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IEEE International Conference on bioinformatics and Biomedicine (IEEE BIBM)

作者： Zhang, La Li, Xiao-peng Lai, Jia-qi Zhang, Lei Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Guangdong Peoples R China Guangdong Prov Hosp Chinese Med Guangzhou Guangdong Peoples R China Guangzhou Univ Chinese Med Clin Coll 2 Class 2016 Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781509030507

Network pharmacology which is based on bioinformatics and system biology knowledge has been employed into the field of Traditional Chinese Medicine (TCM) researches in recent years. In order to summarize current available bioinformatics databases, especially those can be used for TCM formulae study;a systematic search of Chinese literature by May 2017 was conducted. Results showed that research methods of network pharmacology have been widely applied in a variety of diseases and TCM formulae since 2012. Four comprehensive TCM bioinformatics databases with multi-functions were briefly introduced.

关键词： bioinformatics databases network pharmacology Chinese medicine formulae systematic review

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StLiter: A Novel Algorithm to Iteratively Build the Compacted de Bruijn Graph From Many Complete Genomes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2022年第4期19卷 2471-2483页

作者： Yu, Changyong Mao, Keming Zhao, Yuhai Chang, Cheng Wang, Guoren Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Software Shenyang 110819 Peoples R China Beijing Inst Life Natl Ctr Prot Sci Beijing Beijing Proteome Res Ctr State Key Lab Prote Beijing 102206 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

Recently, the compacted de Bruijn graph (cDBG) of complete genome sequences was successfully used in read mapping due to its ability to deal with the repetitions in genomes. However, current approaches are not flexible enough to fit frequently building the graphs with different k-mer lengths. Instead of building the graph directly, how can we build the compacted de Bruijin graph of longer k-mer based on the one of short k-mer? In this article, we present StLiter, a novel algorithm to build the compacted de Bruijn graph either directly from genome sequences or indirectly based on the graph of a short k-mer. For 100 simulated human genomes, StLiter can construct the graph of k-mer length 15-18 in 2.5-3.2 hours with maximal similar to 70GB memory in the case of without considering the reverese complements of the reference genomes. And it costs 4.5-5.9 hours when considering the reverse complements. In experiments, we compared StLiter with TwoPaCo, the state-of-art method for building the graph, on 4 datasets. For k-mer length 15-18, StLiter can build the graph 5-9 times faster than TwoPaCo using less maximal memory cost. For k-mer length larger than 18, given the graph of a short (k-x)-mer, such as x= 1-2, compared with TwoPaCo building the graph directly, StLiter can also build the graph more efficiently. The source codes of StLiter can be downloaded from web site https://***/BioLab-cz/StLiter.

关键词： Graph algorithm indexing methods bioinformatics databases queries

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Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding

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CURRENT COMPUTER-AIDED DRUG DESIGN 2024年第6期20卷 1013-1024页

作者： Qu, Xiaohan Du, Guoxia Hu, Jing Cai, Yongming Guangdong Pharmaceut Univ Sch Med Informat Engn Guangzhou Peoples R China Guangdong Prov Tradit Chinese Med Precis Med Big D Guangzhou Peoples R China

Background In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman *** Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary *** The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in

关键词： Deep learning drug development bioinformatics databases classifier design and evaluation feature representation topology

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Improving Protein-Protein Interaction Pair Ranking with an Integrated Global Association Score

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2012年第6期9卷 1690-1695页

作者： Tsai, Richard Tzong-Han Yuan Ze Univ Dept Comp Sci & Engn Chungli 320 Taiwan

Protein-protein interaction (PPI) database curation requires text-mining systems that can recognize and normalize interactor genes and return a ranked list of PPI pairs for each article. The order of PPI pairs in this list is essential for ease of curation. Most of the current PPI pair ranking approaches rely on association analysis between the two genes in the pair. However, we propose that ranking an extracted PPI pair by considering both the association between the paired genes and each of those genes' global associations with all other genes mentioned in the paper can provide a more reliable ranked list. In this work, we present a composite interaction score that considers not only the association score between two interactors (pair association score) but also their global association scores. We test three representative data fusion algorithms to estimate this global association score-two Borda-Fuse models and one linear combination model (LCM). The three estimation methods are evaluated using the data set of the BioCreative II. 5 Interaction Pair Task (IPT) in terms of area under the interpolated precision/recall curve (AUC iP/R). Our experimental results indicate that using LCM to estimate the global association score can boost the AUC iP/R score from 0.0175 to 0.2396, outperforming the best BioCreative II. 5 IPT system.

