TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via th...
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Analysis and visualization of biological networks, such as protein-protein and protein-DNA interactions, are crucially important toward obtaining a thorough understanding of living systems. Here, we present an integra...
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Tuberculosis (TB) is one of the top ten reasons for death from an infectious agent. Although TB is curable and preventable, delay in diagnosis and treatment can lead the patient to death. Advancements in computer-Aide...
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A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespread approach is to fr...
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RECOMB 2013 was successfully held in Tsinghua University, Beijing, China on April 7-10, 2013, hosted by the bioinformatics Division and Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Inform...
RECOMB 2013 was successfully held in Tsinghua University, Beijing, China on April 7-10, 2013, hosted by the bioinformatics Division and Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Information Science and Technology (TNLIST). A total of about 500 professionals from both academia and industry from 29 countries and regions attended the conference and its RECOMB-Seq satellite workshop after the main conference.
High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is sti...
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High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search.
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
A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individ-uals within a group. This app...
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The alignment of reads generated by next-generation sequencers is an important problem in many biomedical applications. Although many methods have been proposed, we introduce a new randomized algorithm with the distin...
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ISBN:
(纸本)9781479907151
The alignment of reads generated by next-generation sequencers is an important problem in many biomedical applications. Although many methods have been proposed, we introduce a new randomized algorithm with the distinction of having high performance across a wide range of read lengths and base error rates. We utilize two FM indices to facilitate efficient bidirectional searching. Randomization allows us to estimate effectively key parameters, which ultimately account for the consistency in performance of the method. Our method by and large outperformed some of the recent and popular methods over a wide range of read lengths and base error rates.
Subnetworks can reveal the complex patterns of the whole-genome network by extracting the interactions that depend on temporal, spatial, or condition specific context. In this paper we present an optimization framewor...
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Subnetworks can reveal the complex patterns of the whole-genome network by extracting the interactions that depend on temporal, spatial, or condition specific context. In this paper we present an optimization framework to identify condition specific subnetworks. This framework allows us to identify the most coherent subnetwork by integrating the information from both nodes and edges in the graph. Importantly we design an algorithm to solve the optimization problem efficiently. It is very fast and can extract subnetworks from large-scale network with about 10000 nodes. As a pilot study we apply our method to identify type 2 diabetes related subnetworks in the human protein-protein interaction network.
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