2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

13-16 Nov. 2017

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  • [Front cover]

    Publication Year: 2017, Page(s): c1
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  • Preface

    Publication Year: 2017, Page(s):1 - 2
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  • Organization

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  • Main conference program committee members

    Publication Year: 2017, Page(s):1 - 8
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  • [Copyright notice]

    Publication Year: 2017, Page(s): 1
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  • Author index

    Publication Year: 2017, Page(s):1 - 17
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  • Biological big data analytics: Challenges and algorithms

    Publication Year: 2017, Page(s): 1
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  • Adventures with large biomedical datasets: Diseases, medical records, environment and genetics

    Publication Year: 2017, Page(s): 2
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  • Going beyond patterns: Deep understanding of biology with machine learning

    Publication Year: 2017, Page(s): 3
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  • Towards automated deep learning model construction and its applications in computational chemical biology

    Publication Year: 2017, Page(s): 4
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  • An energy landscape view of protein structure, dynamics, and (Dys) function

    Publication Year: 2017, Page(s): 5
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  • Radiomics — Beyond imaging for personalized and precision medicine

    Publication Year: 2017, Page(s): 6
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  • ChIP-seq data completion and transcription factors binding analyses

    Publication Year: 2017, Page(s): 7
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  • Differential privacy preserving deep learning in healthcare

    Publication Year: 2017, Page(s): 8
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  • Confidence assessment of protein-DNA complex models

    Publication Year: 2017, Page(s):9 - 15
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1295 KB) | HTML iconHTML

    Protein-DNA docking is an important computational technique for generating native or near-native complex models. A docking program typically generates a number of complex conformations and predicts the docking solution based on interaction energies. However, incomplete sampling and energy function deficiencies can result in false positive protein-DNA complex models, which hampers its application i... View full abstract»

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  • Effective small interfering RNA design based on convolutional neural network

    Publication Year: 2017, Page(s):16 - 21
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (460 KB) | HTML iconHTML

    In functional genomics, small interfering RNA (siRNA) can be used to knockdown gene expression. Usually, a target gene has numerous potential siRNAs, but their efficiencies of gene silencing often varies. Thus, for a successful RNA interference (RNAi), selecting the most effective siRNA is a critical step. Despite various computational algorithms have been developed, the efficacy prediction accura... View full abstract»

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  • Reconstructing and mining protein energy landscape to understand disease

    Publication Year: 2017, Page(s):22 - 27
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1252 KB) | HTML iconHTML

    Many pathogenic mutations percolate to protein dysfunction by altering dynamics. Reconstructing protein energy landscapes promises to relate dynamics to function but is generally infeasible due to the disparate spatio-temporal scales involved. Recent algorithmic innovation allows reconstructing energy landscapes of medium-size proteins in the presence of sufficient prior wet-laboratory structure d... View full abstract»

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  • Pattern-directed aligned pattern clustering

    Publication Year: 2017, Page(s):28 - 35
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1213 KB) | HTML iconHTML

    Functional region identification is of fundamental importance for protein sequences analysis for a protein family. Such knowledge not only provides a better scientific understanding but also assists drug discovery. Domain annotation is one approach but it needs to leverage existing databases. For de novo discovery, motif discovery locates and aligns locally similar sub-sequences and represents the... View full abstract»

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  • Structure-based protein family signature: Efficient comparison of multidomain proteins

    Publication Year: 2017, Page(s):36 - 41
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (273 KB) | HTML iconHTML

    The rapid increase in available protein structure datasets requires new techniques for fast, yet, effective analysis of protein 3D structures. In this work, we propose a structure-based signature for protein families, suitable for rapid analysis of multidomain domain protein structures. Our method is alignment-free, using protein strings as the basic representation. A key novelty is the two-stage ... View full abstract»

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  • DeepText2Go: Improving large-scale protein function prediction with deep semantic text representation

    Publication Year: 2017, Page(s):42 - 49
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (153 KB) | HTML iconHTML

    UniProtKB has collected more than 88 million protein sequences by July 2017. Less than 0.2% of these proteins, however, have added experimental GO annotations. To reduce this huge gap, automatic protein function prediction (AFP) becomes increasingly important. Results on CAFA (the Critical Assessment of protein Function Annotation algorithms) benchmark demonstrates that sequence homology based met... View full abstract»

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  • BiRWLGO: A global network-based strategy for lncRNA function annotation using bi-random walk

    Publication Year: 2017, Page(s):50 - 55
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (145 KB) | HTML iconHTML

    A large number of long non-coding RNAs (lncRNAs) have been identified over the past decades. Accumulating evidence proves that lncRNAs play key roles in various biological processes. However, the majority of the lncRNAs have not been functionally characterized. The annotation of lncRNA functions has become an area of focus in the fields of biology and bioinformatics. In this paper, we develop a gl... View full abstract»

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  • A novel algorithm for detecting co-evolutionary domains in protein and nucleotide sequences

    Publication Year: 2017, Page(s):56 - 61
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1576 KB) | HTML iconHTML

    Co-evolution exists ubiquitously in biological systems. At the molecular level, interacting proteins, such as ligands and their receptors and components in protein complexes, co-evolve to maintain their structural and functional interactions. Many proteins contain multiple functional domains interacting with different partners, making co-evolution of interacting domains occur more prominently. Mul... View full abstract»

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  • Discovery and disentanglement of protein aligned pattern clusters to reveal subtle functional subgroups

    Publication Year: 2017, Page(s):62 - 69
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1592 KB) | HTML iconHTML

    Proteins from the same family have similar functions. Hence, it is important to discover from a protein family conserved sequence patterns with variations to unveil the functionality of a functional domain. Aligned Pattern Clusters (APCs) are knowledge-rich representations comparing with probabilistic models. If significant aligned residue associations (ARAs) were discovered in APCs, they could re... View full abstract»

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  • Co-expression networks between protein encoding mitochondrial genes and all the remaining genes in human tissues

    Publication Year: 2017, Page(s):70 - 73
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (844 KB)

    Recent advances in sequencing allow the study of all identified human genes (≈ 22,000 protein encoding genes), which have differential expression between tissues. However, current knowledge on gene interactions lags behind, especially when one of the elements encodes a mitochondrial protein (≈ 1500). Mitochondrial proteins are encoded either by mitochondrial DNA (mtDNA; 13 proteins) or by nuclear ... View full abstract»

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  • RUPEE: Scalable protein structure search using run position encoded residue descriptors

    Publication Year: 2017, Page(s):74 - 78
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (57 KB)

    We have developed a fast, scalable, and purely geometric structure search combining techniques from information retrieval and big data with a novel approach to encoding sequences of torsion angles. Along the way, we introduce a new torsion angle plot without breaks in continuity while still maintaining traditional torsion angle ranges, to assist in identifying separable regions of torsion angles. ... View full abstract»

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