关键词： Text mining bioinformatics databases

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Estimating Functional Groups in Human Gut Microbiome With Probabilistic Topic Models

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2012年第3期11卷 203-215页

作者： Chen, Xin He, TingTing Hu, Xiaohua Zhou, Yanhong An, Yuan Wu, Xindong Cent China Normal Univ Dept Comp Sci Wuhan Peoples R China Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA Huazhong Univ Sci & Technol Sch Life Sci & Technol Wuhan Peoples R China Univ Vermont Dept Comp Sci Burlington VT USA

In this paper, based on the functional elements derived from non-redundant CDs catalogue, we show that the configuration of functional groups in meta-genome samples can be inferred by probabilistic topic modeling. The probabilistic topic modeling is a Bayesian method that is able to extract useful topical information from unlabeled data. When used to study microbial samples (assuming that relative abundance of functional elements is already obtained by a homology-based approach), each sample can be considered as a "document," which has a mixture of functional groups, while each functional group (also known as a "latent topic") is a weight mixture of functional elements (including taxonomic levels, and indicators of gene orthologous groups and KEGG pathway mappings). The functional elements bear an analogy with "words." Estimating the probabilistic topic model can uncover the configuration of functional groups (the latent topic) in each sample. The experimental results demonstrate the effectiveness of our proposed method.

关键词： bioinformatics databases biological data mining metagenomics probabilistic topic model

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On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types

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BMC bioinformatics 2014年第1期15卷 1-18页

作者： Zhang, Yun Phillips, Charles A. Rogers, Gary L. Baker, Erich J. Chesler, Elissa J. Langston, Michael A. Dupont Pioneer Johnston IA 50131 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Baylor Univ Dept Comp Sci Waco TX 76798 USA Jackson Lab Bar Harbor ME 04609 USA

Background: Integrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees. Results: The new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives. Conclusions: The new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://***. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practica

关键词： Biclique Biclustering bioinformatics databases Bipartite graphs Graph algorithms

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Dynamical systems for discovering protein complexes and functional modules from biological networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2007年第2期4卷 233-250页

作者： Li, Wenyuan Liu, Ying Huang, Hung-Chung Peng, Yanxiong Lin, Yongjing Ng, Wee-Keong Ong, Kok-Leong Univ Texas Dept Comp Sci Richardson TX 75083 USA Univ Texas Dept Comp Sci Richardson TX 75083 USA Univ Texas Dept Mol & Cell Biol Richardson TX 75083 USA Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Deakin Univ Sch Informat Technol & Engn Geelong Vic 3217 Australia

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NP-hard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a "spurious" heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in "spurious" heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

关键词： graph algorithms neural nets evolutionary computing bioinformatics databases

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From biological to social networks: Link prediction based on multi-way spectral clustering

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DATA & KNOWLEDGE ENGINEERING 2013年 87卷 226-242页

作者： Symeonidis, Panagiotis Iakovidou, Nantia Mantas, Nikolaos Manolopoulos, Yannis Aristotle Univ Thessaloniki GR-54006 Thessaloniki Greece Aristotle Univ Thessaloniki Dept Informat GR-54006 Thessaloniki Greece

