Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 193 entries
Sorted by: Best Match Show Resources per page
Firmiana: towards a one-stop proteomic cloud platform for data processing and analysis.

Nature biotechnology

Feng J, Ding C, Qiu N, Ni X, Zhan D, Liu W, Xia X, Li P, Lu B, Zhao Q, Nie P, Song L, Zhou Q, Lai M, Guo G, Zhu W, Ren J, Shi T, Qin J.
PMID: 28486446
Nat Biotechnol. 2017 May 09;35(5):409-412. doi: 10.1038/nbt.3825.

No abstract available.

Cite
Feng J, Ding C, Qiu N, et al. Firmiana: towards a one-stop proteomic cloud platform for data processing and analysis. Nat Biotechnol. 2017;35(5):409-412doi: 10.1038/nbt.3825.
Feng, J., Ding, C., Qiu, N., Ni, X., Zhan, D., Liu, W., Xia, X., Li, P., Lu, B., Zhao, Q., Nie, P., Song, L., Zhou, Q., Lai, M., Guo, G., Zhu, W., Ren, J., Shi, T., & Qin, J. (2017). Firmiana: towards a one-stop proteomic cloud platform for data processing and analysis. Nature biotechnology, 35(5), 409-412. https://doi.org/10.1038/nbt.3825
Feng, Jinwen, et al. "Firmiana: towards a one-stop proteomic cloud platform for data processing and analysis." Nature biotechnology vol. 35,5 (2017): 409-412. doi: https://doi.org/10.1038/nbt.3825
Feng J, Ding C, Qiu N, Ni X, Zhan D, Liu W, Xia X, Li P, Lu B, Zhao Q, Nie P, Song L, Zhou Q, Lai M, Guo G, Zhu W, Ren J, Shi T, Qin J. Firmiana: towards a one-stop proteomic cloud platform for data processing and analysis. Nat Biotechnol. 2017 May 09;35(5):409-412. doi: 10.1038/nbt.3825. PMID: 28486446.
Copy Download .nbib Format: NLM
Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library.

PloS one

Murdock J, Allen C, Börner K, Light R, McAlister S, Ravenscroft A, Rose R, Rose D, Otsuka J, Bourget D, Lawrence J, Reed C.
PMID: 28922416
PLoS One. 2017 Sep 18;12(9):e0184188. doi: 10.1371/journal.pone.0184188. eCollection 2017.

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history...

Cite
Murdock J, Allen C, Börner K, et al. Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library. PLoS One. 2017;12(9):e0184188doi: 10.1371/journal.pone.0184188.
Murdock, J., Allen, C., Börner, K., Light, R., McAlister, S., Ravenscroft, A., Rose, R., Rose, D., Otsuka, J., Bourget, D., Lawrence, J., & Reed, C. (2017). Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library. PloS one, 12(9), e0184188. https://doi.org/10.1371/journal.pone.0184188
Murdock, Jaimie, et al. "Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library." PloS one vol. 12,9 (2017): e0184188. doi: https://doi.org/10.1371/journal.pone.0184188
Murdock J, Allen C, Börner K, Light R, McAlister S, Ravenscroft A, Rose R, Rose D, Otsuka J, Bourget D, Lawrence J, Reed C. Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library. PLoS One. 2017 Sep 18;12(9):e0184188. doi: 10.1371/journal.pone.0184188. eCollection 2017. PMID: 28922416; PMCID: PMC5602542.
Copy Download .nbib Format: NLM
A heuristic approach to determine an appropriate number of topics in topic modeling.

BMC bioinformatics

Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W.
PMID: 26424364
BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25.

BACKGROUND: Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics...

Cite
Zhao W, Chen JJ, Perkins R, et al. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8doi: 10.1186/1471-2105-16-S13-S8.
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC bioinformatics, 16S8. https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao, Weizhong, et al. "A heuristic approach to determine an appropriate number of topics in topic modeling." BMC bioinformatics vol. 16 (2015): S8. doi: https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25. PMID: 26424364; PMCID: PMC4597325.
Copy Download .nbib Format: NLM
Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database.

