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Showing 1 to 12 of 20 entries
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Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment.

Proceedings. IEEE International Conference on Data Mining

Liu R, Cheng W, Tong H, Wang W, Zhang X.
PMID: 27239167
Proc IEEE Int Conf Data Min. 2015 Nov;2015:291-300. doi: 10.1109/ICDM.2015.13.

Network clustering is an important problem that has recently drawn a lot of attentions. Most existing work focuses on clustering nodes within a single network. In many applications, however, there exist

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Liu R, Cheng W, Tong H, et al. Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment. Proc IEEE Int Conf Data Min. 2015;2015:291-300doi: 10.1109/ICDM.2015.13.
Liu, R., Cheng, W., Tong, H., Wang, W., & Zhang, X. (2015). Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment. Proceedings. IEEE International Conference on Data Mining, 2015291-300. https://doi.org/10.1109/ICDM.2015.13
Liu, Rui, et al. "Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment." Proceedings. IEEE International Conference on Data Mining vol. 2015 (2015): 291-300. doi: https://doi.org/10.1109/ICDM.2015.13
Liu R, Cheng W, Tong H, Wang W, Zhang X. Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment. Proc IEEE Int Conf Data Min. 2015 Nov;2015:291-300. doi: 10.1109/ICDM.2015.13. PMID: 27239167; PMCID: PMC4880426.
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Mutation effect estimation on protein-protein interactions using deep contextualized representation learning.

NAR genomics and bioinformatics

Zhou G, Chen M, Ju CJT, Wang Z, Jiang JY, Wang W.
PMID: 32166223
NAR Genom Bioinform. 2020 Jun;2(2):lqaa015. doi: 10.1093/nargab/lqaa015. Epub 2020 Mar 05.

The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts...

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Zhou G, Chen M, Ju CJT, et al. Mutation effect estimation on protein-protein interactions using deep contextualized representation learning. NAR Genom Bioinform. 2020;2(2):lqaa015doi: 10.1093/nargab/lqaa015.
Zhou, G., Chen, M., Ju, C. J. T., Wang, Z., Jiang, J. Y., & Wang, W. (2020). Mutation effect estimation on protein-protein interactions using deep contextualized representation learning. NAR genomics and bioinformatics, 2(2), lqaa015. https://doi.org/10.1093/nargab/lqaa015
Zhou, Guangyu, et al. "Mutation effect estimation on protein-protein interactions using deep contextualized representation learning." NAR genomics and bioinformatics vol. 2,2 (2020): lqaa015. doi: https://doi.org/10.1093/nargab/lqaa015
Zhou G, Chen M, Ju CJT, Wang Z, Jiang JY, Wang W. Mutation effect estimation on protein-protein interactions using deep contextualized representation learning. NAR Genom Bioinform. 2020 Jun;2(2):lqaa015. doi: 10.1093/nargab/lqaa015. Epub 2020 Mar 05. PMID: 32166223; PMCID: PMC7059401.
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The reactome pathway knowledgebase 2022.

Nucleic acids research

Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, Griss J, Sevilla C, Matthews L, Gong C, Deng C, Varusai T, Ragueneau E, Haider Y, May B, Shamovsky V, Weiser J, Brunson T, Sanati N, Beckman L, Shao X, Fabregat A, Sidiropoulos K, Murillo J, Viteri G, Cook J, Shorser S, Bader G, Demir E, Sander C, Haw R, Wu G, Stein L, Hermjakob H, D'Eustachio P.
PMID: 34788843
Nucleic Acids Res. 2022 Jan 07;50:D687-D692. doi: 10.1093/nar/gkab1028.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered...