Link prediction in protein-protein interaction networks (PPINs) is an important task in biology, since the vast majority of biological functions involve such protein interactions. Link prediction is also important for online social networks (OSNs), which provide predictions about who is a friend of whom. Many link prediction methods for PPINs/OSNs are local-based and do not exploit all network structure, which limits prediction accuracy. On the other hand, there are global approaches to detect the overall path structure in a network, being computationally prohibitive for huge-size PPINs/OSNs. In this paper, we enhance a previously proposed multi-way spectral clustering method by introducing new ways to capture node proximity in both PPINs/OSNs. Our new enhanced method uses information obtained from the top few eigenvectors of the normalized Laplacian matrix. As a result, it produces a less noisy matrix, which is smaller and more compact than the original one. In this way, we are able to provide faster and more accurate link predictions. Moreover, our new spectral clustering model is based on the well-known Bray-Curtis coefficient to measure proximity between two nodes. Compared to traditional clustering algorithms, such as k-means and DBSCAN, which assume globular (convex) regions in Euclidean space, our approach is more flexible in capturing the non-connected components of a social graph and a wider range of cluster geometries. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms and k-means algorithm, using two synthetic data sets, three real social networks and three real human protein data sets. Our experimental results show that our SpectralLink algorithm outperforms the local approaches, the k-means algorithm and another spectral clustering method in terms of effectiveness, whereas it is more efficient than the global approaches. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Cross-disciplinary applications Information filtering Clustering bioinformatics databases

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Designing Patterns and Profiles for Faster HMM Search

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2009年第2期6卷 232-243页

作者： Sun, Yanni Buhler, Jeremy Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

Profile HMMs are powerful tools for modeling conserved motifs in proteins. They are widely used by search tools to classify new protein sequences into families based on domain architecture. However, the proliferation of known motifs and new proteomic sequence data poses a computational challenge for search, requiring days of CPU time to annotate an organism's proteome. It is highly desirable to speed up HMM search in large databases. We design PROSITE-like patterns and short profiles that are used as filters to rapidly eliminate protein-motif pairs for which a full profile HMM comparison does not yield a significant match. The design of the pattern-based filters is formulated as a multichoice knapsack problem. Profile-based filters with high sensitivity are extracted from a profile HMM based on their theoretical sensitivity and false positive rate. Experiments show that our profile-based filters achieve high sensitivity (near 100 percent) while keeping around 20x speedup with respect to the unfiltered search program. Pattern-based filters typically retain at least 90 percent of the sensitivity of the source HMM with 30-40x speedup. The profile-based filters have sensitivity comparable to the multistage filtering strategy HMMERHEAD [15] and are faster in most of our experiments.

关键词： Biology and genetics bioinformatics databases sequence similarity search hidden Markov models

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Automated detection of records in biological sequence databases that are inconsistent with the literature

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 71卷 229-240页

作者： Bouadjenek, Mohamed Reda Verspoor, Karin Zobel, Justin Univ Melbourne Dept Comp & Informat Syst Parkville Vic 3053 Australia

We investigate and analyse the data quality of nucleotide sequence databases with the objective of automatic detection of data anomalies and suspicious records. Specifically, we demonstrate that the published literature associated with each data record can be used to automatically evaluate its quality, by cross-checking the consistency of the key content of the database record with the referenced publications. Focusing on GenBank, we describe a set of quality indicators based on the relevance paradigm of information retrieval (IR). Then, we use these quality indicators to train an anomaly detection algorithm to classify records as "confident" or "suspicious". Our experiments on the PubMed Central collection show assessing the coherence between the literature and database records, through our algorithms, is an effective mechanism for assisting curators to perform data cleansing. Although fewer than 0.25% of the records in our data set are known to be faulty, we would expect that there are many more in GenBank that have not yet been identified. By automated comparison with literature they can be identified with a precision of up to 10% and a recall of up to 30%, while strongly outperforming several baselines. While these results leave substantial room for improvement, they reflect both the very imbalanced nature of the data, and the limited explicitly labelled data that is available. Overall, the obtained results show promise for the development of a new kind of approach to detecting low-quality and suspicious sequence records based on literature analysis and consistency. From a practical point of view, this will greatly help curators in identifying inconsistent records in large-scale sequence databases by highlighting records that are likely to be inconsistent with the literature. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Data analysis Data quality bioinformatics databases Anomaly detection

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