Empowering Healthcare through Comprehensive Informatics Education: The Status and Future of Biomedical and Health Informatics Education.

OBJECTIVES: Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development. METHODS: We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts' opinions. RESULTS: This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting. CONCLUSIONS: To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.

A blockchain-based healthcare data marketplace: prototype and demonstration.

Objectives: This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. Materials and methods: We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. Results: We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Discussion: Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. Conclusion: The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.

Association between short- and medium-term exposure to air pollutants and depressive episode using comprehensive air quality index among the population in South Korea.

BACKGROUND: Currently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk. METHODS: Using a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study. Six air pollutants (PM10, PM2.5, O3, CO, SO2, NO2) were measured for the analysis. Every air pollutant was standardized by air quality index (AQI) and CAI was calculated for universal representation. Using logistic regression, short- and medium-term exposure by AQI and CAI with the risk of depressive episode was calculated by odds ratio and 95 % confidence interval (CI). Furthermore, consecutive measurements of CAI over 1-month time intervals were evaluated with the risk of depressive episodes. Every analysis was conducted seasonally. RESULTS: There were 950 depressive episodes occurred during the survey. An increase in AQI for short-term exposure (0-30 days) showed higher risk of depressive episode in CO, while medium-term exposure (0-120 days) showed higher risk of depressive episode in CO, SO2, PM2.5, and PM10. During the cold season, the exposure to at least one abnormal CAI within 1-month intervals over 120 days was associated with a 68 % (95 % CI 1.11-2.54) increase in the risk of depressive episode. CONCLUSIONS: Short- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.

Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey.

This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults aged ≥50 years (Korea National Health and Nutrition Examination Survey), we explored 119 factors to detect LBP in individuals who reported experiencing LBP for at least 30 days within the previous 3 months. Our primary model, model 1, employed eXtreme Gradient Boosting (XGBoost) and selected primary factors (PFs) based on their feature importance scores. To extend this, we introduced additional factors, such as lumbar X-ray findings, physical activity, sitting time, and nutrient intake levels, which were available only during specific survey periods, into models 2 to 4. Model performance was evaluated using the area under the curve, with predicted probabilities explained by SHapley Additive exPlanations. Eleven PFs were identified, and model 1 exhibited an enhanced area under the curve .8 (.77-.84, 95% confidence interval). The factors had varying impacts across individuals, underscoring the need for personalized assessment. Hip and knee joint pain were the most significant PFs. High levels of physical activity were found to have a negative association with LBP, whereas a high intake of omega-6 was found to have a positive association. Notably, we identified factor clusters, including hip joint pain and female sex, potentially linked to osteoarthritis. In summary, this study successfully developed effective XGBoost models for LBP detection, thereby providing valuable insight into LBP-related factors. Comprehensive LBP management, particularly in women with osteoarthritis, is crucial given the presence of multiple factors. PERSPECTIVE: This article introduces XGBoost models designed to detect LBP and explores the multifactorial aspects of LBP through the application of SHapley Additive exPlanations and network analysis on the 4 developed models. The utilization of this analytical system has the potential to aid in devising personalized management strategies to address LBP.

Predicted Body Composition Against COVID-19: A Potential Digital Health Strategy.

This study focused on the associations between predicted lean body mass index (LBMI), appendicular skeletal muscle mass index (ASMI), and body fat mass index (BFMI) with the 2019 coronavirus disease (COVID-19). A nationwide population-based non-underweight cohort of 2,037,714 participants underwent two consecutive biennial health screening examinations, with changes in predicted body composition indices estimated using a multivariable-adjusted logistic regression model. Increased LBMI and ASMI were associated with a lower COVID-19 risk among men who became obese. In COVID-19 patients, increased LBMI, ASMI, and BFMI were associated with a higher risk of extracorporeal membrane oxygenation among obese men.

Moderate-to-Vigorous Physical Activity Changes with the Risk of SARS-CoV-2 Infection: A Nested Case-Control Study.

We identified the association of changes in moderate-to-vigorous physical activity (MVPA) with SARS-CoV-2 infection. From 6,396,500 patients, we performed a nested case-control study who participated in both biennial check-ups. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression. From physically inactive patients at period I, the odds increased when engaged in 1-2, 3-4, or ≥5 times of MVPA/week at period II. This study found that MVPA was directly associated with SARS-CoV-2 infection.

COVID-19-related cardiovascular disease risk due to weight gain: a nationwide cohort study.

