R for science

Insilicos can be your data analysis partner on your next scientific project. We have an extensive analysis background, particularly in the R language. We also conduct projects using MATLAB and Octave.

Big Data

Biology and some other sciences can now generate large amounts of data, even for a single experiment. Meta experiments that aggregate data several experiments contend with even larger data.

Insilicos can help you deal with big data problems. We have experience using strategies such as cloud computing and GPU computing to address big data.

Wide Data

We have experience applying a variety of analyses to wide data, such as genomic and proteomic data and many other kinds of biological data. Wide data is data a large number of variables –sometimes vastly more variables than instances. Often, the critical challenge with wide data is variable selection: selecting a parsimonious set of variables that can be used to model the data. Insilicos personnel are pioneers in the application of Least Angle Regression (LARS) as a variable selection technique.

Tall Data

We also have experience with analysis of tall data, where there are a large number of instances (subjects, measurements, or similar). Ensemble learning is a computationally efficient method for deriving highly accurate models from tall data. We have extensive experience using ensemble learning techniques such as random forests to analyze and classify tall experiments. Insilicos Cloud Army is a cloud-based framework for R computing, and is particularly well-suited to ensemble learning.

For more information on how Insilicos can work with you on R science projects, contact us.