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Gillespie M, Jassal B, Stephan R, et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022;50:D687-D692doi: 10.1093/nar/gkab1028.
Gillespie, M., Jassal, B., Stephan, R., Milacic, M., Rothfels, K., Senff-Ribeiro, A., Griss, J., Sevilla, C., Matthews, L., Gong, C., Deng, C., Varusai, T., Ragueneau, E., Haider, Y., May, B., Shamovsky, V., Weiser, J., Brunson, T., Sanati, N., Beckman, L., Shao, X., Fabregat, A., Sidiropoulos, K., Murillo, J., Viteri, G., Cook, J., Shorser, S., Bader, G., Demir, E., Sander, C., Haw, R., Wu, G., Stein, L., Hermjakob, H., & D'Eustachio, P. (2022). The reactome pathway knowledgebase 2022. Nucleic acids research, 50D687-D692. https://doi.org/10.1093/nar/gkab1028
Gillespie, Marc, et al. "The reactome pathway knowledgebase 2022." Nucleic acids research vol. 50 (2022): D687-D692. doi: https://doi.org/10.1093/nar/gkab1028
Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, Griss J, Sevilla C, Matthews L, Gong C, Deng C, Varusai T, Ragueneau E, Haider Y, May B, Shamovsky V, Weiser J, Brunson T, Sanati N, Beckman L, Shao X, Fabregat A, Sidiropoulos K, Murillo J, Viteri G, Cook J, Shorser S, Bader G, Demir E, Sander C, Haw R, Wu G, Stein L, Hermjakob H, D'Eustachio P. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022 Jan 07;50:D687-D692. doi: 10.1093/nar/gkab1028. PMID: 34788843; PMCID: PMC8689983.
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Cardiovascular proteomics in the era of big data: experimental and computational advances.

Clinical proteomics

Lam MP, Lau E, Ng DC, Wang D, Ping P.
PMID: 27980500
Clin Proteomics. 2016 Dec 05;13:23. doi: 10.1186/s12014-016-9124-y. eCollection 2016.

Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac...

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Lam MP, Lau E, Ng DC, et al. Cardiovascular proteomics in the era of big data: experimental and computational advances. Clin Proteomics. 2016;13:23doi: 10.1186/s12014-016-9124-y.
Lam, M. P., Lau, E., Ng, D. C., Wang, D., & Ping, P. (2016). Cardiovascular proteomics in the era of big data: experimental and computational advances. Clinical proteomics, 1323. https://doi.org/10.1186/s12014-016-9124-y
Lam, Maggie P Y, et al. "Cardiovascular proteomics in the era of big data: experimental and computational advances." Clinical proteomics vol. 13 (2016): 23. doi: https://doi.org/10.1186/s12014-016-9124-y
Lam MP, Lau E, Ng DC, Wang D, Ping P. Cardiovascular proteomics in the era of big data: experimental and computational advances. Clin Proteomics. 2016 Dec 05;13:23. doi: 10.1186/s12014-016-9124-y. eCollection 2016. PMID: 27980500; PMCID: PMC5137214.
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Glycoproteomic Profiling Provides Candidate Myocardial Infarction Predictors of Later Progression to Heart Failure.

ACS omega

Deleon-Pennell KY, Ero OK, Ma Y, Padmanabhan Iyer R, Flynn ER, Espinoza I, Musani SK, Vasan RS, Hall ME, Fox ER, Lindsey ML.
PMID: 30729226
ACS Omega. 2019 Jan 31;4(1):1272-1280. doi: 10.1021/acsomega.8b02207. Epub 2019 Jan 15.

We hypothesized that identifying plasma glycoproteins that predict the development of heart failure following myocardial infarction (MI) could help to stratify subjects at risk. Plasma collected at visit 2 (2005-2008) from an MI subset of Jackson Heart Study participants...

