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7th international conference on bioinformatics, incob 2008

作者： Ranganathan, Shoba Hsu, Wen-Lian Yang, Ueng-Cheng Tan, Tin Wee Department of Chemistry and Biomolecular Sciences ARC Centre of Excellence in Bioinformatics Macquarie University Sydney NSW 2109 Australia Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore 8 Medical Drive Singapore 117597 Singapore Institute of Information Science Academia Sinica Nankang Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Institute of Biomedical Informatics Center for Systems and Synthetic Biology National Yang-Ming University Taiwan

the 2008 annual conference of the Asia Pacific bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998, was organized as the 7th international conference on bioinformatics (incob), jointly with the bioinformatics and Systems Biology in Taiwan (BIT 2008) conference, Oct. 20-23, 2008 at Taipei, Taiwan. Besides bringing together scientists from the field of bioinformatics in this region, incob is actively involving researchers from the area of systems biology, to facilitate greater synergy between these two groups. Marking the 10th Anniversary of APBioNet, this incob 2008 meeting followed on from a series of successful annual events in Bangkok (thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India) and Hong Kong. Additionally, tutorials and the Workshop on Education in bioinformatics and Computational Biology (WEBCB) immediately prior to the 20th Federation of Asian and Oceanian Biochemists and Molecular Biologists (FAOBMB) Taipei conference provided ample opportunity for inducting mainstream biochemists and molecular biologists from the region into a greater level of awareness of the importance of bioinformatics in their craft. In this editorial, we provide a brief overview of the peer-reviewed manuscripts accepted for publication herein, grouped into thematic areas. As the regional research expertise in bioinformatics matures, the papers fall into thematic areas, illustrating the specific contributions made by APBioNet to global bioinformatics efforts. © 2012 BioMed Central Ltd.

关键词： bioinformatics

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Emerging strengths in Asia Pacific bioinformatics

Emerging strengths in Asia Pacific bioinformatics

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关键词： bioinformatics

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Protein subcellular localization prediction of eukaryotes using a knowledge-based approach

Protein subcellular localization prediction of eukaryotes us...

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8th international conference on bioinformatics: bioinformatics, incob 2009

作者： Lin, Hsin-Nan Chen, Ching-Tai Sung, Ting-Yi Ho, Shinn-Ying Hsu, Wen-Lian Bioinformatics Program Academia Sinica Taipei Taiwan Bioinformatics Lab. Institute of Information Science Academia Sinica Taipei Taiwan Institute of Bioinformatics National Chiao Tung University Hsinchu Taiwan

Background: the study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles. Results: In this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred site's performance. the experiment results show that KnowPredsite achieves higher prediction accuracy than ngLOC and Blast-hit method. For singlelocalized proteins, the overall accuracy of KnowPredsite is 91.7%. For multi-localized proteins, the overall accuracy of KnowPred site is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPredsite. Conclusion: KnowPredsite demonstrates the power of identifying re

关键词： Proteins

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So you think you can PLS-DA?

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BMC bioinformatics 2020年第1期21卷 1-10页

作者： Ruiz-Perez, Daniel Guan, Haibin Madhivanan, Purnima Mathee, Kalai Narasimhan, Giri Florida Int Univ Bioinformat Res Grp BioRG 11200 SW 8th St Miami FL 33199 USA Florida Int Univ Dept Epidemiol 11200 SW 8th St Miami FL 24105 USA Florida Int Univ Herbert Wertheim Coll Med 11200 SW 8th St Miami FL 24105 USA

BackgroundPartial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented, namely Principal Component Analysis (PCA).ResultsWe demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector. In some cases, it outperforms PLS-DA, which is made aware of the class labels in its input. Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models encountered when analyzing bioinformatics and clinical data. Other methods were also evaluated. Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the ground truth for the feature selection was available. All the 3D figures shown in this paper as well as the supplementary ones can be viewed interactively at http://***/plsdaConclusionsOur results highlighted the strengths and weaknesses of PLS-DA in comparison with PCA for different underlying data models.

