Help Needed - Building a model from scratch for predicting the outcome of a bio-process
Hey Happy People,I’m completely new to huggingface and so far I’m super excited and fascinated. I dont have too much experience with modeling, but am really interested in learning more.I would love to hear some tips and advices to my problem from you!So I’m currently working on a project (in University as a student) in which we are producing an enzyme in a fermentation process. We can judge the quality of the product by a measuring method which gives a spectrum with different peaks.There will be 8 different batches (with different cultivation parameters). Every batch is represented as a time series dataset with about 7-8 features and about 400 single datapoints. To every batch I will have 7 spectra which will allow me to evaluate the quality of the product. The 7 spectra a linked to different timestamps in the dataset.I want to build a model which is capable of predicting the outcome of a batch, which means predicting the spectrum measurement, so the quality of the product. Based only on the starting parameters.There is one big question arising for me.How do I get to train the model properly? I need a target value for every datapoint, how can I do that? There is the possibility to interpolate the missing data points, but I think this might not be it. Also there is something called Gaussian processes with which you can do that, but I still need to get to know about this stuff better. Do you have any Ideas on how to deal with such a dataset?What would your general approaches to this situation be and do you have any suggestions to what kind of model would be suitable for such a task?(Also people have used hybrid forms of mechanistical- and ML-models to predict biological processes. If someone has useful information on how to integrate something like this, I would be happy to hear that.)