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Deleon-Pennell KY, Ero OK, Ma Y, et al. Glycoproteomic Profiling Provides Candidate Myocardial Infarction Predictors of Later Progression to Heart Failure. ACS Omega. 2019;4(1):1272-1280doi: 10.1021/acsomega.8b02207.
Deleon-Pennell, K. Y., Ero, O. K., Ma, Y., Padmanabhan Iyer, R., Flynn, E. R., Espinoza, I., Musani, S. K., Vasan, R. S., Hall, M. E., Fox, E. R., & Lindsey, M. L. (2019). Glycoproteomic Profiling Provides Candidate Myocardial Infarction Predictors of Later Progression to Heart Failure. ACS omega, 4(1), 1272-1280. https://doi.org/10.1021/acsomega.8b02207
Deleon-Pennell, Kristine Y, et al. "Glycoproteomic Profiling Provides Candidate Myocardial Infarction Predictors of Later Progression to Heart Failure." ACS omega vol. 4,1 (2019): 1272-1280. doi: https://doi.org/10.1021/acsomega.8b02207
Deleon-Pennell KY, Ero OK, Ma Y, Padmanabhan Iyer R, Flynn ER, Espinoza I, Musani SK, Vasan RS, Hall ME, Fox ER, Lindsey ML. Glycoproteomic Profiling Provides Candidate Myocardial Infarction Predictors of Later Progression to Heart Failure. ACS Omega. 2019 Jan 31;4(1):1272-1280. doi: 10.1021/acsomega.8b02207. Epub 2019 Jan 15. PMID: 30729226; PMCID: PMC6356850.
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Using the laws of thermodynamics to understand how matrix metalloproteinases coordinate the myocardial response to injury.

Metalloproteinases in medicine

Iyer RP, Jung M, Lindsey ML.
PMID: 27376092
Metalloproteinases Med. 2015;2:75-82. doi: 10.2147/MNM.S74093. Epub 2015 Oct 30.

Following myocardial infarction (MI), the left ventricle (LV) undergoes a series of molecular, cellular, and functional alterations that are both part of the wound healing response to form a scar in the infarct region and the consequence of that...

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Iyer RP, Jung M, Lindsey ML. Using the laws of thermodynamics to understand how matrix metalloproteinases coordinate the myocardial response to injury. Metalloproteinases Med. 2015;2:75-82doi: 10.2147/MNM.S74093.
Iyer, R. P., Jung, M., & Lindsey, M. L. (2015). Using the laws of thermodynamics to understand how matrix metalloproteinases coordinate the myocardial response to injury. Metalloproteinases in medicine, 275-82. https://doi.org/10.2147/MNM.S74093
Iyer, Rugmani Padmanabhan, et al. "Using the laws of thermodynamics to understand how matrix metalloproteinases coordinate the myocardial response to injury." Metalloproteinases in medicine vol. 2 (2015): 75-82. doi: https://doi.org/10.2147/MNM.S74093
Iyer RP, Jung M, Lindsey ML. Using the laws of thermodynamics to understand how matrix metalloproteinases coordinate the myocardial response to injury. Metalloproteinases Med. 2015;2:75-82. doi: 10.2147/MNM.S74093. Epub 2015 Oct 30. PMID: 27376092; PMCID: PMC4930103.
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Citizen Science for Mining the Biomedical Literature.

Citizen science : theory and practice

Tsueng G, Nanis SM, Fouquier J, Good BM, Su AI.
PMID: 30416754
Citiz Sci. 2016;1(2). doi: 10.5334/cstp.56. Epub 2016 Dec 31.

Biomedical literature represents one of the largest and fastest growing collections of unstructured biomedical knowledge. Finding critical information buried in the literature can be challenging. To extract information from free-flowing text, researchers need to: 1. identify the entities in...

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Tsueng G, Nanis SM, Fouquier J, et al. Citizen Science for Mining the Biomedical Literature. Citiz Sci. 2016;1(2)doi: 10.5334/cstp.56.
Tsueng, G., Nanis, S. M., Fouquier, J., Good, B. M., & Su, A. I. (2016). Citizen Science for Mining the Biomedical Literature. Citizen science : theory and practice, 1(2), . https://doi.org/10.5334/cstp.56
Tsueng, Ginger, et al. "Citizen Science for Mining the Biomedical Literature." Citizen science : theory and practice vol. 1,2 (2016). doi: https://doi.org/10.5334/cstp.56
Tsueng G, Nanis SM, Fouquier J, Good BM, Su AI. Citizen Science for Mining the Biomedical Literature. Citiz Sci. 2016;1(2). doi: 10.5334/cstp.56. Epub 2016 Dec 31. PMID: 30416754; PMCID: PMC6226017.
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Temporally Factorized Network Modeling for Evolutionary Network Analysis.

Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining

Yu W, Aggarwal CC, Wang W.
PMID: 28626845
Proc Int Conf Web Search Data Min. 2017 Feb;2017:455-464. doi: 10.1145/3018661.3018669.

