Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening

Overview

Data and Results used in the publication entitled "Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening"

Data

This folder contains the raw data used during this work.

`EvoEF.csv` contains information on the library used (sequences, number of mutations, etc.) and the fitness (energy) used as continuous mean values. `mut.csv` contains the information about the combinatorial scaling (N vs N_norm), the number of mutations (m) and the probability of each variant using different distributions (uniform and binomial) at different $p_{WT}$.

For further details on how the fitness values were calculated and how the combinatorial scale works, please refer to our prevoius [Paper](https://arxiv.org/abs/2405.05167).

Results

This folder contains the results (outputs) of all scripts used. Such results are included in the form of `.npy` and `.npz` files. To load such files with numpy you should include the option `allow_pickle=True`.