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Showing 1 to 12 of 390 entries
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Finding biomarkers in non-model species: literature mining of transcription factors involved in bovine embryo development.

BioData mining

Turenne N, Tiys E, Ivanisenko V, Yudin N, Ignatieva E, Valour D, Degrelle SA, Hue I.
PMID: 22931563
BioData Min. 2012 Aug 29;5(1):12. doi: 10.1186/1756-0381-5-12.

BACKGROUND: Since processes in well-known model organisms have specific features different from those in Bos taurus, the organism under study, a good way to describe gene regulation in ruminant embryos would be a species-specific consideration of closely related species...

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Turenne N, Tiys E, Ivanisenko V, et al. Finding biomarkers in non-model species: literature mining of transcription factors involved in bovine embryo development. BioData Min. 2012;5(1):12doi: 10.1186/1756-0381-5-12.
Turenne, N., Tiys, E., Ivanisenko, V., Yudin, N., Ignatieva, E., Valour, D., Degrelle, S. A., & Hue, I. (2012). Finding biomarkers in non-model species: literature mining of transcription factors involved in bovine embryo development. BioData mining, 5(1), 12. https://doi.org/10.1186/1756-0381-5-12
Turenne, Nicolas, et al. "Finding biomarkers in non-model species: literature mining of transcription factors involved in bovine embryo development." BioData mining vol. 5,1 (2012): 12. doi: https://doi.org/10.1186/1756-0381-5-12
Turenne N, Tiys E, Ivanisenko V, Yudin N, Ignatieva E, Valour D, Degrelle SA, Hue I. Finding biomarkers in non-model species: literature mining of transcription factors involved in bovine embryo development. BioData Min. 2012 Aug 29;5(1):12. doi: 10.1186/1756-0381-5-12. PMID: 22931563; PMCID: PMC3563503.
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Prediction of relevant biomedical documents: a human microbiome case study.

BioData mining

Thompson P, Madan JC, Moore JH.
PMID: 26361503
BioData Min. 2015 Sep 10;8:28. doi: 10.1186/s13040-015-0061-5. eCollection 2015.

BACKGROUND: Retrieving relevant biomedical literature has become increasingly difficult due to the large volume and rapid growth of biomedical publication. A query to a biomedical retrieval system often retrieves hundreds of results. Since the searcher will not likely consider...

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Thompson P, Madan JC, Moore JH. Prediction of relevant biomedical documents: a human microbiome case study. BioData Min. 2015;8:28doi: 10.1186/s13040-015-0061-5.
Thompson, P., Madan, J. C., & Moore, J. H. (2015). Prediction of relevant biomedical documents: a human microbiome case study. BioData mining, 828. https://doi.org/10.1186/s13040-015-0061-5
Thompson, Paul, et al. "Prediction of relevant biomedical documents: a human microbiome case study." BioData mining vol. 8 (2015): 28. doi: https://doi.org/10.1186/s13040-015-0061-5
Thompson P, Madan JC, Moore JH. Prediction of relevant biomedical documents: a human microbiome case study. BioData Min. 2015 Sep 10;8:28. doi: 10.1186/s13040-015-0061-5. eCollection 2015. PMID: 26361503; PMCID: PMC4564977.
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Study of Meta-analysis strategies for network inference using information-theoretic approaches.

BioData mining

Pham NC, Haibe-Kains B, Bellot P, Bontempi G, Meyer PE.
PMID: 28484519
BioData Min. 2017 May 06;10:15. doi: 10.1186/s13040-017-0136-6. eCollection 2017.

BACKGROUND: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from...

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Pham NC, Haibe-Kains B, Bellot P, et al. Study of Meta-analysis strategies for network inference using information-theoretic approaches. BioData Min. 2017;10:15doi: 10.1186/s13040-017-0136-6.
Pham, N. C., Haibe-Kains, B., Bellot, P., Bontempi, G., & Meyer, P. E. (2017). Study of Meta-analysis strategies for network inference using information-theoretic approaches. BioData mining, 1015. https://doi.org/10.1186/s13040-017-0136-6
Pham, Ngoc C, et al. "Study of Meta-analysis strategies for network inference using information-theoretic approaches." BioData mining vol. 10 (2017): 15. doi: https://doi.org/10.1186/s13040-017-0136-6
Pham NC, Haibe-Kains B, Bellot P, Bontempi G, Meyer PE. Study of Meta-analysis strategies for network inference using information-theoretic approaches. BioData Min. 2017 May 06;10:15. doi: 10.1186/s13040-017-0136-6. eCollection 2017. PMID: 28484519; PMCID: PMC5420410.
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Erratum to: BioDB extractor: customized data extraction system for commonly used bioinformatics databases.

