Posts Tagged ‘from’

Predicting peptide binding sites on protein surfaces by clustering chemical interactions – Yan – 2014 – Journal of Computational Chemistry – Wiley Online Library

July 4, 2016

Predicting peptide binding sites on protein surfaces by ACCLUSTER http://onlinelibrary.wiley.com/doi/10.1002/jcc.23771/abstract #Chemical interactions out perform pure #packing

Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes

July 2, 2016

#SingleCell Coexpression…Reveals Distinct Func. Modules [v clustering bulk RNAseq]… & Clinical Outcomes [in GBM]
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004892

QT:{{”
We found that the co-expressed genes observed in single cells and bulk tumors have little overlap and show distinct characteristics. The co-expressed genes identified in bulk tumors tend to have similar biological functions, and are enriched for intrachromosomal
interactions with synchronized promoter activity. In contrast, single-cell co-expressed genes are enriched for known protein-protein interactions, and are regulated through interchromosomal interactions. “}}

Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling: Cell

July 2, 2016

Kinome-wide Decoding of #Network-Attacking Mutations Rewiring Cancer http://www.cell.com/cell/abstract/S0092-8674(15)01108-3 Mapping NAMs onto well-known signaling pathways

Tempus – Buying Time

June 18, 2016

http://www.tempus.com/

The Independent Discovery of TCP/IP, By Ants

June 18, 2016

The Independent Discovery of TCP/IP, By Ants
http://priceonomics.com/the-independent-discovery-of-tcpip-by-ants/ Protocol has similarities to #ant communication; man reinventing nature

How does multiple testing correction work?

June 13, 2016

How does multiple-testing correction work
http://www.nature.com/nbt/journal/v27/n12/abs/nbt1209-1135.html Intuition for teaching: genome-wide error rate on a single gene v family

The role of regulatory variation in complex traits and disease : Nature Reviews Genetics : Nature Publishing Group

June 12, 2016

Reg. variation in cplx traits by @LeonidKruglyak
http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html nice teaching figure for #eQTLs, showing how mostly cis + hotspots http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html

Amazon.com: Ice to the Eskimos: How to Market a Product Nobody Wants eBook: Jon Spoelstra: Kindle Store

June 5, 2016

https://www.amazon.com/Ice-Eskimos-Market-Product-Nobody-ebook/dp/B000RO9VK4?ie=UTF8&btkr=1&ref_=dp-kindle-redirect

Genome-wide association study identifies 74 loci associated with educational attainment : Nature : Nature Publishing Group

May 21, 2016

GWAS identifies 74 loci associated w. educational attainment http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17671.html Described in 3 pgs of main text & 146 pgs of supplement

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17671.html

Lalonde E*, Ishkanian AS*, ….P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC, Bristow RG (2014) “Tumour genomic a nd microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” La ncet Oncology 15(13):1521-1532 (PMID: 25456371)

May 17, 2016

Genomic & microenvironmental heterogeneity as integrated predictors for prostate #cancer recurrence
http://www.ncbi.nlm.nih.gov/pubmed/25456371 CNVs & hypoxia

* Lalonde E*, Ishkanian AS*, ….P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC, Bristow RG (2014) “Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” Lancet Oncology 15(13):1521-1532 (PMID: 25456371)

The novelty of the paper is that it is the first study integrating DNA-based signatures and microenviroment-based signature for cancer prognosis. The authors found four prognostic indices, i.e. cancer genomic subtype (generated from clusters of CNV profiles), genomic instability (represented by the percentage of genome alteration), DNA signature (276 genes identified from random forests), and tumor hypoxia (the microenvironment signature), to be effective in predicting patient survival in different groups. Standard clinical univariate and multivariate analyses were performed.