Biological events such as gene expression, regulation, phosphorylation, localization and protein catabolism play important roles in the development of diseases. Understanding the association between diseases and genes can be enhanced with the identification of involved biological events in this association. Although biological knowledge has been accumulated in several databases and can be accessed through the Web, there is no specialized Web tool yet allowing for a query into the relationship among diseases, genes and biological events. For this task, we developed DigSee to search MEDLINE abstracts for evidence sentences describing that ‘genes’ are involved in the development of ‘cancer’ through ‘biological events’. DigSee is available through http://gcancer.org/digsee.
Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the development of disease through biological events from Medline abstracts. We identified associations between 13,054 genes and 4,494 disease types, which cover more disease-related genes than manually curated databases for all disease types (e.g., Online Mendelian Inheritance in Man) and also than those for specific diseases (e.g., Alzheimer’s disease and hypertension). We show that the text mining findings are reliable, as per the PubMed scale, in that the disease-disease relationships inferred from the literature-wide findings are similar to those inferred from manually curated databases in a well-known study. In addition, literature-wide distribution of biological events across disease types reveals different characteristics of disease types.
Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.