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Summary: Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers. Motivation: Combination therapy has shown promise as a treatment for complex diseases such as cancer. Synergistic drug combinations can offer increased therapeutic efficacy and reduce toxicity compared with single drugs. However, class imbalances in datasets have complicated the use of computational tools, such as deep learning, for synergistic drug prediction. We propose an improved deep forest-based model, ForSyn, to address the above problem on imbalanced, medium- or small-scale datasets with high dimensionality.