Improve introduction

README.md

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 Cesaire J. K. Fouodo
 
 ### Introduction
-Recent technological advances have enabled the simultaneous targeting of multiple pathways to enhance therapies for complex diseases. This often results in the collection of numerous data entities across various layers of patient groups, posing a challenge in integrating all data into a single analysis. Ideally, patient data will overlap across layers, allowing for early or intermediate integrative techniques. These techniques are challenging when patient data does not overlap well. Late integration modeling addresses this by analyzing each data entity separately to obtain layer-specific results, which are then integrated using meta-analysis. As data entities can differ by their internal architectures, it is commonly preferable to utilize layer-specific analysis methods instead of a single analysis method across all layers. 
+Recent technological advances have enabled the simultaneous targeting of multiple pathways to enhance therapies for complex diseases. This often results in the collection of numerous data entities across various layers of patient groups, posing a challenge for integrating all data into a single analysis. Ideally, patient data will overlap across layers, allowing for early or intermediate integrative techniques. However, these techniques are challenging when patient data does not overlap well. Additionally, the internal structure of each data entity may necessitate specific statistical methods rather than applying the same method across all layers. Late integration modeling addresses this by analyzing each data entity separately to obtain layer-specific results, which are then integrated using meta-analysis. Currently, no R package offers this flexibility.
 
-We introduce the package fuseMLR for late integration modeling in R. The package allows users to define a study with multiple layers, data entities and layer-specific machine learning methods. The package is user-friendly, allowing for the training of different models across layers and automatically condcuting meta analysis once layer-specific training is completed. Additionally, fuseMLR enables users to perform variable selection at the layer level and make predictions for new studies within a single task.
+We introduce the fuseMLR package for late integration modeling in R. This package allows users to define studies with multiple layers, data entities, and layer-specific machine learning methods. FuseMLR is user-friendly, enabling the training of different models across layers and automatically conducting meta-analysis once layer-specific training is completed. Additionally, fuseMLR allows for variable selection at the layer level and makes predictions for new data entities.
 
 ### Installation
 Installation from Github: