Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new...
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Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.
Deep Probabilistic programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP offers a unified perspective, where inf...
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Redox-active disulfides are capable of being oxidized and reduced under physiological conditions. The enzymatic role of redox-active disulfides in thiol-disulfide reductases is well-known, but redox-active disulfides ...
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Antibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior es...
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We performed a gene co-expression analysis on Lung Squamous Cell Carcinoma data to find modules (groups) of genes that may highly impact the growth of these type of tumors. Additionally, we used cancer survival data t...
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A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lo...
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Background. There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of...
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Alternations in tumor microenvironment are critical in driving tumor development. However,it proves difficult to characterize their molecular components and further relate to important pathological *** using a network...
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Alternations in tumor microenvironment are critical in driving tumor development. However,it proves difficult to characterize their molecular components and further relate to important pathological *** using a network approach,we characterized systematic expression changes at multi-gene module levels between colorectal tumors with and without recurrence after surgery and confirmed their association with two critical
Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across ...
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The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, ...
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The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.
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