diff --git a/report.tex b/report.tex
index c9e54be..89a5b94 100644
--- a/report.tex
+++ b/report.tex
@@ -189,7 +189,11 @@
        
     Machine learning algorithms are often regarded as black boxes, providing powerful predictions but limited insight into the underlying mechanisms driving those predictions. In fields such as medicine and biology, however, interpretability is not just a desirable feature—it is a necessity. Transparent models and interpretable outputs are critical for ensuring that predictions and recommendations can be explained, validated, and trusted, especially when they influence life-altering decisions. In the context of cancer treatment, this need for interpretability becomes even more pressing. Misguided treatment decisions can delay the administration of effective therapies, significantly increasing the risk of mortality. Beyond guiding clinical decisions, the interpretability of features in machine learning models also offers a unique opportunity to deepen our understanding of drug resistance in cancer. By unraveling the biological and molecular underpinnings of resistance, we can develop more targeted and effective therapeutic strategies.
 
-    
+    The authors of \cite{paclitaxel} applied the SHapley Additive exPlanations (SHAP), a model interpretation framework, to quantify and rank the feature contributions for classification models. After analysing feature importances authors were able to reduce count of the features from 112 to only 25 and even increase the accuracy of their models. Interestingly that their models were able to achieve approximately 78\% even when only the top three features were utilized for classification.
+
+    In \cite{tabular} authors also used SHAP to performe feature analysis. In this study, feature importance analysis was utilized to identify key genes associated with cisplatin resistance. By leveraging feature importance derived from multiple predictive models, the authors highlighted BCL2L1 as a critical gene mediating cisplatin resistance. Notably, earlier studies suggested that $\beta$-catenin expression is necessary for maintaining elevated levels of BCL2L1 in cisplatin-resistant cells. Additionally, the analysis revealed the involvement of PLK2, whose expression was linked to $\beta$-catenin activity. The authors demonstrated that lower expression levels of BCL2L1 and PLK2 correlated with improved outcomes in ovarian cancer, and inhibitors targeting these genes showed a synergistic effect when combined with cisplatin. These findings underscore the importance of the $\beta$-catenin/BCL2L1 axis in driving resistance and provide potential targets for enhancing cisplatin efficacy.
+
+
     
     \section{Results}
     In all works, the construction of machine learning models is essentially a secondary result. First of all, studies show the applicability of these methods to tasks related to the problems of cancer cell resistance to chemotherapy. Also, using machine learning methods, the authors test their hypotheses, confirm or discover links between various characteristics of cancer cells, patient clinical data and drug resistance.