benchscofi 2.0.0

Package which contains implementations of published collaborative filtering-based algorithms for drug repurposing.

BENCHmark for drug Screening with COllaborative FIltering (benchscofi) Python Package

This repository is a part of the EU-funded RECeSS project (#101102016), and hosts the implementations and / or wrappers to published implementations of collaborative filtering-based algorithms for easy benchmarking.

Statement of need

As of 2022, current drug development pipelines last around 10 years, costing $2billion in average, while drug commercialization failure rates go up to 90%. These issues can be mitigated by drug repurposing, where chemical compounds are screened for new therapeutic indications in a systematic fashion. In prior works, this approach has been implemented through collaborative filtering. This semi-supervised learning framework leverages known drug-disease matchings in order to recommend new ones.

There is no standard pipeline to train, validate and compare collaborative filtering-based repurposing methods, which considerably limits the impact of this research field. In benchscofi, the estimated improvement over the state-of-the-art (implemented in the package) can be measured through adequate and quantitative metrics tailored to the problem of drug repurposing across a large set of publicly available drug repurposing datasets.

Install the latest release

The fastest way to get access to all functionalities of benchscofi is to run the following command:

## Using the Docker image: will open a container
docker push recessproject/benchscofi:1.0.1

Documentation about benchscofi (and a manual installation) can be found at this page. The complete list of dependencies for benchscofi can be found at requirements.txt (pip).

Licence

This repository is under an OSI-approved MIT license.

Citation

If you use benchscofi in academic research, please cite it as follows

Réda, Clémence, Jill-Jênn Vie, and Olaf Wolkenhauer. "A new standard for drug repurposing by collaborative filtering: stanscofi and benchscofi." (2023).

Community guidelines with respect to contributions, issue reporting, and support

You are more than welcome to add your own algorithm to the package!

1. Add a novel implementation / algorithm

Add a new Python file (extension .py) in src/benchscofi/ named <model> (where model is the name of the algorithm), which contains a subclass of stanscofi.models.BasicModel which has the same name as your Python file. At least implement methods preprocessing, model_fit, model_predict_proba, and a default set of parameters (which is used for testing purposes). Please have a look at the placeholder file Constant.py which implements a classification algorithm which labels all datapoints as positive. It is highly recommended to provide a proper documentation of your class, along with its methods. When pushing a new algorithm to benchscofi, it is automatically tested (see tests/test_models.py and TemplateTest.py which are run). In order to run this test locally, please run in the tests/ folder:

python3 -m test_models <model> <dataset:default=Synthetic>

2. Rules for contributors

Pull requests and issue flagging are welcome, and can be made through the GitHub interface. Support can be provided by reaching out to recess-project[at]proton.me. However, please note that contributors and users must abide by the Code of Conduct.

Benchmark AUC and NDCG@items values (default parameters, single random training/testing set split) [updated 08/11/23]

These values (rounded to the closest 3rd decimal place) can be reproduced using the following command in folder tests/

python3 -m test_models <algorithm> <dataset:default=Synthetic> <batch_ratio:default=1>

:no_entry:'s represent failure to train or to predict. N/A's have not been tested yet. When present, percentage in parentheses is the considered value of batch_ratio (to avoid memory crash on some of the datasets). [mem]: memory crash [err]: error

Algorithm (global AUC)	Synthetic*	TRANSCRIPT [a]	Gottlieb [b]	Cdataset [c]	PREDICT [d]	LRSSL [e]
PMF	0.922	0.579	0.598	0.604	0.656	0.611
PulearnWrapper	1.000	:no_entry:	N/A	:no_entry:	:no_entry:	:no_entry:
ALSWR	0.971	0.507	0.677	0.724	0.693	0.685
FastaiCollabWrapper	1.000	0.876	0.856	0.837	0.835	0.851
SimplePULearning	0.995	0.949 (0.4)	:no_entry:[err]	:no_entry:[err]	0.994 (4%)	:no_entry:
SimpleBinaryClassifier	0.876	:no_entry:[mem]	0.855	0.938 (40%)	0.998 (1%)	:no_entry:
NIMCGCN	0.907	0.854	0.843	0.841	0.914 (60%)	0.873
FFMWrapper	0.924	:no_entry:[mem]	1.000 (40%)	1.000 (20%)	:no_entry:[mem]	:no_entry:
VariationalWrapper	:no_entry:[err]	:no_entry:[err]	0.851	0.851	:no_entry:[err]	:no_entry:
DRRS	:no_entry:[err]	0.662	0.838	0.878	:no_entry:[err]	0.892
SCPMF	0.853	0.680	0.548	0.538	:no_entry:[err]	0.708
BNNR	1.000	0.922	0.949	0.959	0.990 (1%)	0.972
LRSSL	0.127	0.581 (90%)	0.159	0.846	0.764 (1%)	0.665
MBiRW	1.000	0.913	0.954	0.965	:no_entry:[err]	0.975
LibMFWrapper	1.000	0.919	0.892	0.912	0.923	0.873
LogisticMF	1.000	0.910	0.941	0.955	0.953	0.933
PSGCN	0.767	:no_entry:[err]	0.802	0.888	:no_entry:	0.887
DDA_SKF	0.779	0.453	0.544	0.264 (20%)	0.591	0.542
HAN	1.000	0.870	0.909	0.905	0.904	0.923
PUextraTrees (n_estimators=10)	0.045 (50%)	0.325 (50%)	0.246 (20%)	:no_entry:[mem]	0.309 (5%)
XGBoost (n_estimators=100)	0.500	0.500 (20%)	0.500	0.500	0.500 (1%)	0.500 (60%)

