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Efficient experimental design for uncertainty reduction in gene regulatory networks.

BMC bioinformatics

Dehghannasiri R, Yoon BJ, Dougherty ER.
PMID: 26423515
BMC Bioinformatics. 2015;16:S2. doi: 10.1186/1471-2105-16-S13-S2. Epub 2015 Sep 25.

BACKGROUND: An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene...

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Dehghannasiri R, Yoon BJ, Dougherty ER. Efficient experimental design for uncertainty reduction in gene regulatory networks. BMC Bioinformatics. 2015;16:S2doi: 10.1186/1471-2105-16-S13-S2.
Dehghannasiri, R., Yoon, B. J., & Dougherty, E. R. (2015). Efficient experimental design for uncertainty reduction in gene regulatory networks. BMC bioinformatics, 16S2. https://doi.org/10.1186/1471-2105-16-S13-S2
Dehghannasiri, Roozbeh, et al. "Efficient experimental design for uncertainty reduction in gene regulatory networks." BMC bioinformatics vol. 16 (2015): S2. doi: https://doi.org/10.1186/1471-2105-16-S13-S2
Dehghannasiri R, Yoon BJ, Dougherty ER. Efficient experimental design for uncertainty reduction in gene regulatory networks. BMC Bioinformatics. 2015;16:S2. doi: 10.1186/1471-2105-16-S13-S2. Epub 2015 Sep 25. PMID: 26423515; PMCID: PMC4597030.
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Dynamical modeling of uncertain interaction-based genomic networks.

BMC bioinformatics

Mohsenizadeh DN, Hua J, Bittner M, Dougherty ER.
PMID: 26423606
BMC Bioinformatics. 2015;16:S3. doi: 10.1186/1471-2105-16-S13-S3. Epub 2015 Sep 25.

BACKGROUND: Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires...

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Mohsenizadeh DN, Hua J, Bittner M, et al. Dynamical modeling of uncertain interaction-based genomic networks. BMC Bioinformatics. 2015;16:S3doi: 10.1186/1471-2105-16-S13-S3.
Mohsenizadeh, D. N., Hua, J., Bittner, M., & Dougherty, E. R. (2015). Dynamical modeling of uncertain interaction-based genomic networks. BMC bioinformatics, 16S3. https://doi.org/10.1186/1471-2105-16-S13-S3
Mohsenizadeh, Daniel N, et al. "Dynamical modeling of uncertain interaction-based genomic networks." BMC bioinformatics vol. 16 (2015): S3. doi: https://doi.org/10.1186/1471-2105-16-S13-S3
Mohsenizadeh DN, Hua J, Bittner M, Dougherty ER. Dynamical modeling of uncertain interaction-based genomic networks. BMC Bioinformatics. 2015;16:S3. doi: 10.1186/1471-2105-16-S13-S3. Epub 2015 Sep 25. PMID: 26423606; PMCID: PMC4596957.
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Enzyme Annotation and Metabolic Reconstruction Using KEGG.

Methods in molecular biology (Clifton, N.J.)

Kanehisa M.
PMID: 28451977
Methods Mol Biol. 2017;1611:135-145. doi: 10.1007/978-1-4939-7015-5_11.

KEGG is an integrated database resource for linking sequences to biological functions from molecular to higher levels. Knowledge on molecular functions is stored in the KO (KEGG Orthology) database, while cellular- and organism-level functions are represented in the PATHWAY...

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Kanehisa M. Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods Mol Biol. 2017;1611:135-145doi: 10.1007/978-1-4939-7015-5_11.
Kanehisa, M. (2017). Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods in molecular biology (Clifton, N.J.), 1611135-145. https://doi.org/10.1007/978-1-4939-7015-5_11
Kanehisa, Minoru. "Enzyme Annotation and Metabolic Reconstruction Using KEGG." Methods in molecular biology (Clifton, N.J.) vol. 1611 (2017): 135-145. doi: https://doi.org/10.1007/978-1-4939-7015-5_11
Kanehisa M. Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods Mol Biol. 2017;1611:135-145. doi: 10.1007/978-1-4939-7015-5_11. PMID: 28451977.
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chopBAI: BAM index reduction solves I/O bottlenecks in the joint analysis of large sequencing cohorts.

