carrascomj / drastic Goto Github PK
View Code? Open in Web Editor NEWDeep Rapid Annotation using Submodels Training in Cells
Deep Rapid Annotation using Submodels Training in Cells
We should account sometime to prepare the poster and coordinate the presentation.
The preprocessing task involves sampling from the genomes on varying length chunks.
minimum_size
of the chunks (maximum length of a gene)Which license should we use for the repository? I guess it's fine with a MIT or GNU license.
Christmas is coming and the deadline is looming! Please tell others if you are working on one of the tasks and edit this issue as you please.
pd.DataFrame
about theThe README should have the models and features we are going to accomplish.
Now that we have something working, we should prepare the notebooks and organize the different models:
.py
ready to be imported.src/data/
, ready to be imported.Having defined all of the pre-processing functions (embeddings not included), we need to define some API for the user to transform the raw data into a suitable format:
/bin
to go from paths to files (FASTA and feature table) to the pre-processed dataframe (maybe a CSV).notes while we work and develop models
first draft in latex
review 1
First model to consider for the first milestone.
Reference: Stephen Merity, Nitish Shirish Keskar, Richard Socher (2017). Regularizing and Optimizing LSTM Language Models.
Given that we have defined the functions for extracting labeled ranges from the data (#3), all that is left is to build a dataframe (I suppose in pandas) using these ranges and the genome sequence.
My first guess is that the dataset would contain two columns, one for the sequence and other for the label.
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