The problem of evolutionary network analysis has gained increasing attention in recent years, because of an increasing number of networks, which are encountered in temporal settings. For example, social networks, communication networks, and information networks continuously evolve over time,...

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Yu W, Aggarwal CC, Wang W. Temporally Factorized Network Modeling for Evolutionary Network Analysis. Proc Int Conf Web Search Data Min. 2017;2017:455-464doi: 10.1145/3018661.3018669.
Yu, W., Aggarwal, C. C., & Wang, W. (2017). Temporally Factorized Network Modeling for Evolutionary Network Analysis. Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining, 2017455-464. https://doi.org/10.1145/3018661.3018669
Yu, Wenchao, et al. "Temporally Factorized Network Modeling for Evolutionary Network Analysis." Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining vol. 2017 (2017): 455-464. doi: https://doi.org/10.1145/3018661.3018669
Yu W, Aggarwal CC, Wang W. Temporally Factorized Network Modeling for Evolutionary Network Analysis. Proc Int Conf Web Search Data Min. 2017 Feb;2017:455-464. doi: 10.1145/3018661.3018669. PMID: 28626845; PMCID: PMC5470848.
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Approximating Isotope Distributions of Biomolecule Fragments.

ACS omega

Goldfarb D, Lafferty MJ, Herring LE, Wang W, Major MB.
PMID: 30288463
ACS Omega. 2018 Sep 30;3(9):11383-11391. doi: 10.1021/acsomega.8b01649. Epub 2018 Sep 19.

In mass spectrometry (MS)-based proteomics, protein and peptide sequences are determined by the isolation and subsequent fragmentation of precursor ions. When an isolation window captures only part of a precursor's isotopic distribution, the isotope distributions of the fragments depend...

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Goldfarb D, Lafferty MJ, Herring LE, et al. Approximating Isotope Distributions of Biomolecule Fragments. ACS Omega. 2018;3(9):11383-11391doi: 10.1021/acsomega.8b01649.
Goldfarb, D., Lafferty, M. J., Herring, L. E., Wang, W., & Major, M. B. (2018). Approximating Isotope Distributions of Biomolecule Fragments. ACS omega, 3(9), 11383-11391. https://doi.org/10.1021/acsomega.8b01649
Goldfarb, Dennis, et al. "Approximating Isotope Distributions of Biomolecule Fragments." ACS omega vol. 3,9 (2018): 11383-11391. doi: https://doi.org/10.1021/acsomega.8b01649
Goldfarb D, Lafferty MJ, Herring LE, Wang W, Major MB. Approximating Isotope Distributions of Biomolecule Fragments. ACS Omega. 2018 Sep 30;3(9):11383-11391. doi: 10.1021/acsomega.8b01649. Epub 2018 Sep 19. PMID: 30288463; PMCID: PMC6166224.
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The Implicitome: A Resource for Rationalizing Gene-Disease Associations.

PloS one

Hettne KM, Thompson M, van Haagen HH, van der Horst E, Kaliyaperumal R, Mina E, Tatum Z, Laros JF, van Mulligen EM, Schuemie M, Aten E, Li TS, Bruskiewich R, Good BM, Su AI, Kors JA, den Dunnen J, van Ommen GJ, Roos M, 't Hoen PA, Mons B, Schultes EA.
PMID: 26919047
PLoS One. 2016 Feb 26;11(2):e0149621. doi: 10.1371/journal.pone.0149621. eCollection 2016.

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production...

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Hettne KM, Thompson M, van Haagen HH, et al. The Implicitome: A Resource for Rationalizing Gene-Disease Associations. PLoS One. 2016;11(2):e0149621doi: 10.1371/journal.pone.0149621.
Hettne, K. M., Thompson, M., van Haagen, H. H., van der Horst, E., Kaliyaperumal, R., Mina, E., Tatum, Z., Laros, J. F., van Mulligen, E. M., Schuemie, M., Aten, E., Li, T. S., Bruskiewich, R., Good, B. M., Su, A. I., Kors, J. A., den Dunnen, J., van Ommen, G. J., Roos, M., 't Hoen, P. A., Mons, B., & Schultes, E. A. (2016). The Implicitome: A Resource for Rationalizing Gene-Disease Associations. PloS one, 11(2), e0149621. https://doi.org/10.1371/journal.pone.0149621
Hettne, Kristina M, et al. "The Implicitome: A Resource for Rationalizing Gene-Disease Associations." PloS one vol. 11,2 (2016): e0149621. doi: https://doi.org/10.1371/journal.pone.0149621
Hettne KM, Thompson M, van Haagen HH, van der Horst E, Kaliyaperumal R, Mina E, Tatum Z, Laros JF, van Mulligen EM, Schuemie M, Aten E, Li TS, Bruskiewich R, Good BM, Su AI, Kors JA, den Dunnen J, van Ommen GJ, Roos M, 't Hoen PA, Mons B, Schultes EA. The Implicitome: A Resource for Rationalizing Gene-Disease Associations. PLoS One. 2016 Feb 26;11(2):e0149621. doi: 10.1371/journal.pone.0149621. eCollection 2016. PMID: 26919047; PMCID: PMC4769089.
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Exploring applications of crowdsourcing to cryo-EM.

Journal of structural biology

Bruggemann J, Lander GC, Su AI.
PMID: 29486249
J Struct Biol. 2018 Jul;203(1):37-45. doi: 10.1016/j.jsb.2018.02.006. Epub 2018 Feb 24.

Extraction of particles from cryo-electron microscopy (cryo-EM) micrographs is a crucial step in processing single-particle datasets. Although algorithms have been developed for automatic particle picking, these algorithms generally rely on two-dimensional templates for particle identification, which may exhibit biases...

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Bruggemann J, Lander GC, Su AI. Exploring applications of crowdsourcing to cryo-EM. J Struct Biol. 2018;203(1):37-45doi: 10.1016/j.jsb.2018.02.006.
Bruggemann, J., Lander, G. C., & Su, A. I. (2018). Exploring applications of crowdsourcing to cryo-EM. Journal of structural biology, 203(1), 37-45. https://doi.org/10.1016/j.jsb.2018.02.006
Bruggemann, Jacob, et al. "Exploring applications of crowdsourcing to cryo-EM." Journal of structural biology vol. 203,1 (2018): 37-45. doi: https://doi.org/10.1016/j.jsb.2018.02.006
Bruggemann J, Lander GC, Su AI. Exploring applications of crowdsourcing to cryo-EM. J Struct Biol. 2018 Jul;203(1):37-45. doi: 10.1016/j.jsb.2018.02.006. Epub 2018 Feb 24. PMID: 29486249; PMCID: PMC6086358.
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Heterogeneous network embedding enabling accurate disease association predictions.

BMC medical genomics

Xiong Y, Guo M, Ruan L, Kong X, Tang C, Zhu Y, Wang W.
PMID: 31865913
BMC Med Genomics. 2019 Dec 23;12:186. doi: 10.1186/s12920-019-0623-3.

BACKGROUND: It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic...

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Xiong Y, Guo M, Ruan L, et al. Heterogeneous network embedding enabling accurate disease association predictions. BMC Med Genomics. 2019;12:186doi: 10.1186/s12920-019-0623-3.
Xiong, Y., Guo, M., Ruan, L., Kong, X., Tang, C., Zhu, Y., & Wang, W. (2019). Heterogeneous network embedding enabling accurate disease association predictions. BMC medical genomics, 12186. https://doi.org/10.1186/s12920-019-0623-3
Xiong, Yun, et al. "Heterogeneous network embedding enabling accurate disease association predictions." BMC medical genomics vol. 12 (2019): 186. doi: https://doi.org/10.1186/s12920-019-0623-3
Xiong Y, Guo M, Ruan L, Kong X, Tang C, Zhu Y, Wang W. Heterogeneous network embedding enabling accurate disease association predictions. BMC Med Genomics. 2019 Dec 23;12:186. doi: 10.1186/s12920-019-0623-3. PMID: 31865913; PMCID: PMC6927100.
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Showing 1 to 12 of 20 entries