You may use gsub function
> c <- "ce7382"
> gsub("[a-zA-Z ]", "", c)
Feel free to add other characters you need to remove to the regexp and / or to cast the result to number with as.numeric.
Determine the function of genes and the elements that regulate genes
throughout the genome.
Find variations in the DNA sequence among people and determine their
significance. The most common type of genetic variation is known as a
single nucleotide polymorphism or SNP (pronounced “snip”). These
small differences may help predict a person’s risk of ...
Does that mean that my model (or indeed my approach of using an AE) is ineffective
Well, it depends. Auto Encoder are a quite broad field, there are many hyperparameters to tune, width, depth, loss function, optimizer, epochs.
How should I proceed?
From my gut feeling I would say that you don't have enough data to train the AE properly. Keep in Mind, ...
It seems like your problem is not a typical "time series" problem: in data science and related problems, we normally look at evenly-spaced time series (that is, a measurement every interval, with each interval being the same length, such as 1 second, 3 months, 24 microseconds, etc).
There exist methods for working with events that can take some random time ...
It would be helpful if you described your dataset more. Gene expression datasets seem to often have very high dimensionality and Lasso regularized logistic regression is a popular method to approach this problem. This paper takes it a little further and might help you out:
The first question about missing data is always why is it missing?
Have you checked or know why the data is missing and whether it is MAR, MCAR or not missing at random?
If your data is MCAR imputation is generally fine and your lower test metric might simply indicate a suboptimal imputation strategy. In this case you could try MICE or similar more advanced ...
First you should define a metric that suits the problem $R^2$ in your case.
Do a correct cross-validation and train test splits.
And then choose in the cross validation which option has the best results for your model (imputing missing or xgboost no imputing). This way you are doing an empirical experiment and selecting the best result.
Probably you want to ...
So, the direct answer here is clearly NO.
The answer comes from the definitions of classification and regression. In a classification task what a model predicts is the probability of an instance to belong to a class (e.g. 'image with clouds' vs 'image without clouds' ), in regression you are trying to predict continuous values (e.g. the level of '...
If the undesired characters are constant as in the example, like ce7380 where the ce is unwanted, one may try the following:
df <- df %>%
mutate_at("INTERACTOR_A", str_replace, "ce", "")
This instructs R to perform the mutation function in the column INTERACTOR_A and replace the constant ce with ...
Certificates (is "for research use only" ok for you?)
There is no "common" or "average" genome, some of the prices are for bacterial some for human(4,6 x 10 ^6 bp (E.coli) vs. 3.3 x 10^9 bp(human))
You may build models to classify genomes by population.
Run unsupervised learning (clustering) to see if populations are reconstructed in the model.
Build models to infer missing genotypes
To do a Scalable DNA analysis you may check Adam software based on Apache Spark