Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy can produce petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because...
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Genome-and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution,but its characteristics and implication in cancer evolution and therapy remain lar...
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Genome-and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution,but its characteristics and implication in cancer evolution and therapy remain largely ***,we analyzed large-scale transcriptome/proteome profiles,such as The Cancer Genome Atlas(TCGA),the Genotype-Tissue Expression(GTEx),and the Clinical Proteomic Tumor Analysis Consortium(CPTAC),and found that compared to normal tissues,different cancer types showed a convergent pattern toward using biosynthetically low-cost amino *** a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids,termed energy cost per amino acid(ECPA).With this index,we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development,and revealed their reversed ***,focusing on the liver,a tissue with a dramatic increase in ECPA during development,we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single *** study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.
Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as...
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In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: "Discharge Me!". We evaluate both encoder-decoder...
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An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DN...
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An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.
In this paper, we present Hyperbolic Diffusion Procrustes Analysis (HDPA), a new method for informative representation of hierarchical datasets based on hyperbolic geometry, diffusion geometry, and Procrustes analysis...
In this paper, we present Hyperbolic Diffusion Procrustes Analysis (HDPA), a new method for informative representation of hierarchical datasets based on hyperbolic geometry, diffusion geometry, and Procrustes analysis. Our method jointly embeds multiple datasets in a product manifold of hyperbolic spaces, where the data's hidden common hierarchical structure is provably recovered. In addition, our method generates an intrinsic embedding that accommodates the joint representation of multiple datasets with different features, acquired by different equipment, at different sites, or under different environmental conditions. Experimental results demonstrate the efficacy of HDPA on three biomedical datasets comprising heterogeneous gene expression and mass cytometry data.
MERS-CoV, which belongs to the beta-coronaviruses together with SARS-CoV-2, although it has received relatively less attention by the COVID-19 pandemic, there is a sufficient possibility of new MERS-CoV lineages and v...
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ISBN:
(纸本)9798400708343
MERS-CoV, which belongs to the beta-coronaviruses together with SARS-CoV-2, although it has received relatively less attention by the COVID-19 pandemic, there is a sufficient possibility of new MERS-CoV lineages and variants. Previous studies have discussed the possibility of frequent recombination of MERS-CoV. We thus present a highly accurate method for the phylogenetic analysis and classification of MERS-CoV including recombinant sequences. We collected the sequences of S protein from MERS-CoV and divided them into five phylogenetic groups, of which recombinant sequences were divided into seven types. Physicochemical properties of amino acids were then calculated from the S protein sequences, and the results were used for the random forest model, Naïve Bayes classification, and k-nearest neighbor method. We also constructed several feature subsets based on the ranked amino acid properties and applied them to the random forest model. In each dataset, the amino acid physicochemical properties were ranked differently. Using this information, classification of MERS-CoV based on machine learning algorithms showed that the random forest model had the best accuracy and area under the curve compared with the k-nearest neighbor and Naïve Bayes classification methods. Several feature subsets were constructed using the correlation feature selection algorithm and applied to the random forest model. Overall, the performance of the classifier was improved compared to that when using all features. Coronaviruses including MERS-CoV continue to evolve into new forms through recombination or mutation. We thus present a method to increase the accuracy of their classification using additional information of the viral protein sequence, and confirm that a subunit consisting of optimal prominent features can improve the performance of the classifier by removing the unnecessary characteristic information.
Neuroinflammation immediately follows the onset of ischemic stroke. During this process, microglial cells are activated in and recruited to the tissue surrounding the irreversibly injured infarct core, referred to as ...
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Neuroinflammation immediately follows the onset of ischemic stroke in the middle cerebral artery. During this process, microglial cells are activated in and recruited to the penumbra. Microglial cells can be activated...
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Recent efforts to sequence the genomes of thousands of matched normal-tumor samples have led to the identification of millions of somatic mutations, the majority of which are non-coding. Most of these mutations are be...
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