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Abstract Background To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features. Results We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies. Conclusions Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.

Abstract Background Accumulating evidence suggests that tobacco smoking affects the susceptibility to and severity of chronic periodontitis. Epigenetics may explain the role of smoking in the development and progress of periodontal disease. In this study, we performed transcriptomic and methylomic analyses of non-periodontitis and periodontitis-affected gingival tissues according to smoking status. Methods Human gingival tissues were obtained from 20 patients, including non-smokers with and without periodontitis (n = 5 per group) and smokers with and without periodontitis (n = 5 per group). Total RNA and genomic DNA were isolated, and their quality was validated according to strict standards. The Illumina NextSeq500 sequencing system was used to generate transcriptome and methylome datasets. Results Comprehensive analysis, including between-group correlation, differential gene expression, DNA methylation, gene set enrichment, and protein-protein interaction, indicated that smoking may change the transcription and methylation states of extracellular matrix (ECM) organization-related genes, which exacerbated the periodontal condition. Conclusions Our results suggest that smoking-related changes in DNA methylation patterns and subsequent alterations in the expression of genes coding for ECM components may be causally related to the increased susceptibility to periodontitis in smokers as they could influence ECM organization, which in turn may have an effect on disease characteristics.