Studies in health technology and informatics

Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M.
PMID: 26262371
Stud Health Technol Inform. 2015;216:1072.

The aim of this study was to investigate whether standard Big Data mining methods lead to clinically useful results. An association analysis was performed using the apriori algorithm to discover associations among co-morbidities of diabetes patients. Selected data were...

Cite
Nienhoff H, Huebner U, Frey A, et al. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072
Nienhoff, H., Huebner, U., Frey, A., Przysucha, M., & Kimura, M. (2015). Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Studies in health technology and informatics, 2161072.
Nienhoff, Hendrik, et al. "Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database." Studies in health technology and informatics vol. 216 (2015): 1072.
Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072. PMID: 26262371.
Copy Download .nbib Format: NLM
BioCreative V CDR task corpus: a resource for chemical disease relation extraction.

Database : the journal of biological databases and curation

Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z.
PMID: 27161011
Database (Oxford). 2016 May 09;2016. doi: 10.1093/database/baw068. Print 2016.

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation...

Cite
Li J, Sun Y, Johnson RJ, et al. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford). 2016;2016doi: 10.1093/database/baw068.
Li, J., Sun, Y., Johnson, R. J., Sciaky, D., Wei, C. H., Leaman, R., Davis, A. P., Mattingly, C. J., Wiegers, T. C., & Lu, Z. (2016). BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database : the journal of biological databases and curation, 2016. https://doi.org/10.1093/database/baw068
Li, Jiao, et al. "BioCreative V CDR task corpus: a resource for chemical disease relation extraction." Database : the journal of biological databases and curation vol. 2016 (2016). doi: https://doi.org/10.1093/database/baw068
Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford). 2016 May 09;2016. doi: 10.1093/database/baw068. Print 2016. PMID: 27161011; PMCID: PMC4860626.
Copy Download .nbib Format: NLM
High-throughput translational medicine: challenges and solutions.

Advances in experimental medicine and biology

Sulakhe D, Balasubramanian S, Xie B, Berrocal E, Feng B, Taylor A, Chitturi B, Dave U, Agam G, Xu J, Börnigen D, Dubchak I, Gilliam TC, Maltsev N.
PMID: 24292961
Adv Exp Med Biol. 2014;799:39-67. doi: 10.1007/978-1-4614-8778-4_3.

Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and...

Cite
Sulakhe D, Balasubramanian S, Xie B, et al. High-throughput translational medicine: challenges and solutions. Adv Exp Med Biol. 2014;799:39-67doi: 10.1007/978-1-4614-8778-4_3.
Sulakhe, D., Balasubramanian, S., Xie, B., Berrocal, E., Feng, B., Taylor, A., Chitturi, B., Dave, U., Agam, G., Xu, J., Börnigen, D., Dubchak, I., Gilliam, T. C., & Maltsev, N. (2014). High-throughput translational medicine: challenges and solutions. Advances in experimental medicine and biology, 79939-67. https://doi.org/10.1007/978-1-4614-8778-4_3
Sulakhe, Dinanath, et al. "High-throughput translational medicine: challenges and solutions." Advances in experimental medicine and biology vol. 799 (2014): 39-67. doi: https://doi.org/10.1007/978-1-4614-8778-4_3
Sulakhe D, Balasubramanian S, Xie B, Berrocal E, Feng B, Taylor A, Chitturi B, Dave U, Agam G, Xu J, Börnigen D, Dubchak I, Gilliam TC, Maltsev N. High-throughput translational medicine: challenges and solutions. Adv Exp Med Biol. 2014;799:39-67. doi: 10.1007/978-1-4614-8778-4_3. PMID: 24292961.
Copy Download .nbib Format: NLM
Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data.

PloS one

Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT, Girard FP, Stephens RM.
PMID: 24312478
PLoS One. 2013 Dec 02;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013.

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not...

