Update README abstract for pFLEX library

README.md

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 **Abstract**
 
-Genetic networks derived from omics data are a powerful tool for systematic gene function prediction. Performance evaluation of such predictions is crucial to judge the data and computational pipeline to derive the networks, but functional diversity within protein complex or pathway standards often cause hidden evaluation biases. To visualize and mitigate such biases, we recently developed an R package FLEX. Here, we present the FLEX genetic network benchmarking tool as Python library with new and improved functionality. The pFLEX library improves the overall runtime 4.1 to 15.8-fold. It offers additional evaluation metrics that allow for an easy comparison of precision recall performance at the complex or pathway resolution between genetic networks. We demonstrate the utility of pFLEX for evaluating tissue-specific co-essentiality networks and data normalization strategies of the Cancer Dependency Map. This illustrates how different biological module-resolved precision recall metrics in pFLEX enable sensitive and fast evaluation of genetic networks.
+Genetic networks derived from omics data are a powerful tool for systematic gene function prediction. Performance evaluation of such predictions is crucial to judge the data and computational pipeline for network construction, but unbalanced functional standards often cause hidden evaluation biases. To visualize and mitigate such biases, we previously developed the R package FLEX. Here, we present the pFLEX genetic network benchmarking tool as Python library with new and improved functionality. pFLEX improves overall runtime 4.1 to 15.8-fold. It offers additional evaluation metrics that allow for easy comparison of precision recall performance at the complex or pathway resolution between genetic networks. We demonstrate the utility of pFLEX for evaluating tissue-specific co-essentiality networks and data normalization strategies of the Cancer Dependency Map, as well as for cell line-specific Perturb-Seq-derived networks. This illustrates the requirement for biological module-resolved precision recall metrics in pFLEX for sensitive and fast evaluation of genetic networks.
 
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