关键词： PLS-DA PCA Feature selection Dimensionality reduction bioinformatics

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the 2017 Computational Structural bioinformatics Workshop 8

The 2017 Computational Structural Bioinformatics Workshop

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8th ACM international conference on bioinformatics, Computational Biology,and Health Informatics (ACM-BCB)

作者： Haspel, Nurit Shehu, Amarda Molloy, Kevin Univ Massachusetts Boston 100 Morrissey Blvd Boston MA 02125 USA George Mason Univ 4400 Univ Dr Fairfax VA 22030 USA

ISBN: (纸本)9781450347228

the rapid accumulation of macromolecular structures presents a unique set of challenges and opportunities in the analysis, comparison, modeling, and prediction of macromolecular structures and interactions. this workshop aims to bring together researchers with expertise in bioinformatics, computational biology, structural biology, data mining, optimization and high performance computing to discuss new results, techniques, and research problems in computational structural bioinformatics.

关键词： Computational structural biology structural bioinformatics computational biology

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Physicochemical property space distribution among human metabolites, drugs and toxins

Physicochemical property space distribution among human meta...

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8th international conference on bioinformatics: bioinformatics, incob 2009

作者： Khanna, Varun Ranganathan, Shoba Dept. of Chemistry and Biomolecular Sciences ARC Centre of Excellence in Bioinformatics Macquarie University Sydney Australia Dept. of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore

Background: the current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. the current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. this paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets. Results: We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. the distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets. Conclusion: the property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. the inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and

关键词： Metabolites

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Detecting robust time-delayed regulation in mycobacterium tuberculosis

Detecting robust time-delayed regulation in mycobacterium tu...

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8th international conference on bioinformatics: Computational Biology, incob 2009

作者： Chaturvedi, Iti Rajapakse, Jagath C. Bioinformatics Research Center School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA 02139 United States MIT Alliance Singapore 117543 Singapore

Background: Time delays are often found in gene regulation though most techniques of building gene regulatory networks are not capable of capturing such phenomena. Here we look at the delays in the DNA repair system of Mycobacterium tuberculosis which is unusually slow in the bacteria. We propose a method based on a skip-chain model to study this phenomena in gene networks. the Viterbi paths of the underlying Markov chains find the most likely regulatory interactions among genes, taking care of very long delays. Using the derived networks, we discuss the delayed regulations and robustness of the DNA damage seen in the bacterium. Results: We evaluated our method on time-course gene expressions after DNA damage with Mitocyin C. Several time-delayed interactions were observed with our analysis. the presence of hubs in the networks indicates that a small number of transcriptional factors regulate the rest of the system. We demonstrate the use of priors to overcome over-fitting problem in the generation of networks. We compare our results with the gene networks derived with dynamic Bayesian networks (DBN). Conclusion: Different transcription networks are active at different stages, and constant feedback and regulation is maintained throughout the activities of a biological pathway. Skip-chain models are capable of capturing, long distant and the time-delayed regulations. Use of a Dirichlet prior over parameters and Gibbs prior over structure can greatly reduce the over-fitting in the new model. © 2012 BioMed Central Ltd .

关键词： Bacteria

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A modular kernel approach for integrative analysis of protein domain boundaries

A modular kernel approach for integrative analysis of protei...

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8th international conference on bioinformatics: Computational Biology, incob 2009

作者： Yoo, Paul D. Zhou, Bing Bing Zomaya, Albert Y. Advanced Networks Research Group School of Information Technologies University of Sydney NSW 2006 Australia Sydney Bioinformatics Centre University of Sydney Sydney NSW 2006 Australia