BioData mining

Karbhal R, Sawant S, Kulkarni-Kale U.
PMID: 26843893
BioData Min. 2016 Feb 02;9:8. doi: 10.1186/s13040-016-0081-9. eCollection 2016.

[This corrects the article DOI: 10.1186/s13040-015-0067-z.].

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Karbhal R, Sawant S, Kulkarni-Kale U. Erratum to: BioDB extractor: customized data extraction system for commonly used bioinformatics databases. BioData Min. 2016;9:8doi: 10.1186/s13040-016-0081-9.
Karbhal, R., Sawant, S., & Kulkarni-Kale, U. (2016). Erratum to: BioDB extractor: customized data extraction system for commonly used bioinformatics databases. BioData mining, 98. https://doi.org/10.1186/s13040-016-0081-9
Karbhal, Rajiv, et al. "Erratum to: BioDB extractor: customized data extraction system for commonly used bioinformatics databases." BioData mining vol. 9 (2016): 8. doi: https://doi.org/10.1186/s13040-016-0081-9
Karbhal R, Sawant S, Kulkarni-Kale U. Erratum to: BioDB extractor: customized data extraction system for commonly used bioinformatics databases. BioData Min. 2016 Feb 02;9:8. doi: 10.1186/s13040-016-0081-9. eCollection 2016. PMID: 26843893; PMCID: PMC4738769.
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Data integration to prioritize drugs using genomics and curated data.

BioData mining

Louhimo R, Laakso M, Belitskin D, Klefström J, Lehtonen R, Hautaniemi S.
PMID: 27231484
BioData Min. 2016 May 26;9:21. doi: 10.1186/s13040-016-0097-1. eCollection 2016.

BACKGROUND: Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a...

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Louhimo R, Laakso M, Belitskin D, et al. Data integration to prioritize drugs using genomics and curated data. BioData Min. 2016;9:21doi: 10.1186/s13040-016-0097-1.
Louhimo, R., Laakso, M., Belitskin, D., Klefström, J., Lehtonen, R., & Hautaniemi, S. (2016). Data integration to prioritize drugs using genomics and curated data. BioData mining, 921. https://doi.org/10.1186/s13040-016-0097-1
Louhimo, Riku, et al. "Data integration to prioritize drugs using genomics and curated data." BioData mining vol. 9 (2016): 21. doi: https://doi.org/10.1186/s13040-016-0097-1
Louhimo R, Laakso M, Belitskin D, Klefström J, Lehtonen R, Hautaniemi S. Data integration to prioritize drugs using genomics and curated data. BioData Min. 2016 May 26;9:21. doi: 10.1186/s13040-016-0097-1. eCollection 2016. PMID: 27231484; PMCID: PMC4881054.
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Considerations for higher efficiency and productivity in research activities.

BioData mining

Forero DA, Moore JH.
PMID: 27833658
BioData Min. 2016 Nov 09;9:35. doi: 10.1186/s13040-016-0115-3. eCollection 2016.

There are several factors that are known to affect research productivity; some of them imply the need for large financial investments and others are related to work styles. There are some articles that provide suggestions for early career scientists...

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Forero DA, Moore JH. Considerations for higher efficiency and productivity in research activities. BioData Min. 2016;9:35doi: 10.1186/s13040-016-0115-3.
Forero, D. A., & Moore, J. H. (2016). Considerations for higher efficiency and productivity in research activities. BioData mining, 935. https://doi.org/10.1186/s13040-016-0115-3
Forero, Diego A, and Moore, Jason H. "Considerations for higher efficiency and productivity in research activities." BioData mining vol. 9 (2016): 35. doi: https://doi.org/10.1186/s13040-016-0115-3
Forero DA, Moore JH. Considerations for higher efficiency and productivity in research activities. BioData Min. 2016 Nov 09;9:35. doi: 10.1186/s13040-016-0115-3. eCollection 2016. PMID: 27833658; PMCID: PMC5103420.
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Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads.