BACKGROUND: Cardiovascular disease (CVD) is a significant contributor to morbidity and mortality worldwide, with CVD and post-acute COVID-19 associated CVD increasing. It remains unknown whether COVID-19 patients with weight gain are at a high risk for CVD events. Therefore, the primary objective of this study is to investigate the association between weight control and the risk of CVD following COVID-19. METHODS: The study included 2,024,728 adults who participated in two rounds of health screening between 2017 and 2020. The final cohort, which included 70,996 participants in the COVID-19 group and 212,869 participants in the control group. The adjusted hazard ratio of BMI change to CVD risk was calculated using Cox proportional hazards regression. RESULTS: We identified a total of 2869 cases of CVD (861 events for COVID-19 group and 2,008 events for the control group). Compared to individuals with a stable BMI, COVID-19 patients without obesity had an increased risk of CVD (adjusted hazard ratio [aHR] = 2.28; 95% confidence interval [CI], 1.15-4.53; p-value = 0.018). Additionally, non-COVID-19 patients with obesity also exhibited a higher risk of CVD (aHR = 1.58; 95% CI, 1.01-2.47; p-value = 0.046). CONCLUSION: In conclusion, people who gained weight during the pandemic, regardless of their weight category, had a significantly higher risk of CVD associated with COVID-19 compared to those who maintained their weight before the pandemic.

Increased Estimated GFR Is Negatively Associated With the Risk of SARS-CoV-2 Infection and Severe COVID-19 Within Normal to Mildly Decreased Levels: Nested Case-Control Study.

BACKGROUND: While accumulating evidence indicates chronic kidney disease as a risk factor for coronavirus disease 2019 (COVID-19), the association between normal or mildly decreased kidney function and COVID-19 is unaddressed. Here, we have examined the association of an increase in estimated glomerular filtration rate (eGFR) with the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe COVID-19 outcomes among patients within normal to mildly decreased kidney function. METHODS: The patients who participated in both health screenings from period I (2017-2018) to II (2019-2020) were enrolled to our study. All participants were categorized into four groups according to the changes in eGFR stage from period I to II: 1) persistently stage G1, 2) from stage G2 to G1, 3) from stage G1 to G2, 4) persistently stage G2. In addition, the changes in eGFR value were defined by subtracting its value of period I from II. Patients were followed up for SARS-CoV-2 infection from January 1, 2021 to any diagnosis of COVID-19 or December 31, 2021, whichever happened first. In addition, those with SARS-CoV-2 infection were followed-up for one month after diagnosis to analyze severe COVID-19. Adjusted odds ratio (aOR) was calculated using multivariable-adjusted logistic regression. RESULTS: We identified 159,427 patients with and 1,804,798 patients without SARS-CoV-2 infection. The risk of SARS-CoV-2 infection decreased when eGFR stage changed from G2 to G1 (aOR, 0.957; 95% confidence interval [CI], 0.938-0.977) and persistently maintained at G1 (aOR, 0.966; 95% CI, 0.943-0.990), compared with the persistently stage G2 group. In addition, the risk showed an inverse relationship with changes in eGFR value, which was depicted by restricted cubic spline curves. For the overall risk of severe COVID-19, the persistently stage G1 showed the lowest risk (aOR, 0.897; 95% CI, 0.827-0.972), followed by those from stage G1 to G2 (aOR, 0.900; 95% CI, 0.828-0.978) and those from stage G2 to G1 (aOR, 0.931; 95% CI, 0.871-0.995), compared with the persistently stage G2 group. CONCLUSION: An increase in eGFR was negatively associated with the risk of SARS-CoV-2 infection and severe COVID-19 among normal or mildly decreased kidney function. For severe COVID-19, maintaining higher baseline eGFR may act as a protective factor against its risk.

Association of weight changes with SARS-CoV-2 infection and severe COVID-19 outcomes: A nationwide retrospective cohort study.