Cite
Mudunuri US, Khouja M, Repetski S, et al. Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PLoS One. 2013;8(12):e80503doi: 10.1371/journal.pone.0080503.
Mudunuri, U. S., Khouja, M., Repetski, S., Venkataraman, G., Che, A., Luke, B. T., Girard, F. P., & Stephens, R. M. (2013). Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PloS one, 8(12), e80503. https://doi.org/10.1371/journal.pone.0080503
Mudunuri, Uma S, et al. "Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data." PloS one vol. 8,12 (2013): e80503. doi: https://doi.org/10.1371/journal.pone.0080503
Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT, Girard FP, Stephens RM. Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data. PLoS One. 2013 Dec 02;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013. PMID: 24312478; PMCID: PMC3846626.
Copy Download .nbib Format: NLM
Identification of genomic features in the classification of loss- and gain-of-function mutation.

BMC medical informatics and decision making

Jung S, Lee S, Kim S, Nam H.
PMID: 26043747
BMC Med Inform Decis Mak. 2015;15:S6. doi: 10.1186/1472-6947-15-S1-S6. Epub 2015 May 20.

BACKGROUND: Alterations of a genome can lead to changes in protein functions. Through these genetic mutations, a protein can lose its native function (loss-of-function, LoF), or it can confer a new function (gain-of-function, GoF). However, when a mutation occurs,...

Cite
Jung S, Lee S, Kim S, et al. Identification of genomic features in the classification of loss- and gain-of-function mutation. BMC Med Inform Decis Mak. 2015;15:S6doi: 10.1186/1472-6947-15-S1-S6.
Jung, S., Lee, S., Kim, S., & Nam, H. (2015). Identification of genomic features in the classification of loss- and gain-of-function mutation. BMC medical informatics and decision making, 15S6. https://doi.org/10.1186/1472-6947-15-S1-S6
Jung, Seunghwan, et al. "Identification of genomic features in the classification of loss- and gain-of-function mutation." BMC medical informatics and decision making vol. 15 (2015): S6. doi: https://doi.org/10.1186/1472-6947-15-S1-S6
Jung S, Lee S, Kim S, Nam H. Identification of genomic features in the classification of loss- and gain-of-function mutation. BMC Med Inform Decis Mak. 2015;15:S6. doi: 10.1186/1472-6947-15-S1-S6. Epub 2015 May 20. PMID: 26043747; PMCID: PMC4460711.
Copy Download .nbib Format: NLM
Search wide, dig deep: literature searching for qualitative research. An analysis of the publication formats and information sources used for four systematic reviews in public health.

Research synthesis methods

Stansfield C, Brunton G, Rees R.
PMID: 26052653
Res Synth Methods. 2014 Jun;5(2):142-51. doi: 10.1002/jrsm.1100. Epub 2013 Oct 25.

BACKGROUND: When literature searching for systematic reviews, it is good practice to search widely across different information sources. Little is known about the contributions of different publication formats (e.g. journal article and book chapter) and sources, especially for studies...

Cite
Stansfield C, Brunton G, Rees R. Search wide, dig deep: literature searching for qualitative research. An analysis of the publication formats and information sources used for four systematic reviews in public health. Res Synth Methods. 2013;5(2):142-51doi: 10.1002/jrsm.1100.
Stansfield, C., Brunton, G., & Rees, R. (2014). Search wide, dig deep: literature searching for qualitative research. An analysis of the publication formats and information sources used for four systematic reviews in public health. Research synthesis methods, 5(2), 142-51. https://doi.org/10.1002/jrsm.1100
Stansfield, Claire, et al. "Search wide, dig deep: literature searching for qualitative research. An analysis of the publication formats and information sources used for four systematic reviews in public health." Research synthesis methods vol. 5,2 (2014): 142-51. doi: https://doi.org/10.1002/jrsm.1100
Stansfield C, Brunton G, Rees R. Search wide, dig deep: literature searching for qualitative research. An analysis of the publication formats and information sources used for four systematic reviews in public health. Res Synth Methods. 2014 Jun;5(2):142-51. doi: 10.1002/jrsm.1100. Epub 2013 Oct 25. PMID: 26052653.
Copy Download .nbib Format: NLM
DWFS: a wrapper feature selection tool based on a parallel genetic algorithm.

