sorted the cited implemented tools alphabetically, renamed the pipeli…

…ne picture and added more alt-text

CITATIONS.md

@@ -14,25 +14,25 @@
 
 ## Pipeline tools
 
-- [DrEvalPy](https://github.com/daisybio/drevalpy)
+- [DrEvalPy](https://github.com/daisybio/drevalpy): The pipeline mostly automates the individual steps of the DrEvalPy PyPI package.
 
   > Bernett, J, Iversen, P, Picciani, M, Wilhelm, M, Baum, K, List, M. Will be published soon.
 
-- [SRMF](https://doi.org/10.1186/s12885-017-3500-5)
+- [DIPK](https://doi.org/10.1093/bib/bbae153): Implemented model in the pipeline.
 
-  > Wang L, Li X, Zhang L, Gao Q. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization. BMC cancer. 2017 Dec;17:1-2.
+  > Li P, Jiang Z, Liu T, Liu X, Qiao H, Yao X. Improving drug response prediction via integrating gene relationships with deep learning. Briefings in Bioinformatics. 2024 May;25(3):bbae153.
 
-- [MOLI](https://doi.org/10.1093/bioinformatics/btz318)
+- [MOLI](https://doi.org/10.1093/bioinformatics/btz318): Implemented model in the pipeline.
 
   > Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics. 2019 Jul;35(14):i501-9.
 
-- [SuperFELT](https://doi.org/10.1186/s12859-021-04146-z)
+- [SRMF](https://doi.org/10.1186/s12885-017-3500-5): Implemented model in the pipeline.
 
-  > Park S, Soh J, Lee H. Super. FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data. BMC bioinformatics. 2021 May 25;22(1):269.
+  > Wang L, Li X, Zhang L, Gao Q. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization. BMC cancer. 2017 Dec;17:1-2.
 
-- [DIPK](https://doi.org/10.1093/bib/bbae153)
+- [SuperFELT](https://doi.org/10.1186/s12859-021-04146-z): Implemented model in the pipeline.
 
-  > Li P, Jiang Z, Liu T, Liu X, Qiao H, Yao X. Improving drug response prediction via integrating gene relationships with deep learning. Briefings in Bioinformatics. 2024 May;25(3):bbae153.
+  > Park S, Soh J, Lee H. Super. FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data. BMC bioinformatics. 2021 May 25;22(1):269.
 
 ## Software packaging/containerisation tools
 

README.md

@@ -34,7 +34,7 @@ tuning is fair and consistent. With its flexible model interface, DrEval support
 ranging from statistical models to complex neural networks. By contributing your model to the
 DrEval catalog, you can increase your work's exposure, reusability, and transferability.
 
-# ![DrEval_pipeline](assets/DrEval_pipeline_simplified.png)
+# ![Pipeline diagram showing the major steps of nf-core/drugresponseeval](assets/drugresponseeval_pipeline_simplified.png)
 
 1. The response data is loaded
 2. All models are trained and evaluated in a cross-validation setting