The NDCG score is computed across all diseases (global), at k=#items.

Algorithm (global NDCG@k)	Synthetic@300*	TRANSCRIPT@613[a]	Gottlieb@593[b]	Cdataset@663[c]	PREDICT@1577[d]	LRSSL@763[e]
PMF	0.070	0.019	0.015	0.011	0.005	0.007
PulearnWrapper	N/A	:no_entry:	N/A	:no_entry:	:no_entry:	:no_entry:
ALSWR	0.000	0.177	0.236	0.406	0.193	0.424
FastaiCollabWrapper	1.000	0.035	0.012	0.003	0.001	0.000
SimplePULearning	1.000	0.059 (40%)	:no_entry:[err]	:no_entry:[err]	0.025 (4%)	:no_entry:[err]
SimpleBinaryClassifier	0.000	:no_entry:[mem]	0.002	0.005 (40%)	0.070 (1%)	:no_entry:[err]
NIMCGCN	0.568	0.022	0.006	0.005	0.007 (60%)	0.014
FFMWrapper	1.000	:no_entry:[mem]	1.000 (40%)	1.000 (20%)	:no_entry:[mem]	:no_entry:
VariationalWrapper	:no_entry:[err]	:no_entry:[err]	0.011	0.010	:no_entry:[err]	:no_entry:
DRRS	:no_entry:[err]	0.484	0.301	0.426	:no_entry:[err]	0.182
SCPMF	0.528	0.102	0.025	0.011	:no_entry:[err]	0.008
BNNR	1.000	0.466	0.417	0.572	0.217 (1%)	0.508
LRSSL	0.206	0.032 (90%)	0.009	0.004	0.103 (1%)	0.012
MBiRW	1.000	0.085	0.267	0.352	:no_entry:[err]	0.457
LibMFWrapper	1.000	0.419	0.431	0.605	0.502	0.430
LogisticMF	1.000	0.323	0.106	0.101	0.076	0.078
PSGCN	0.969	:no_entry:[err]	0.074	0.052	:no_entry:[err]	0.110
DDA_SKF	1.000	0.039	0.069	0.078 (20%)	0.065	0.069
HAN	1.000	0.075	0.007	0.000	0.001	0.002
PUextraTrees (n_estimators=10)	0.000 (50%)	0.198 (50%)	0.162 (20%)	:no_entry:[mem]	0.235 (5%)
XGBoost (n_estimators=100)	0.061	0.000 (20%)	0.002	0.000	0.000 (1%)	0.000 (60%)

:no_entry: Note that results from ``LibMFWrapper'' are not reproducible, and the resulting metrics might slightly vary across iterations.

:no_entry: XGBoost and SimpleBinaryClassifier do not take into account unlabeled points (they assume they are negative points).

Datasets

*Synthetic dataset created with function generate_dummy_dataset in stanscofi.datasets and the following arguments:

npositive=200 #number of positive pairs
nnegative=100 #number of negative pairs
nfeatures=50 #number of pair features
mean=0.5 #mean for the distribution of positive pairs, resp. -mean for the negative pairs
std=1 #standard deviation for the distribution of positive and negative pairs
random_seed=124565 #random seed

[a] Réda, Clémence. (2023). TRANSCRIPT drug repurposing dataset (2.0.0) [Data set]. Zenodo. doi:10.5281/zenodo.7982976

[b] Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011). PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular systems biology, 7(1), 496.

[c] Luo, H., Li, M., Wang, S., Liu, Q., Li, Y., & Wang, J. (2018). Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics, 34(11), 1904-1912.

[d] Réda, Clémence. (2023). PREDICT drug repurposing dataset (2.0.1) [Data set]. Zenodo. doi:10.5281/zenodo.7983090

[e] Liang, X., Zhang, P., Yan, L., Fu, Y., Peng, F., Qu, L., … & Chen, Z. (2017). LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning. Bioinformatics, 33(8), 1187-1196.