This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respiratory activity can be estimated from the high frequency (HF) band of HRV signals. For drowsiness detection, many earlier works commonly used dominant respiration (DR) characteristics. However, in some situations where emphasized power in a power spectrum of HRV occurs at multi sub-frequency, the DR measures may possibly fail to capture overall respiration characteristics. To handle this problem, we propose two spectral indices, the weighted mean (WM) and the weighted standard deviation (WSD) of the HF band in the power spectrum. These indices are used to properly capture the overall shape of the respiratory activity shown through the HF band of the HRV power spectrum as an alternative to the DR measures. For experiments, we collected HRV data with an electrocardiogram device worn on the body under a virtual driving environment. The proposed indices somewhat clearly showed the tendency that respiratory frequency decreases and respiration regularity increases in drowsy states of all subjects, while existing DR measures hardly showed this. In addition, when the proposed indices are used alone or together with conventional HRV-related measures as input features for classification models, they showed the best performance in distinguishing drowsiness from wakefulness.

In practice, some drugs produce a number of negative biological effects that can mitigate their effectiveness as a remedy. To address this issue, several studies have been performed for the prediction of drug-induced toxicity from gene-expression data, and a significant amount of work has been done on predicting limited drug-induced symptoms or single-organ toxicity. Since drugs often lead to some injuries in several organs like liver or kidney, however, it would be very useful to forecast the drug-induced injuries for multiple organs. Therefore, in this work, our aim was to develop a multi-organ toxicity prediction model using an integrative model of gene-expression data. To train our integrative model, we used 3708 in-vivo samples of gene-expression profiles exposed to one of 41 drugs related to 21 distinct physiological changes divided between liver and kidney (liver 11, kidney 10). Specifically, we used the gene-expression profiles to learn an ensemble classifier for each of 21 pathology prediction models. Subsequently, these classifiers were combined with weights to generate an integrative model for each pathological finding. The integrative model outputs the likeliness of presenting the trained pathology in a given test sample of gene-expression profile, called an integrative prediction score (IPS). For the evaluation of an integrative model, we estimated the prediction performance with the k-fold cross-validation. Our results demonstrate that the proposed integrative model is superior to individual pathology prediction models in predicting multi-organ drug-induced toxicities over all the targeted pathological findings. On average, the AUC of the integrative models was 88% while the AUC of individual pathology prediction models was 68%. Not only does this integrative model produce comparable prediction performance to existing approaches, but also it produces very stable performance overall. In addition, our approach is easily expandable to a variety of other multi-organ toxicology applications.

Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data may hinder improvement of performance. Moreover, compared with all possible drug combinations, the highly limited range of drug combinations used for model training prevents achieving high generalizability. To handle these problems, we propose a unified embedding-based prediction model using knowledge graph constructed with data of drug–protein and protein–protein interactions. Herein, single or multiple drugs or proteins are mapped into the same embedding space, allowing us to (1) jointly utilize side effect occurrence data associated with single drugs and multidrug combinations to train prediction models and (2) quantify connectivity strengths between drugs and other entities such as proteins. Due to these characteristics, it becomes also possible to utilize the quantified relationships between distant nodes, as well as neighboring nodes, of all possible multidrug combinations to regularize the models. Compared with existing methods, our model showed improved performance, especially in predicting the side effects of new combinations containing novel drugs that have no clinical information on polypharmacy effects. Furthermore, our unified embedding vectors have been shown to provide interpretability, albeit to a limited extent, for proteins highly associated with multidrug side effect.

The effective development of new drugs relies on the identification of genes that are related to the symptoms of toxicity. Although many researchers have inferred toxicity markers, most have focused on discovering toxicity occurrence markers rather than toxicity severity markers. In this study, we aimed to identify gene markers that are relevant to both the occurrence and severity of toxicity symptoms. To identify gene markers for each of four targeted liver toxicity symptoms, we used microarray expression profiles and pathology data from 14,143 in vivo rat samples. The gene markers were found using sparse linear discriminant analysis (sLDA) in which symptom severity is used as a class label. To evaluate the inferred gene markers, we constructed regression models that predicted the severity of toxicity symptoms from gene expression profiles. Our cross-validated results revealed that our approach was more successful at finding gene markers sensitive to the aggravation of toxicity symptoms than conventional methods. Moreover, these markers were closely involved in some of the biological functions significantly related to toxicity severity in the four targeted symptoms.

Han HJ, et al. J Korean Med Sci. 2026 Aug;41:e226. https://doi.org/10.3346/jkms.2026.41.e226