BACKGROUND: Obesity is a risk factor for COVID-19. However, it is unknown whether weight changes can alter this risk. We investigated the association of weight changes with SARS-CoV-2 infection and acute severe COVID-19 outcomes occurring within two months of the infection. METHODS: We used 6.3 million nationwide cohort. The body weight was classified as follows: (1) underweight, body mass index (BMI) < 18.5 kg/m2; (2) normal, BMI 18.5-22.9 kg/m2; (3) overweight, BMI 23-24.9 kg/m2; (4) obese, BMI≥ 25 kg/m2. Weight changes were defined by comparing the classification of body weight during the health screening period I and II. The outcomes were SARS-CoV-2 infection and severe COVID-19 outcomes within two months after the infection. The association was evaluated using multivariable-adjusted logistic regression. The following covariates were adjusted: age, sex, household income, cigarette smoking, alcohol consumption, physical activity, hypertension, diabetes mellitus, dyslipidemia, Charlson comorbidity index score, and dose of all COVID-19 vaccinations prior to SARS-CoV-2 infection. RESULTS: Of the 2119,460 study participants, 184,204 were infected with SARS-CoV-2. Weight gain showed a higher risk of SARS-CoV-2 infection in underweight to normal and normal to overweight groups. Conversely, weight loss showed a lower risk of SARS-CoV-2 infection in normal to underweight, overweight to underweight, overweight to normal, obese to normal, and obese to overweight groups. In addition, weight gain revealed a higher risk of severe COVID-19 outcomes, whereas weight loss showed a lower risk of severe COVID-19 outcomes. CONCLUSION: This study found that weight loss and gain are associated with a lower and higher risk of both SARS-CoV-2 infection and severe COVID-19 outcomes, respectively. Healthy weight management may be beneficial against the risk of COVID-19.

Risk of Subsequent Primary Cancer in Thyroid Cancer Survivors: A Nationwide Population-Based Study.

Background: Despite obtaining a good prognosis and long life expectancy, survivors of thyroid cancer can nevertheless develop subsequent primary cancer (SPC). We investigated the risk and types of SPC in patients with thyroid cancer and compared them with subjects without thyroid cancer history (controls). Methods: We conducted a nationwide, population-based, retrospective cohort study based on the Korean National Health Insurance Database. A total of 432,654 patients diagnosed with thyroid cancer between 2004 and 2019 were 1:1 matched with controls for age, sex, income, and region of residence. The hazard ratios (HR) and 95% confidence intervals (CI) of SPC were estimated using Cox proportional hazard models. Results: In total, 78,584 (18.2%) patients with thyroid cancer and 49,979 (11.6%) controls were diagnosed with SPCs over a mean follow-up of 6.9 years. Patients with thyroid cancer had a higher risk of SPC at any site (adjusted HR, 1.62; 95% CI, 1.60-1.64) than the controls. The risk of SPCs was particularly high for patients diagnosed with thyroid cancer at a younger age (<40 years) and within 5 years. Conclusions: Medical caregivers should consider the long-term follow-up of patients with thyroid cancer and discuss the risk of SPC, especially if they complain of cancer-related symptoms.

Exercise Frequency Reduction Is Associated With Higher Risk of Infection in Newly Diagnosed Diabetes: A Nationally Representative Cohort Study.

BACKGROUND: Exercise is an important method to control the progression of diabetes. Since diabetes compromises immune function and increases the risk of infectious diseases, we hypothesized that exercise may affect the risk of infection by its immunoprotective effects. However, population-based cohort studies regarding the association between exercise and the risk of infection are limited, especially regarding changes in exercise frequency. The aim of this study was to determine the association between the change in exercise frequency and the risk of infection among patients with newly diagnosed diabetes. METHODS: Data of 10,023 patients with newly diagnosed diabetes were extracted from the Korean National Health Insurance Service-Health Screening Cohort. Self-reported questionnaires for moderate-to-vigorous physical activity (MVPA) were used to classify changes in exercise frequency between two consecutive two-year periods of health screenings (2009-2010 and 2011-2012). The association between changes in exercise frequency and the risk of infection was evaluated using multivariable Cox proportional-hazards regression. RESULTS: Compared with engaging in ≥ 5 times of MVPA/week during both periods, a radical decrease in MVPA (from ≥ 5 times of MVPA/week to physical inactivity) was associated with a higher risk of pneumonia (adjusted hazard ratio [aHR], 1.60; 95% confidence interval [CI], 1.03-2.48) and upper respiratory tract infection (aHR, 1.15; 95% CI, 1.01-1.31). In addition, a reduction of MVPA from ≥ 5 to < 5 times of MVPA/week was associated with a higher risk of pneumonia (aHR, 1.52; 95% CI, 1.02-2.27), whereas the risk of upper respiratory tract infection was not higher. CONCLUSION: Among patients with newly diagnosed diabetes, a reduction in exercise frequency was related to an increase in the risk of pneumonia. For patients with diabetes, a modest level of physical activity may need to be maintained to reduce the risk of pneumonia.