Bioinformatics (Oxford, England)

Kehr B, Melsted P.
PMID: 27153590
Bioinformatics. 2016 Jul 15;32(14):2202-4. doi: 10.1093/bioinformatics/btw149. Epub 2016 Mar 18.

UNLABELLED: Advances in sequencing capacity have led to the generation of unprecedented amounts of genomic data. The processing of this data frequently leads to I/O bottlenecks, e. g. when analyzing a small genomic region across a large number of...

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Kehr B, Melsted P. chopBAI: BAM index reduction solves I/O bottlenecks in the joint analysis of large sequencing cohorts. Bioinformatics. 2016;32(14):2202-4doi: 10.1093/bioinformatics/btw149.
Kehr, B., & Melsted, P. (2016). chopBAI: BAM index reduction solves I/O bottlenecks in the joint analysis of large sequencing cohorts. Bioinformatics (Oxford, England), 32(14), 2202-4. https://doi.org/10.1093/bioinformatics/btw149
Kehr, Birte, and Melsted, Páll. "chopBAI: BAM index reduction solves I/O bottlenecks in the joint analysis of large sequencing cohorts." Bioinformatics (Oxford, England) vol. 32,14 (2016): 2202-4. doi: https://doi.org/10.1093/bioinformatics/btw149
Kehr B, Melsted P. chopBAI: BAM index reduction solves I/O bottlenecks in the joint analysis of large sequencing cohorts. Bioinformatics. 2016 Jul 15;32(14):2202-4. doi: 10.1093/bioinformatics/btw149. Epub 2016 Mar 18. PMID: 27153590.
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VCF-Explorer: filtering and analysing whole genome VCF files.

Bioinformatics (Oxford, England)

Akgün M, Demirci H.
PMID: 29036499
Bioinformatics. 2017 Nov 01;33(21):3468-3470. doi: 10.1093/bioinformatics/btx422.

SUMMARY: The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most...

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Akgün M, Demirci H. VCF-Explorer: filtering and analysing whole genome VCF files. Bioinformatics. 2017;33(21):3468-3470doi: 10.1093/bioinformatics/btx422.
Akgün, M., & Demirci, H. (2017). VCF-Explorer: filtering and analysing whole genome VCF files. Bioinformatics (Oxford, England), 33(21), 3468-3470. https://doi.org/10.1093/bioinformatics/btx422
Akgün, Mete, and Demirci, Hüseyin. "VCF-Explorer: filtering and analysing whole genome VCF files." Bioinformatics (Oxford, England) vol. 33,21 (2017): 3468-3470. doi: https://doi.org/10.1093/bioinformatics/btx422
Akgün M, Demirci H. VCF-Explorer: filtering and analysing whole genome VCF files. Bioinformatics. 2017 Nov 01;33(21):3468-3470. doi: 10.1093/bioinformatics/btx422. PMID: 29036499.
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Chronic disease prevention in primary care: how and when will genomics impact?.

The British journal of general practice : the journal of the Royal College of General Practitioners

Walter FM, Emery J, Burton H.
PMID: 24982473
Br J Gen Pract. 2014 Jul;64(624):331-2. doi: 10.3399/bjgp14X680401.

No abstract available.

