BACKGROUND: Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature. RESULTS: Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks. Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively. CONCLUSION: We believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers.

UNLABELLED: We introduce HiPub, a seamless Chrome browser plug-in that automatically recognizes, annotates and translates biomedical entities from texts into networks for knowledge discovery. Using a combination of two different named-entity recognition resources, HiPub can recognize genes, proteins, diseases, drugs, mutations and cell lines in texts, and achieve high precision and recall. HiPub extracts biomedical entity-relationships from texts to construct context-specific networks, and integrates existing network data from external databases for knowledge discovery. It allows users to add additional entities from related articles, as well as user-defined entities for discovering new and unexpected entity-relationships. HiPub provides functional enrichment analysis on the biomedical entity network, and link-outs to external resources to assist users in learning new entities and relations. AVAILABILITY AND IMPLEMENTATION: HiPub and detailed user guide are available at http://hipub.korea.ac.kr CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we describe a new speech/non-speech classification method that improves the endpoint detection performance for speech recognition in noisy environments. The proposed method uses multiple features to increase the robustness in noisy environments, and the classification and regression tree (CART) technique is applied to effectively combine these multiple features for classification of each frame. We evaluate the performance of the proposed method by conducting speech/non-speech classification experiments on noisy speech. We also investigate the importance of various features on speech/non-speech classification in noisy environments In particular, the proposed method is applied to the endpoint detection algorithm for isolated speech recognition of a voice-dialing cellular phone. We simulate the speech recognition experiments in various noise environments, and the effects of the proposed method on speech recognition performance are evaluated.

A speaker adaptation algorithm using formant frequencies is proposed. The formants extracted from the cepstral means in the reference codebook are iteratively shifted toward the formants of a test speaker. The number of cepstral means selected at each iteration decreases as the iteration increases. The decision of the number of selected cepstral means and a formant based distance measure are formulated. The proposed algorithm was implemented in two schemes and evaluated by speaker-independent, male speaker dependent, and female speaker-dependent recognition experiments. A combined scheme with the Bayesian adaptation obtained 9.7% enhancement for the average recognition accuracy in speaker-independent experiments and 52.6% in speaker-dependent recognition experiments.