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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 Analysis of compound–protein interactions (CPIs) has become a crucial prerequisite for drug discovery and drug repositioning. In vitro experiments are commonly used in identifying CPIs, but it is not feasible to discover the molecular and proteomic space only through experimental approaches. Machine learning’s advances in predicting CPIs have made significant contributions to drug discovery. Deep neural networks (DNNs), which have recently been applied to predict CPIs, performed better than other shallow classifiers. However, such techniques commonly require a considerable volume of dense data for each training target. Although the number of publicly available CPI data has grown rapidly, public data is still sparse and has a large number of measurement errors. In this paper, we propose a novel method, Multi-channel PINN, to fully utilize sparse data in terms of representation learning. With representation learning, Multi-channel PINN can utilize three approaches of DNNs which are a classifier, a feature extractor, and an end-to-end learner. Multi-channel PINN can be fed with both low and high levels of representations and incorporates each of them by utilizing all approaches within a single model. To fully utilize sparse public data, we additionally explore the potential of transferring representations from training tasks to test tasks. As a proof of concept, Multi-channel PINN was evaluated on fifteen combinations of feature pairs to investigate how they affect the performance in terms of highest performance, initial performance, and convergence speed. The experimental results obtained indicate that the multi-channel models using protein features performed better than single-channel models or multi-channel models using compound features. Therefore, Multi-channel PINN can be advantageous when used with appropriate representations. Additionally, we pretrained models on a training task then finetuned them on a test task to figure out whether Multi-channel PINN can capture general representations for compounds and proteins. We found that there were significant differences in performance between pretrained models and non-pretrained models.

A new type of surface acoustic wave (SAW) gyroscope was developed on a floating thin piezoelectric membrane to enhance sensitivity and reliability by removing a bulk noise effect and by importing a higher amplitude of SAW. The developed device constitutes a two-port SAW resonator with a metallic dot array between two interdigital transducers (IDTs), and a one-port SAW delay line. The bulk silicon was completely etched away, leaving only a thin piezoelectric membrane with a thickness of one wavelength. A voltage controlled oscillator (VCO) was connected to a SAW resonator to activate the SAW resonator, while the SAW delay line was connected to the oscilloscope to monitor any variations caused by the Coriolis force. When the device was rotated, a secondary wave was generated, changing the amplitude of the SAW delay line. The highest sensitivity was observed in a device with a full acoustic wavelength thickness of the membrane because most of the acoustic field is confined within an acoustic wavelength thickness from the top surface; moreover, the thin-membrane-based gyroscope eliminates the bulk noise effect flowing along the bulk substrate. The obtained sensitivity and linearity of the SAW gyroscope were ∼27.5 µV deg−1 s−1 and ∼4.3%, respectively. Superior directivity was observed. The device surface was vacuum-sealed using poly(dimethylsiloxane) (PDMS) bonding to eliminate environmental interference. A three-axis detectable gyroscope was also implemented by placing three gyrosensors with the same configuration at right angles to each other on a printed circuit board. [ABSTRACT FROM AUTHOR]