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

<div> <h2>Overview</h2> <div>Data and Results used in the publication entitled "Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening"</div> </div> <div> <h3><strong>Data</strong></h3> <div>This folder contains the raw data used during this work.</div> <div>`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 $p_{WT}$.</div> <div>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).</div> <div>&nbsp;</div> <h3><strong>Results</strong></h3> <div> <div>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`.</div> </div> </div>