# Algorithm History

FIBERS was originally based on the [RARE](https://github.com/UrbsLab/RARE) algorithm, an evolutionary algorithm for rare variant binning. (Dasariraju, S. and Urbanowicz, R.J., 2021, July. [RARE: evolutionary feature engineering for rare-variant bin discovery.](https://dl.acm.org/doi/abs/10.1145/3449726.3463174?casa_token=0MRY0eLfZW0AAAAA:PD75rM0SB_V37prY2Ey1CPCu5twUrWMoPn5C6tD9sBRuQy5TJ_TeqhzWwmvp41gbrsPtQerZpPI56A) In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1335-1343).)

The first implementation of FIBERS was developed within it's own [GitHub repository](https://github.com/UrbsLab/FIBERS), and was applied to an investigation of graft failure in kidney transplantation. (Dasariraju, S., Gragert, L., Wager, G.L., McCullough, K., Brown, N.K., Kamoun, M. and Urbanowicz, R.J., 2023. [HLA amino acid Mismatch-Based risk stratification of kidney allograft failure using a novel Machine learning algorithm.](https://www.sciencedirect.com/science/article/pii/S1532046423000953?casa_token=HP4rI5N9iFkAAAAA:-NgwMAlLUWlvLzzBHU9qz08mv-evC19YxIsFH5RTiGpSiXEd-uBuOkfZbuBShTwstT50vDnIsrM) Journal of Biomedical Informatics, 142, p.104374.)

The first publication detailing scikit-FIBERS (release 0.9.3) was applied and evaluated on simulated right-censored survival data with amino acid mismatch features.
The code for that is available [here](https://github.com/UrbsLab/scikit-FIBERS/tree/gecco_dev). (Urbanowicz, R., Bandhey, H., Kamoun, M., Fogarty, N. and Hsieh, Y.A., 2023, July. [Scikit-FIBERS: An'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival Analyses.](https://dl.acm.org/doi/abs/10.1145/3583133.3596393?casa_token=jZEPXXznvuUAAAAA:IdV4u-Q07p8_AEfvnTtLpBJePZzmdR2DsImvtpN0z2mge0tgLwqutEF18q74afpj9pOnQ8OnlxPKjw) In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp. 1846-1854).) This is the synonmous to [FIBERS 1.0 Release](https://github.com/UrbsLab/scikit-FIBERS/releases/tag/v1.0-beta).

scikit-FIBERS was extended with a prototype adaptive burden thresholding using '[FIBERS-AT](https://github.com/UrbsLab/scikit-FIBERS/tree/evostar_24)' approach to allow bins to simulaneously identify the best bin threshold to apply. (Bandhey, H., Sadek, S., Kamoun, M. and Urbanowicz, R., 2024, March. [Evolutionary Feature-Binning with Adaptive Burden Thresholding for Biomedical Risk Stratification.](https://link.springer.com/chapter/10.1007/978-3-031-56855-8_14) In International Conference on the Applications of Evolutionary Computation (Part of EvoStar) (pp. 225-239). Cham: Springer Nature Switzerland.)

Most recently scikit-FIBERS 2.1.0 was released, as a completely redesigned, refactored and expanded implementation. Expansions include (1) a merge operator, (2) variable mutation rate, (3) improved adaptive burden thresholding, (4) a bin diversity pressure deletion mechanism, (5) fitness options based on deviance residuals to estimate covariate adjustments throughout algorithm training, (6) a bin population cleanup option, and (7) a number of other helpful functions to report/save the underlying bin population and generate various visualizations. A publication on scikit-FIBERS 2.1.0 is in preparation. 

## Previous Version Comparison Benchmarking
The repository contains high-performance-cluster running scripts to compare previous releases of FIBERS for the performance benchmarking: FIBERS 1.0, FIBERS AT (adaptive thresholding), and FIBERS v2.1.0 (most recent benchmarked release). Code used to apply FIBERS 1.0 and AT are found within https://github.com/UrbsLab/scikit-FIBERS/tree/main/src_archive. Original sources of these earlier releases are as follows:
1. FIBERS 1.0: https://github.com/UrbsLab/scikit-FIBERS/tree/gecco_dev / https://github.com/UrbsLab/scikit-FIBERS/releases/tag/v1.0-beta
2. FIBERS AT: https://github.com/UrbsLab/scikit-FIBERS/tree/evostar_24