–mRNA interactions in both single cell and bulk data. The results suggest that utilizing the pseudotemporal information from the data helps reveal the gene regulation in a biological process much better than using the static information. R scripts and datasets can be found at github.com/AndresMCB/PTC. Supplementary data are available at Bioinformatics online.
Publisher: Canadian Science Publishing
Date: 05-2011
DOI: 10.1139/P11-033
Abstract: The population of magnetic sublevels in hydrogen-like uranium ions has been investigated in relativistic ion–atom collisions by observing the subsequent X-ray emission. Using the gas target at the experimental storage ring facility we observed the angular emission of Lyman-α radiation from hydrogen-like uranium ions. The alignment parameter for three different interaction energies was measured and found to agree well with theory. In addition, the use of different gas targets allowed for the electron-impact excitation process to be observed.
Publisher: Canadian Science Publishing
Date: 05-2011
DOI: 10.1139/P11-012
Abstract: We present a review of the relativistic convergent close-coupling (RCCC) method and describe how it has been used to resolve the discrepancy between theory and experiment for the polarization of the Lyman-α 1 X-ray line emitted by hydrogen-like Ti 21+ , Ar 17+ , and Fe 25+ ions excited by electron impact. We find that taking account of Breit relativistic corrections is important to resolve the discrepancy between experiment and theoretical calculations.
Publisher: Oxford University Press (OUP)
Date: 19-09-2022
DOI: 10.1093/BFGP/ELAC030
Abstract: The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of s les along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at github.com/AndresMCB/DynamicCancerDriver
No related grants have been discovered for Andres Mauricio Cifuentes Bernal.