ORCID Profile
0000-0002-6607-5617
Current Organisation
Kyoto University
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Publisher: IMPERIAL COLLEGE PRESS
Date: 10-2008
Publisher: Oxford University Press (OUP)
Date: 20-05-2019
DOI: 10.1093/NAR/GKZ388
Abstract: Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Based on our GOLabeler—a state-of-the-art method for the third critical assessment of functional annotation (CAFA3), in this paper we propose NetGO, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information. Specifically, the advantages of NetGO are threefold in using network information: (i) NetGO relies on a powerful learning to rank framework from machine learning to effectively integrate both sequence and network information of proteins (ii) NetGO uses the massive network information of all species ( ) in STRING (other than only some specific species) and (iii) NetGO still can use network information to annotate a protein by homology transfer, even if it is not contained in STRING. Separating training and testing data with the same time-delayed settings of CAFA, we comprehensively examined the performance of NetGO. Experimental results have clearly demonstrated that NetGO significantly outperforms GOLabeler and other competing methods. The NetGO web server is freely available at issubmission.sjtu.edu.cn/netgo/.
Publisher: ACM
Date: 13-03-2005
Publisher: Springer Science and Business Media LLC
Date: 20-11-2009
Publisher: Elsevier BV
Date: 11-2017
Publisher: Public Library of Science (PLoS)
Date: 23-02-2012
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S12920-019-0625-1
Abstract: As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes roteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and the hierarchical structure of HPO. Specifically, we use a dual graph to regularize Non-negative Matrix Factorization (NMF) in a way that the information from different sources can be seamlessly integrated. In essence, HPOAnnotator solves the sparsity problem of a protein-phenotype association matrix by using a low-rank approximation. By combining the hierarchical structure of HPO and co-annotations of proteins, our model can well capture the HPO semantic similarities. Moreover, graph Laplacian regularizations are imposed in the latent space so as to utilize multiple PPI networks. The performance of HPOAnnotator has been validated under cross-validation and independent test. Experimental results have shown that HPOAnnotator outperforms the competing methods significantly. Through extensive comparisons with the state-of-the-art methods, we conclude that the proposed HPOAnnotator is able to achieve the superior performance as a result of using a low-rank approximation with a graph regularization. It is promising in that our approach can be considered as a starting point to study more efficient matrix factorization-based algorithms.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Oxford University Press (OUP)
Date: 26-02-2016
DOI: 10.1093/BIOINFORMATICS/BTW102
Abstract: Summary: The popularity of using NMR spectroscopy in metabolomics and natural products has driven the development of an array of NMR spectral analysis tools and databases. Particularly, web applications are well used recently because they are platform-independent and easy to extend through reusable web components. Currently available web applications provide the analysis of NMR spectra. However, they still lack the necessary processing and interactive visualization functionalities. To overcome these limitations, we present NMRPro, a web component that can be easily incorporated into current web applications, enabling easy-to-use online interactive processing and visualization. NMRPro integrates server-side processing with client-side interactive visualization through three parts: a python package to efficiently process large NMR datasets on the server-side, a Django App managing server-client interaction, and SpecdrawJS for client-side interactive visualization. Availability and implementation: Demo and installation instructions are available at ools/nmrpro/ Contact: mohamed@kuicr.kyoto-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 17-06-2019
DOI: 10.1038/S41467-019-09799-2
Abstract: The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for % of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
Publisher: Oxford University Press (OUP)
Date: 28-07-2014
DOI: 10.1093/BIOINFORMATICS/BTU501
Abstract: Summary: NetPathMiner is a general framework for mining, from genome-scale networks, paths that are related to specific experimental conditions. NetPathMiner interfaces with various input formats including KGML, SBML and BioPAX files and allows for manipulation of networks in three different forms: metabolic, reaction and gene representations. NetPathMiner ranks the obtained paths and applies Markov model-based clustering and classification methods to the ranked paths for easy interpretation. NetPathMiner also provides static and interactive visualizations of networks and paths to aid manual investigation. Availability: The package is available through Bioconductor and from Github at hmohamed/NetPathMiner Contact: mohamed@kuicr.kyoto-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Oxford University Press (OUP)
Date: 07-07-2016
DOI: 10.1093/BIB/BBV042
Abstract: Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, erse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.
Publisher: Oxford University Press (OUP)
Date: 09-10-2019
DOI: 10.1093/BIOINFORMATICS/BTZ756
Abstract: With the rapidly growing biomedical literature, automatically indexing biomedical articles by Medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and knowledge discovery. Over the past years, many large-scale MeSH indexing approaches have been proposed, such as Medical Text Indexer, MeSHLabeler, DeepMeSH and MeSHProbeNet. However, the performance of these methods is h ered by using limited information, i.e. only the title and abstract of biomedical articles. We propose FullMeSH, a large-scale MeSH indexing method taking advantage of the recent increase in the availability of full text articles. Compared to DeepMeSH and other state-of-the-art methods, FullMeSH has three novelties: (i) Instead of using a full text as a whole, FullMeSH segments it into several sections with their normalized titles in order to distinguish their contributions to the overall performance. (ii) FullMeSH integrates the evidence from different sections in a ‘learning to rank’ framework by combining the sparse and deep semantic representations. (iii) FullMeSH trains an Attention-based Convolutional Neural Network for each section, which achieves better performance on infrequent MeSH headings. FullMeSH has been developed and empirically trained on the entire set of 1.4 million full-text articles in the PubMed Central Open Access subset. It achieved a Micro F-measure of 66.76% on a test set of 10 000 articles, which was 3.3% and 6.4% higher than DeepMeSH and MeSHLabeler, respectively. Furthermore, FullMeSH demonstrated an average improvement of 4.7% over DeepMeSH for indexing Check Tags, a set of most frequently indexed MeSH headings. The software is available upon request. Supplementary data are available at Bioinformatics online.
No related grants have been discovered for Hiroshi Mamitsuka.