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Walter FM, Emery J, Burton H. Chronic disease prevention in primary care: how and when will genomics impact?. Br J Gen Pract. 2014;64(624):331-2doi: 10.3399/bjgp14X680401.
Walter, F. M., Emery, J., & Burton, H. (2014). Chronic disease prevention in primary care: how and when will genomics impact?. The British journal of general practice : the journal of the Royal College of General Practitioners, 64(624), 331-2. https://doi.org/10.3399/bjgp14X680401
Walter, Fiona M, et al. "Chronic disease prevention in primary care: how and when will genomics impact?." The British journal of general practice : the journal of the Royal College of General Practitioners vol. 64,624 (2014): 331-2. doi: https://doi.org/10.3399/bjgp14X680401
Walter FM, Emery J, Burton H. Chronic disease prevention in primary care: how and when will genomics impact?. Br J Gen Pract. 2014 Jul;64(624):331-2. doi: 10.3399/bjgp14X680401. PMID: 24982473; PMCID: PMC4073706.
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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...

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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.
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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,...

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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.
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Quickly finding orthologs as reciprocal best hits with BLAT, LAST, and UBLAST: how much do we miss?.

PloS one

Ward N, Moreno-Hagelsieb G.
PMID: 25013894
PLoS One. 2014 Jul 11;9(7):e101850. doi: 10.1371/journal.pone.0101850. eCollection 2014.

Reciprocal Best Hits (RBH) are a common proxy for orthology in comparative genomics. Essentially, a RBH is found when the proteins encoded by two genes, each in a different genome, find each other as the best scoring match in...

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Ward N, Moreno-Hagelsieb G. Quickly finding orthologs as reciprocal best hits with BLAT, LAST, and UBLAST: how much do we miss?. PLoS One. 2014;9(7):e101850doi: 10.1371/journal.pone.0101850.
Ward, N., & Moreno-Hagelsieb, G. (2014). Quickly finding orthologs as reciprocal best hits with BLAT, LAST, and UBLAST: how much do we miss?. PloS one, 9(7), e101850. https://doi.org/10.1371/journal.pone.0101850
Ward, Natalie, and Moreno-Hagelsieb, Gabriel. "Quickly finding orthologs as reciprocal best hits with BLAT, LAST, and UBLAST: how much do we miss?." PloS one vol. 9,7 (2014): e101850. doi: https://doi.org/10.1371/journal.pone.0101850
Ward N, Moreno-Hagelsieb G. Quickly finding orthologs as reciprocal best hits with BLAT, LAST, and UBLAST: how much do we miss?. PLoS One. 2014 Jul 11;9(7):e101850. doi: 10.1371/journal.pone.0101850. eCollection 2014. PMID: 25013894; PMCID: PMC4094424.
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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...

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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.
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Genomics: the Power and the Promise.

Genome

Jimenez-Sanchez G.
PMID: 26642255
Genome. 2015 Dec;58(12):vii-x. doi: 10.1139/gen-2015-0143.

No abstract available.

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Jimenez-Sanchez G. Genomics: the Power and the Promise. Genome. 2015;58(12):vii-xdoi: 10.1139/gen-2015-0143.
Jimenez-Sanchez, G. (2015). Genomics: the Power and the Promise. Genome, 58(12), vii-x. https://doi.org/10.1139/gen-2015-0143
Jimenez-Sanchez, Gerardo. "Genomics: the Power and the Promise." Genome vol. 58,12 (2015): vii-x. doi: https://doi.org/10.1139/gen-2015-0143
Jimenez-Sanchez G. Genomics: the Power and the Promise. Genome. 2015 Dec;58(12):vii-x. doi: 10.1139/gen-2015-0143. French. PMID: 26642255.
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Integrative analysis of human omics data using biomolecular networks.

Molecular bioSystems

Robinson JL, Nielsen J.
PMID: 27510223
Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11.

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their...

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Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016;12(10):2953-64doi: 10.1039/c6mb00476h.
Robinson, J. L., & Nielsen, J. (2016). Integrative analysis of human omics data using biomolecular networks. Molecular bioSystems, 12(10), 2953-64. https://doi.org/10.1039/c6mb00476h
Robinson, Jonathan L, and Nielsen, Jens. "Integrative analysis of human omics data using biomolecular networks." Molecular bioSystems vol. 12,10 (2016): 2953-64. doi: https://doi.org/10.1039/c6mb00476h
Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11. PMID: 27510223.
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Showing 1 to 12 of 382 entries