BioData mining

Liang M, Raley C, Zheng X, Kutty G, Gogineni E, Sherman BT, Sun Q, Chen X, Skelly T, Jones K, Stephens R, Zhou B, Lau W, Johnson C, Imamichi T, Jiang M, Dewar R, Lempicki RA, Tran B, Kovacs JA, Huang DW.
PMID: 27051465
BioData Min. 2016 Apr 05;9:13. doi: 10.1186/s13040-016-0090-8. eCollection 2016.

BACKGROUND: Gene isoforms are commonly found in both prokaryotes and eukaryotes. Since each isoform may perform a specific function in response to changing environmental conditions, studying the dynamics of gene isoforms is important in understanding biological processes and disease...

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Liang M, Raley C, Zheng X, et al. Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads. BioData Min. 2016;9:13doi: 10.1186/s13040-016-0090-8.
Liang, M., Raley, C., Zheng, X., Kutty, G., Gogineni, E., Sherman, B. T., Sun, Q., Chen, X., Skelly, T., Jones, K., Stephens, R., Zhou, B., Lau, W., Johnson, C., Imamichi, T., Jiang, M., Dewar, R., Lempicki, R. A., Tran, B., Kovacs, J. A., & Huang, D. W. (2016). Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads. BioData mining, 913. https://doi.org/10.1186/s13040-016-0090-8
Liang, Ma, et al. "Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads." BioData mining vol. 9 (2016): 13. doi: https://doi.org/10.1186/s13040-016-0090-8
Liang M, Raley C, Zheng X, Kutty G, Gogineni E, Sherman BT, Sun Q, Chen X, Skelly T, Jones K, Stephens R, Zhou B, Lau W, Johnson C, Imamichi T, Jiang M, Dewar R, Lempicki RA, Tran B, Kovacs JA, Huang DW. Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads. BioData Min. 2016 Apr 05;9:13. doi: 10.1186/s13040-016-0090-8. eCollection 2016. PMID: 27051465; PMCID: PMC4820869.
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BioDB extractor: customized data extraction system for commonly used bioinformatics databases.

BioData mining

Karbhal R, Sawant S, Kulkarni-Kale U.
PMID: 26516349
BioData Min. 2015 Oct 28;8:31. doi: 10.1186/s13040-015-0067-z. eCollection 2015.

BACKGROUND: Diverse types of biological data, primary as well as derived, are available in various formats and are stored in heterogeneous resources. Database-specific as well as integrated search engines are available for carrying out efficient searches of databases. These...

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Karbhal R, Sawant S, Kulkarni-Kale U. BioDB extractor: customized data extraction system for commonly used bioinformatics databases. BioData Min. 2015;8:31doi: 10.1186/s13040-015-0067-z.
Karbhal, R., Sawant, S., & Kulkarni-Kale, U. (2015). BioDB extractor: customized data extraction system for commonly used bioinformatics databases. BioData mining, 831. https://doi.org/10.1186/s13040-015-0067-z
Karbhal, Rajiv, et al. "BioDB extractor: customized data extraction system for commonly used bioinformatics databases." BioData mining vol. 8 (2015): 31. doi: https://doi.org/10.1186/s13040-015-0067-z
Karbhal R, Sawant S, Kulkarni-Kale U. BioDB extractor: customized data extraction system for commonly used bioinformatics databases. BioData Min. 2015 Oct 28;8:31. doi: 10.1186/s13040-015-0067-z. eCollection 2015. PMID: 26516349; PMCID: PMC4624652.
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A feature selection method based on multiple kernel learning with expression profiles of different types.

BioData mining

Du W, Cao Z, Song T, Li Y, Liang Y.
PMID: 28184251
BioData Min. 2017 Feb 02;10:4. doi: 10.1186/s13040-017-0124-x. eCollection 2017.

BACKGROUND: With the development of high-throughput technology, the researchers can acquire large number of expression data with different types from several public databases. Because most of these data have small number of samples and hundreds or thousands features, how...

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Du W, Cao Z, Song T, et al. A feature selection method based on multiple kernel learning with expression profiles of different types. BioData Min. 2017;10:4doi: 10.1186/s13040-017-0124-x.
Du, W., Cao, Z., Song, T., Li, Y., & Liang, Y. (2017). A feature selection method based on multiple kernel learning with expression profiles of different types. BioData mining, 104. https://doi.org/10.1186/s13040-017-0124-x
Du, Wei, et al. "A feature selection method based on multiple kernel learning with expression profiles of different types." BioData mining vol. 10 (2017): 4. doi: https://doi.org/10.1186/s13040-017-0124-x
Du W, Cao Z, Song T, Li Y, Liang Y. A feature selection method based on multiple kernel learning with expression profiles of different types. BioData Min. 2017 Feb 02;10:4. doi: 10.1186/s13040-017-0124-x. eCollection 2017. PMID: 28184251; PMCID: PMC5288949.
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Next generation informatics for big data in precision medicine era.