Background: In this paper, we introduce a novel inter-range interaction integrated approach for protein domain boundary prediction. It involves (1) the design of modular kernel algorithm, which is able to effectively exploit the information of non-local interactions in amino acids, and (2) the development of a novel profile that can provide suitable information to the algorithm. One of the key features of this profiling technique is the use of multiple structural alignments of remote homologues to create an extended sequence profile and combines the structural information with suitable chemical information that plays an important role in protein stability. this profile can capture the sequence characteristics of an entire structural superfamily and extend a range of profiles generated from sequence similarity alone. Results: Our novel profile that combines homology information with hydrophobicity from SARAH1 scale was successful in providing more structural and chemical information. In addition, the modular approach adopted in our algorithm proved to be effective in capturing information from non-local interactions. Our approach achieved 82.1%, 50.9% and 31.5% accuracies for onedomain, two-domain, and three- and more domain proteins respectively. Conclusion: the experimental results in this study are encouraging, however, more work is need to extend it to a broader range of applications. We are currently developing a novel interactive (human in the loop) profiling that can provide information from more distantly related homology. this approach will further enhance the current study. © 2012 BioMed Central Ltd .

关键词： Proteins

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A high throughput approach to keep alive a web-based database system for multiple search among published bioinformatics tools and databases

A high throughput approach to keep alive a web-based databas...

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8th IEEE international conference on bioinformatics and Bioengineering

作者： Atlamazoglou, Vassilis thireou, Trias Alexandridou, Anastasia Spyrou, George Acad Athens Biomed Res Fdn Biomed Informat Unit 4 Soranou Ephessiou Athens 11527 Greece Natl Tech Univ Athens Sch Elect & comp Engn Athens Greece

ISBN: (纸本)9781424428441

Since modern Biology has been transformed to a quantitative science dealing with tremendous amounts of data, the applications of information science and technology on Biology, i.e. bioinformatics, are so man), that they comprise a separate dataset needing organization and indexing for optimum search and information retrieval. Metabasis, a web-based database system for organizing and maintaining information relevant to published bioinformatics tools and databases has been developed by our group. However, such an effort requires rapid and massive information update, an issue quite difficult since the bioinformatics tools production rate is very high and there is no standard protocol in the way the), are presented in the literature. We present here a high throughput automatic retrieval procedure to keep alive a database of this kind, using certain methodology for automatic downloading, parsing and filtering data from the related literature.

关键词： bioinformatics

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A multi-species comparative structural bioinformatics analysis of inherited mutations in α-D-Mannosidase reveals strong genotype-phenotype correlation

A multi-species comparative structural bioinformatics analys...

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8th international conference on bioinformatics: Computational Biology, incob 2009

作者： Khan, Javed Mohammed Ranganathan, Shoba Department of Chemistry and Biomolecular Sciences ARC Center of Excellence in Bioinformatics Macquarie University NSW 2109 Australia Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore 8 Medical Drive Singapore 117597 Singapore

Background: Lysosomal α-mannosidase is an enzyme that acts to degrade N-linked oligosaccharides and hence plays an important role in mannose metabolism in humans and other mammalian species, especially livestock. Mutations in the gene (MAN2B1) encoding lysosomal α-D-mannosidase cause improper coding, resulting in dysfunctional or non-functional protein, causing the disease α-mannosidosis. Mapping disease mutations to the structure of the protein can help in understanding the functional consequences of these mutations and thus indirectly, the finer aspects of the pathology and clinical manifestations of the disease, including phenotypic severity as a function of the genotype. Results: A comprehensive homology modeling study of all the wild-type and inherited mutations of lysosomal α-mannosidase in four different species, human, cow, cat and guinea pig, reveals a significant correlation between the severity of the genotype and the phenotype in α-mannosidosis. We used the X-ray crystallographic structure of bovine lysosomal α-mannosidase as template, containing only two disulphide bonds and some ligands, to build structural models of wild-type structures with four disulfide linkages and all bound ligands. these wild-type models were then used as templates for disease mutations. All the truncations and substitutions involving the residues in and around the active site and those that destabilize the fold led to severe genotypes resulting in lethal phenotypes, whereas the mutations lying away from the active site were milder in both their genotypic and phenotypic expression. Conclusion: Based on the co-location of mutations from different organisms and their proximity to the enzyme active site, we have extrapolated observed mutations from one species to homologous positions in other organisms, as a predictive approach for detecting likely α-mannosidosis. Besides predicting new disease mutations, this approach also provides a way for detecting mutation hotspots in the gene

关键词： Mammals

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