PhD student, University of Southern Denmark, IST - EBB/Epidemiologi, Biostatistik og Biodemografi
Systems biology on epigenetics, microbiotics, epigenomics and biological algorithms
I'm very keen on developing and exploring biological algorithms with main interests in effective pathway analysis, microbial origin, scientific statistics and calculations with use cases within precision medicine and drug development in mind. I have a computer science and bioinformatician background, combining the best of both worlds.
Nowadays I spent the most time on research of health science, developing time to event models for predicting hazard (fatality/death scores) using techniques and regressions models within epigenome-wide association studies (aka. EWAS), primarily for the weak and elderly using DNA-methylation (DNAm). Using these, I want to develop prediction models for hospital use for easier risk score assessment and assigning better times slots for fragile patients, meaning, threat the ones that need it the most first based on science, in order to try and save as many as humanly possible.
Beside from this, I have a great interest and exploring nature within evolutionary and graph algorithms, and their applications within biology, computer science (in terms of effectiveness, data structures, and, neat tricks), and, the indie game development community.
Abstract: We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.
Pub.: 26 Jun '17, Pinned: 29 Jun '17
Abstract: The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.
Pub.: 26 Jun '17, Pinned: 29 Jun '17
Abstract: Sampling-based image matting is currently playing a significant role and showing great further development potentials in image matting. However, the consequent survey articles and detailed classifications are still rare in the field of corresponding research. Furthermore, besides sampling strategies, most of the sampling-based matting algorithms apply additional operations which actually conceal their real sampling performances. To inspire further improvements and new work, this paper makes a comprehensive survey on sampling-based matting in the following five aspects: (i) Only the sampling step is initially preserved in the matting process to generate the final alpha results and make comparisons. (ii) Four basic categories including eight detailed classes for sampling-based matting are presented, which are combined to generate the common sampling-based matting algorithms. (iii) Each category including two classes is analysed and experimented independently on their advantages and disadvantages. (iv) Additional operations, including sampling weight, settling manner, complement and pre- and post-processing, are sequentially analysed and added into sampling. Besides, the result and effect of each operation are also presented. (v) A pure sampling comparison framework is strongly recommended in future work.
Pub.: 15 Jun '17, Pinned: 29 Jun '17
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