PloS one

Soufan O, Kleftogiannis D, Kalnis P, Bajic VB.
PMID: 25719748
PLoS One. 2015 Feb 26;10(2):e0117988. doi: 10.1371/journal.pone.0117988. eCollection 2015.

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification...

Cite
Soufan O, Kleftogiannis D, Kalnis P, et al. DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PLoS One. 2015;10(2):e0117988doi: 10.1371/journal.pone.0117988.
Soufan, O., Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2015). DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PloS one, 10(2), e0117988. https://doi.org/10.1371/journal.pone.0117988
Soufan, Othman, et al. "DWFS: a wrapper feature selection tool based on a parallel genetic algorithm." PloS one vol. 10,2 (2015): e0117988. doi: https://doi.org/10.1371/journal.pone.0117988
Soufan O, Kleftogiannis D, Kalnis P, Bajic VB. DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PLoS One. 2015 Feb 26;10(2):e0117988. doi: 10.1371/journal.pone.0117988. eCollection 2015. PMID: 25719748; PMCID: PMC4342225.
Copy Download .nbib Format: NLM
HTML5 PivotViewer: high-throughput visualization and querying of image data on the web.

Bioinformatics (Oxford, England)

Taylor S, Noble R.
PMID: 24849578
Bioinformatics. 2014 Sep 15;30(18):2691-2. doi: 10.1093/bioinformatics/btu349. Epub 2014 May 21.

MOTIVATION: Visualization and analysis of large numbers of biological images has generated a bottle neck in research. We present HTML5 PivotViewer, a novel, open source, platform-independent viewer making use of the latest web technologies that allows seamless access to...

Cite
Taylor S, Noble R. HTML5 PivotViewer: high-throughput visualization and querying of image data on the web. Bioinformatics. 2014;30(18):2691-2doi: 10.1093/bioinformatics/btu349.
Taylor, S., & Noble, R. (2014). HTML5 PivotViewer: high-throughput visualization and querying of image data on the web. Bioinformatics (Oxford, England), 30(18), 2691-2. https://doi.org/10.1093/bioinformatics/btu349
Taylor, Stephen, and Noble, Roger. "HTML5 PivotViewer: high-throughput visualization and querying of image data on the web." Bioinformatics (Oxford, England) vol. 30,18 (2014): 2691-2. doi: https://doi.org/10.1093/bioinformatics/btu349
Taylor S, Noble R. HTML5 PivotViewer: high-throughput visualization and querying of image data on the web. Bioinformatics. 2014 Sep 15;30(18):2691-2. doi: 10.1093/bioinformatics/btu349. Epub 2014 May 21. PMID: 24849578; PMCID: PMC4155252.
Copy Download .nbib Format: NLM
Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections.

IEEE transactions on visualization and computer graphics

Cappers BCM, van Wijk JJ.
PMID: 28866582
IEEE Trans Vis Comput Graph. 2018 Jan;24(1):532-541. doi: 10.1109/TVCG.2017.2745278. Epub 2017 Aug 29.

Multivariate event sequences are ubiquitous: travel history, telecommunication conversations, and server logs are some examples. Besides standard properties such as type and timestamp, events often have other associated multivariate data. Current exploration and analysis methods either focus on the...

Cite
Cappers BCM, van Wijk JJ. Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections. IEEE Trans Vis Comput Graph. 2017;24(1):532-541doi: 10.1109/TVCG.2017.2745278.
Cappers, B. C. M., & van Wijk, J. J. (2018). Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections. IEEE transactions on visualization and computer graphics, 24(1), 532-541. https://doi.org/10.1109/TVCG.2017.2745278
Cappers, Bram C M, and van Wijk, Jarke J. "Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections." IEEE transactions on visualization and computer graphics vol. 24,1 (2018): 532-541. doi: https://doi.org/10.1109/TVCG.2017.2745278
Cappers BCM, van Wijk JJ. Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections. IEEE Trans Vis Comput Graph. 2018 Jan;24(1):532-541. doi: 10.1109/TVCG.2017.2745278. Epub 2017 Aug 29. PMID: 28866582.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 193 entries