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Chloroplast is a type of subcellular organelle in green plants and algae.It is the main subcellular organelle for conducting photosynthetic process.The proteins,which local-ize within the chloroplast,are responsible for the photosyn-thetic process at molecular level.The chloroplast can be further divided into several compartments.Proteins in different com-partments are related to different steps in the photosynthetic process.Since the molecular function of a protein is highly cor-related to the exact cellular localization,pinpointing the sub-chloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic pro-cess.Experimental process for determining protein subchloro-plast location is always costly and time consuming.Therefore,computational approaches were developed to predict the pro-tein subchloroplast locations from the primary sequences.Over the last decades,more than a dozen studies have tried to predict protein subchloroplast locations with machine learning meth-ods.Various sequence features and various machine learning algorithms have been introduced in this research topic.In this review,we collected the comprehensive information of all ex-isting studies regarding the prediction of protein subchloroplast locations.We compare these studies in the aspects of bench-marking datasets,sequence features,machine learning algo-rithms,predictive performances,and the implementation avail-ability.We summarized the progress and current status in this special research topic.We also try to figure out the most possi-ble future works in predicting protein subchloroplast locations.We hope this review not only list all existing works,but also serve the readers as a useful resource for quickly grasping the big picture of this research topic.We also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.