Archive for the 'SciLit' Category

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Structural insights into mis-regulation of protein kinase A in human tumors

June 8, 2015

Hendrickson cites: Structural insights into mis-regulation of PKA in…tumorshttp://www.pnas.org/content/112/5/1374 SNV stops reg. domain binding #ICSG2015

In other case showed recurring DnaJ–PKA fusion didn’t have coding effect but non-coding effect on promotor

vol. 112 no. 5
1374–1379, doi: 10.1073/pnas.1424206112
Structural insights into mis-regulation of protein kinase A in human tumors

Jonah Cheunga,1,
Christopher Gintera,
Michael Cassidya,
Matthew C. Franklina,
Michael J. Rudolpha,
Nicolas Robineb,
Robert B. Darnellb,c,d, and
Wayne A. Hendricksona,e,1

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PLOS Computational Biology: Exploring the Evolution of Novel Enzyme Functions within Structurally Defined Protein Superfamilies

June 7, 2015

Evolution of…Enzyme Functions w/in Structural…Superfamilies http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002403 Most changes involve substrates rather than chemistry

develops functional change matrix…
based on 276 fams

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High hopes

June 1, 2015

High hopes http://www.sciencemag.org/content/345/6192/18.summary Resurgence interest in the medical benefits of #psychedelics. Is it possible to get them without the high?

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Similarity network fusion for aggregating data types on a genomic scale : Nature Methods : Nature Publishing Group

June 1, 2015

Similarity #network fusion for aggregating data types http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2810.html Combines mRNA, miRNA & gene fusions to classify cancer subtypes
http://compbio.cs.toronto.edu/SNF/SNF

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Found this review

May 31, 2015

Nice graph of seq. machine output v time

http://www.cell.com/molecular-cell/abstract/S1097-2765(15)00340-8

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Machine learning applications in genetics and genomics : Nature Reviews Genetics : Nature Publishing Group

May 30, 2015

#Machinelearning applications in…genomics
http://www.nature.com/nrg/journal/v16/n6/full/nrg3920.html Nice overview of key distinctions betw generative & discriminative models

In their review, “Machine learning in genetics and genomics”, Libbrecht and Noble overview important aspects of application of machine learning to genomic data. The review presents illustrative classical genomics problems where machine learning techniques have proven useful and describes the differences between supervised, semi-supervised and unsupervised learning as well as generative and discriminative models. The authors discuss considerations that should be made when selecting the right machine learning approach depending on the biological problem and data at hand, provide general practical guidelines and suggest possible solutions to common challenges.

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Identification of Asthma Phenotypes Using Cluster Analysis in the Severe Asthma Research Program (ATS Journals)

May 30, 2015

Identification of #Asthma Phenotypes Using Cluster Analysis
http://www.atsjournals.org/doi/abs/10.1164/rccm.200906-0896OC#.VWk-gFxViko 5 canonical groups based on lung function, meds usage, &c

Canonical clustering of asthmatic patients into different groups

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Noninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma (ATS Journals)

May 30, 2015

Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma http://www.atsjournals.org/doi/abs/10.1164/rccm.201408-1440OC Consistent blood expression patterns

Noninvasive Analysis of the Sputum Transcriptome Discriminates
Clinical Phenotypes of Asthma (ATS Journals)

Yan, X., Chu, J.-H., Gomez, J., Koenigs, M., Holm, C., He, X., Perez,
M. F., Zhao, H., Mane, S., Martinez, F. D., Ober, C., Nicolae, D. L.,
Barnes, K. C., London, S. J., Gilliland, F., Weiss, S. T., Raby, B.
A., Cohn, L., and Chupp, G. L. “Non-Invasive Analysis of the Sputum
Transcriptome Discriminates Clinical Phenotypes of Asthma” American
Journal of Respiratory and Critical Care Medicine (2015):
doi:10.1164/rccm.201408-1440OC,

QT:{{"
Conclusions: There are common patterns of gene expression in the
sputum and blood of children and adults that are associated with near
fatal, severe and milder asthma.
"}}

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Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding

May 25, 2015

Mtn gorilla genomes reveal…impact of long-term…inbreeding http://www.sciencemag.org/content/348/6231/242 Pop. variation so low that very deleterious SNPs purged

Science 10 April 2015:
Vol. 348 no. 6231 pp. 242-245
DOI: 10.1126/science.aaa3952

Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding

Yali Xue1,*,
Javier Prado-Martinez2,*,
Peter H. Sudmant3,*,
…
Tomas Marques-Bonet2,12,
Chris Tyler-Smith1,†,
Aylwyn Scally13,†

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ALK Mutations Confer Differential Oncogenic Activation and Sensitivity to ALK Inhibition Therapy in Neuroblastoma: Cancer Cell

May 22, 2015

ALK Mutations Confer Differential Oncogenic Activation
http://www.cell.com/cancer-cell/abstract/S1535-6108%2814%2900393-6 MD modeling better assessing #SNV impact than stats, ie sift

ALK Mutations Confer Differential Oncogenic Activation and Sensitivity to ALK Inhibition Therapy in Neuroblastoma

Scott C. Bresler
Daniel A. Weiser
Peter J. Huwe
…
Ravi Radhakrishnan
Mark A. Lemmon
Yaël P. Mossé

DOI: http://dx.doi.org/10.1016/j.ccell.2014.09.019

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