similar to: Finding words that are within +/- X words of "KRAS" using tm package or other means

Hello Everyone, I have what I think is a complex text parsing task. I've provided some sample data below. There's a relatively simple version of the coding that needs to be done and a more complex version. If someone could help me out with either version, I'd greatly appreciate it. Here are my sample data. haveData <- structure(list(profile_key = structure(c(1L, 1L, 2L, 2L, 2L,

Hello All, Can anyone tell help me understand why the function below doesn't work and how I can fix it? Below are some sample data, some code that works on individual rows of the data, and my attempt to translate that code into a function. My hope is to get the function working and then to apply it to the larger data frame using ddply() from the plyr package or possibly some other approach.

Happy Friday Everyone, ? Hope Friday afternoon doesn't turn out to be a terrible time to post a question. I've been doing a little data mining of patient text medical records as of late. I started out trying to predict whether or not cancer patients had received KRAS mutation testing and did quite well with that. Now I'm trying to predict the results of KRAS testing (mutated?vs. wild

Hello All, Started out awhile ago trying to select columns in a dataframe whose names contain some variation of the word "mutant" using code like: names(KRASyn)[grep("muta", names(KRASyn))] The idea then would be to add together the various columns using code like: KRASyn$Mutant_comb <- rowSums(KRASyn[grep("muta", names(KRASyn))]) What I discovered though, is

Hello, everybody. We are working on a tool for mutation testing. The work is still in progress and far away from being done. However, we have got some results already. And we would like to share them with you. But, let me give you a brief introduction first. ### Mutation Testing In a nutshell, Mutation Testing is a way to evaluate a quality of a test suite. The approach suggests introducing a

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BioInformatics Scientist Job Code: 10-TR25 Location: Cambridge, MA Description We are seeking a highly motivated, independent bioinformatics scientist to join a group of scientists, analysts and programmers to develop tools and methodologies for large-scale gene expression data analysis. The group supports a variety of research projects in target and drug discovery as well as biomarker

Hello, Apologies if this is the wrong list, I am a first-time poster here. I have an experiment in which an output is measured in response to 42 different categories. I am only interested which of the categories is significantly different from a reference category. Here is the summary of the results: summary(simple.fit) Call: lm(formula = as.numeric(as.vector(TNFa)) ~ Mutant.ID, data =

Hello all, Sorry if this is an FAQ. I have been trying to search the archives without success. I have a dataset (ChiPs microarray) where the experiment to experiment variability is very high but where within an experiment, the data nearly always goes in the "right" (hypothesis confirming) direction. I am trying to figure out the right way to use R to do the statistics and generate

Hello all, Sorry if this is an FAQ. I have been trying to search the archives without success. I have a dataset (ChiPs microarray) where the experiment to experiment variability is very high but where within an experiment, the data nearly always goes in the "right" (hypothesis confirming) direction. I am trying to figure out the right way to use R to do the statistical analysis and

Among others, datam contains the columns: logconc, tm, dose, subj, bilirubin. None of these are factor variables. The following compartment models work (the first still has not converged after 100 interations): res1 <- nlme(logconc~p2+p3+log(dose/(exp(p1)-exp(p2))* (exp(-exp(p2)*tm)-exp(-exp(p1)*tm))),start=list(fixed=c(5,-2,-0.1)), fixed=list(p1+p2+p3~1),control=list(maxIter=100),

Version of R: Windows Version 2.0.0 The experimental design contains two plant lines - a control (C) and a mutant (M) - grown out three separate times in plots A, B, C. The design is unbalanced: In plot A, 9 control plants were grown with 29 mutant plants. In plot B, 8 control plants were grown with 20 mutant plants. In plot C, 8 control plants were grown with 22 mutant plants. The

Hi, I am trying to plot the following data so that it can be visually represented well. I tried the dotchart but I felt it was too spread out. Then I tried the barplot which is good enough for me. Is there a way to give the labels for the y-axis as in the dot chart? Also, I feel the grey level is confusing, so is there options for designs within the bars? I cannot use color as the journal wants

mcga 1.1 (machine coded genetic algorithms) package implements a genetic algorithm optimisation tool with machine coded chromosomes. The machine coded chromosomes stand for chromosomes that are not decoded and encoded. The byte representation of 'double' type variables are crossed-over and mutated. This is different from the binary coded and real coded genetic algorithms. Linux and

mcga 1.1 (machine coded genetic algorithms) package implements a genetic algorithm optimisation tool with machine coded chromosomes. The machine coded chromosomes stand for chromosomes that are not decoded and encoded. The byte representation of 'double' type variables are crossed-over and mutated. This is different from the binary coded and real coded genetic algorithms. Linux and

Hello, I didn't give enough information when I sent an query before, so I'm trying again with a more detailed explanation: In this data set, each patient has a different number of measured variables (they represent tumors, so some people had 2 tumors, some had 5, etc). The problem I have is that often in later cycles for a patient, tumors that were originally measured are now missing (or

Dear R help, I am having a problem with the Design package and my problem is detailed here. I fit a cox model to my data and validate the Somer's Dxy using the Design package. (Because of computation time problem, i only try 10 bootstrap samples for the time being) This is the model without stratification: > library(Design) >

Dear All, I am a bit struggling with the many packages for Markov models available in R. Apologies for now posting a code snippet, but I am looking for some guidance here. Please consider a set like the one below (which you can get with data<-read.csv('http://dl.dropboxusercontent.com/u/5685598/data_table.csv') ). ID therapy age1 age2 EFS 7308 ormo_lunga 78

Hi, The problem is most likely, you need to call a R CMD BATCH with your arguments and the R-script inside of a shell script that you submit to your qsub. Unfortunately we don't use qsub anymore so can't test it, but it should be as follows: R-script eg. test.R: > ##First read in the arguments listed at the command line > args=(commandArgs(TRUE)) > > ##args is now a list of

Hey Paul, Thanks for the reply! If you wanted you could try my radio stream, http://stream.socorock.com:8000/socorock.ogg.m3u - that's really good news, I think I'm gonna pick up one of these things soon! What a great way to listen to radio. Cheers, Jordan On 02/26/2012 12:01 AM, Paul Webster wrote: > The Grace and Logitech devices can play streaming OGG. I have different models of