2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

15-18 Dec. 2016

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  • [Front cover]

    Publication Year: 2016, Page(s): c1
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  • [Copyright notice]

    Publication Year: 2016, Page(s): 1
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  • Preface

    Publication Year: 2016, Page(s):1 - 2
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  • Computational psychophysiology based research methodology for mental health

    Publication Year: 2016, Page(s): 1
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  • Whole genome sequencing of disease animal models

    Publication Year: 2016, Page(s): 2
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  • Information and decision-making in dynamic cell signaling

    Publication Year: 2016, Page(s): 3
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  • Trajectory analysis — Linking genomic and proteomic data with disease progression

    Publication Year: 2016, Page(s): 4
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  • Deep-Learning: Investigating feed-forward deep Neural Networks for modeling high throughput chemical bioactivity data

    Publication Year: 2016, Page(s): 5
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  • Networks and models for the integrated analysis of multi omics data

    Publication Year: 2016, Page(s): 6
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  • High performance computational biology and drug design on TianHe Supercomputers

    Publication Year: 2016, Page(s): 7
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  • Multi-omic approaches for liver cancer biomarker discovery

    Publication Year: 2016, Page(s): 8
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  • Semi-hypothesis guided exploratory analysis for biomedical applications

    Publication Year: 2016, Page(s): 9
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  • Computational tools for studying gene regulation in the 3-dimensional genome

    Publication Year: 2016, Page(s): 10
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  • Clinical application of precision medicine: Zhongshan Hospital strategy

    Publication Year: 2016, Page(s): 11
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  • An algorithmic-information calculus for reprogramming biological networks

    Publication Year: 2016, Page(s): 12
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  • A map of binding cavity conformations reveals differences in binding specificity

    Publication Year: 2016, Page(s):13 - 19
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1454 KB) | HTML iconHTML

    Protein structure comparison algorithms are useful for predicting aspects of protein function. Some algorithms identify remote homologs, while others distinguish closely related proteins that prefer different substrates. Most of these methods assume that proteins are rigid in order to perform comparisons more rapidly, while others compensate for flexibility by representing proteins as a connected ... View full abstract»

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  • InfDisSim: A novel method for measuring disease similarity based on information flow

    Publication Year: 2016, Page(s):20 - 26
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (3096 KB) | HTML iconHTML

    Similar diseases are often caused by their similar molecular origins, such as disease-related protein-coding genes (PCGs). And nowadays, the function of PCGs has been widely studied on a gene function network, where each node represents a gene and each edge indicates an interaction between pair-wise genes. Therefore, functional interaction between disease-related PCGs should be exploited to measur... View full abstract»

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  • Predicting microRNA-environmental factor interactions based on bi-random walk and multi-label learning

    Publication Year: 2016, Page(s):27 - 32
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (225 KB) | HTML iconHTML

    Increasing evidences have shown that microRNAs (miRNAs) play important roles in many diseases. The environmental factors (EFs) can regulate the expression level of miRNAs in human tissues. Therefore, identifying potential miRNA-environmental factor interactions is helpful not only for understanding the pathogenesis of diseases, but also for disease diagnosis, prognosis and treatment. In this paper... View full abstract»

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  • CAMIL: Clustering and Assembly with Multiple Instance Learning for phenotype prediction

    Publication Year: 2016, Page(s):33 - 40
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (218 KB) | HTML iconHTML

    The recent advent of Metagenome-Wide Association Studies (MGWAS) has allowed for increased accuracy in the prediction of patient phenotype (disease), but has also presented big data challenges. Meanwhile, Multiple Instance Learning (MIL) is useful in the domain of bioinformatics because, in addition to classifying patient phenotype, it can also identify individual parts of the microbiome that are ... View full abstract»

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  • Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy

    Publication Year: 2016, Page(s):41 - 46
    Cited by:  Papers (6)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1392 KB) | HTML iconHTML

    The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to ex... View full abstract»

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  • PredRBR: Accurate Prediction of RNA-Binding Residues in proteins using Gradient Tree Boosting

    Publication Year: 2016, Page(s):47 - 52
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (901 KB) | HTML iconHTML

    Prediction of Protein-RNA binding sites is one of the most challenging and intriguing problems in the field of computational biology. Here, we proposed an effectively machine learning algorithm termed PredRBR (Prediction of RNA Binding Residues), using Gradient Tree Boosting algorithm and mRMR-IFS feature selection method in combination with sequence features, structure characteristics and two cat... View full abstract»

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  • HPTree: Reconstructing phylogenetic trees for ultra-large unaligned DNA sequences via NJ model and Hadoop

    Publication Year: 2016, Page(s):53 - 58
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (614 KB) | HTML iconHTML

    Constructing phylogenetic tree for ultra-large sequences (eg. Files more than 1GB) is quite difficult, especially for the unaligned DNA sequences. It is meaningless and impracticable to do multiple sequence alignment for large diverse DNA sequences. We try to do clustering firstly for the mounts of DNA sequences, and divide them into several clusters. Then each cluster is aligned and phylogenetic ... View full abstract»

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  • Accurate annotation of metagenomic data without species-level references

    Publication Year: 2016, Page(s):59 - 64
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (576 KB) | HTML iconHTML

    Taxonomic annotation is a critical first step for analysis of metagenomic data. Despite a lot of tools being developed, the accuracy is still not satisfactory, in particular, when a close species-level reference does not exist in the database. In this paper, we propose a novel annotation tool, MetaAnnotator, to annotate metagenomic reads, which outperforms all existing tools significantly when onl... View full abstract»

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  • A de novo genome assembler based on MapReduce and bi-directed de Bruijn graph

    Publication Year: 2016, Page(s):65 - 71
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (218 KB) | HTML iconHTML

    The next generation sequencing (NGS) techniques have enabled biologists to generate large DNA sequences in a high-throughput and low-cost way. Assembly of NGS reads still face great challenges due to the short reads and enormous high volume. In this paper, we presented a new assembler, called GAMR, which is based on bi-directed de Bruijn graph and implemented using MapReduce framework. We designed... View full abstract»

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  • Concod: Accurate consensus-based approach of calling deletions from high-throughput sequencing data

    Publication Year: 2016, Page(s):72 - 77
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (792 KB) | HTML iconHTML

    Accurate calling of structural variations such as deletions with short sequence reads from high-throughput sequencing is an important but challenging problem in the field of genome analysis. There are many existing methods for calling deletions. At present, not a single method clearly outperforms all other methods in precision and sensitivity. A popular strategy used by several authors is combinin... View full abstract»

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