BioData mining

Zhang Y, Zhu Q, Liu H.
PMID: 26539249
BioData Min. 2015 Nov 03;8:34. doi: 10.1186/s13040-015-0064-2. eCollection 2015.

The rise of data-intensive biology, advances in informatics technology, and changes in the way health care is delivered has created an compelling opportunity to allow us investigate biomedical questions in the context of "big data" and develop knowledge systems...

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Zhang Y, Zhu Q, Liu H. Next generation informatics for big data in precision medicine era. BioData Min. 2015;8:34doi: 10.1186/s13040-015-0064-2.
Zhang, Y., Zhu, Q., & Liu, H. (2015). Next generation informatics for big data in precision medicine era. BioData mining, 834. https://doi.org/10.1186/s13040-015-0064-2
Zhang, Yuji, et al. "Next generation informatics for big data in precision medicine era." BioData mining vol. 8 (2015): 34. doi: https://doi.org/10.1186/s13040-015-0064-2
Zhang Y, Zhu Q, Liu H. Next generation informatics for big data in precision medicine era. BioData Min. 2015 Nov 03;8:34. doi: 10.1186/s13040-015-0064-2. eCollection 2015. PMID: 26539249; PMCID: PMC4632361.
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GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.

BioData mining

Urbanowicz RJ, Kiralis J, Sinnott-Armstrong NA, Heberling T, Fisher JM, Moore JH.
PMID: 23025260
BioData Min. 2012 Oct 01;5(1):16. doi: 10.1186/1756-0381-5-16.

BACKGROUND: Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of...

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Urbanowicz RJ, Kiralis J, Sinnott-Armstrong NA, et al. GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Min. 2012;5(1):16doi: 10.1186/1756-0381-5-16.
Urbanowicz, R. J., Kiralis, J., Sinnott-Armstrong, N. A., Heberling, T., Fisher, J. M., & Moore, J. H. (2012). GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData mining, 5(1), 16. https://doi.org/10.1186/1756-0381-5-16
Urbanowicz, Ryan J, et al. "GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures." BioData mining vol. 5,1 (2012): 16. doi: https://doi.org/10.1186/1756-0381-5-16
Urbanowicz RJ, Kiralis J, Sinnott-Armstrong NA, Heberling T, Fisher JM, Moore JH. GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Min. 2012 Oct 01;5(1):16. doi: 10.1186/1756-0381-5-16. PMID: 23025260; PMCID: PMC3605108.
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DNA microarray integromics analysis platform.

BioData mining

Waller T, Gubała T, Sarapata K, Piwowar M, Jurkowski W.
PMID: 26110022
BioData Min. 2015 Jun 25;8:18. doi: 10.1186/s13040-015-0052-6. eCollection 2015.

BACKGROUND: The study of interactions between molecules belonging to different biochemical families (such as lipids and nucleic acids) requires specialized data analysis methods. This article describes the DNA Microarray Integromics Analysis Platform, a unique web application that focuses on...

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Waller T, Gubała T, Sarapata K, et al. DNA microarray integromics analysis platform. BioData Min. 2015;8:18doi: 10.1186/s13040-015-0052-6.
Waller, T., Gubała, T., Sarapata, K., Piwowar, M., & Jurkowski, W. (2015). DNA microarray integromics analysis platform. BioData mining, 818. https://doi.org/10.1186/s13040-015-0052-6
Waller, Tomasz, et al. "DNA microarray integromics analysis platform." BioData mining vol. 8 (2015): 18. doi: https://doi.org/10.1186/s13040-015-0052-6
Waller T, Gubała T, Sarapata K, Piwowar M, Jurkowski W. DNA microarray integromics analysis platform. BioData Min. 2015 Jun 25;8:18. doi: 10.1186/s13040-015-0052-6. eCollection 2015. PMID: 26110022; PMCID: PMC4479227.
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Showing 1 to 12 of 390 entries