Segal cites: Measurements of 1000s of…designed promoters
http://www.nature.com/nbt/journal/v30/n6/full/nbt.2205.html Num. binding sites correlated w/ expr., for 1st few #ICSG2015
Segal cites: Determinants of TF binding outside the core binding site http://genome.cshlp.org/content/early/2015/06/05/gr.185033.114.abstract Large-scale measurement of affinity #ICSG2015
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
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
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?
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
Nice graph of seq. machine output v time
http://www.cell.com/molecular-cell/abstract/S1097-2765(15)00340-8
#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.
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