Inverse association between changes in systolic and diastolic blood pressure and risk of depression: A nationally representative cohort study.

BACKGROUND: Depression is one of complex mental disorders with diverse etiological factors but the association between blood pressure (BP) and depression is unknown. We aimed to investigate the association between changes in BP (systolic and diastolic) and incident depression. METHODS: From the National Health Insurance Service-Health Screening Cohort (NHIS-HEALS), 224,192 participants who underwent biennial health screenings from period I (2004-05) and II (2006-07) were included in the study. Systolic BP (SBP) and diastolic BP (DBP) categories were defined as follows: SBP into 5 categories (<90 mmHg, 90 mmHg -119 mmHg, 120 mmHg -129 mmHg, 130 mmHg -139 mmHg, ≥140 mmHg) and DBP into 4 categories (<60 mmHg, 60 mmHg -79 mmHg, 80 mmHg -89 mmHg, ≥90 mmHg). Also, BP levels were classified into 5 groups: normal, elevated BP, stage 1 BP, stage 2 BP, hypotension. Using the Cox proportional hazards regression, changes in SBP and DBP between two screening periods and the risk of depression were calculated by adjusted hazard ratio (aHR) and 95 % confidence interval (CI). RESULTS: There were 17,780 depression events during 1.5 million person-year of follow-up. Compared to the participants with SBP ≥ 140 mmHg or DBP ≥ 90 mmHg from both periods, those who decreased SBP from ≥140 mmHg to 120 mmHg-129 mmHg (aHR 1.13; 95 % CI 1.04-1.24; P = 0.001) and those who decreased DBP from ≥90 mmHg to 60 mmHg-79 mmHg (aHR 1.10; 95 % CI 1.02-1.20; P = 0.020) showed a higher risk of depression, respectively. CONCLUSIONS: Changes in SBP and DBP showed an inverse relationship with depression risk.

Machine-learning enhancement of urine dipstick tests for chronic kidney disease detection.

OBJECTIVE: Screening for chronic kidney disease (CKD) requires an estimated glomerular filtration rate (eGFR, mL/min/1.73 m2) from a blood sample and a proteinuria level from a urinalysis. We developed machine-learning models to detect CKD without blood collection, predicting an eGFR less than 60 (eGFR60 model) or 45 (eGFR45 model) using a urine dipstick test. MATERIALS AND METHODS: The electronic health record data (n = 220 018) obtained from university hospitals were used for XGBoost-derived model construction. The model variables were age, sex, and 10 measurements from the urine dipstick test. The models were validated using health checkup center data (n = 74 380) and nationwide public data (KNHANES data, n = 62 945) for the general population in Korea. RESULTS: The models comprised 7 features, including age, sex, and 5 urine dipstick measurements (protein, blood, glucose, pH, and specific gravity). The internal and external areas under the curve (AUCs) of the eGFR60 model were 0.90 or higher, and a higher AUC for the eGFR45 model was obtained. For the eGFR60 model on KNHANES data, the sensitivity was 0.93 or 0.80, and the specificity was 0.86 or 0.85 in ages less than 65 with proteinuria (nondiabetes or diabetes, respectively). Nonproteinuric CKD could be detected in nondiabetic patients under the age of 65 with a sensitivity of 0.88 and specificity of 0.71. DISCUSSION AND CONCLUSIONS: The model performance differed across subgroups by age, proteinuria, and diabetes. The CKD progression risk can be assessed with the eGFR models using the levels of eGFR decrease and proteinuria. The machine-learning-enhanced urine-dipstick test can become a point-of-care test to promote public health by screening CKD and ranking its risk of progression.

Association of Physical Activity With SARS-CoV-2 Infection and Severe Clinical Outcomes Among Patients in South Korea.

Importance: The association of moderate to vigorous physical activity (MVPA) with COVID-19 outcomes is unclear and needs to be investigated. Objective: To identify the association of longitudinal changes in MVPA with SARS-CoV-2 infection and severe COVID-19 outcomes. Design, Setting, and Participants: This nested case-control study used data from 6 396 500 adult patients in South Korean who participated in National Health Insurance Service (NHIS) biennial health screenings from period 1 (2017-2018) to period 2 (2019-2020). Patients were followed from October 8, 2020, until the diagnosis of COVID-19 or December 31, 2021. Exposure: Moderate to vigorous physical activity was measured by self-report on questionnaires during both NHIS health screenings and calculated by adding the frequency (times per week) of each moderate (≥30 minutes per day) and vigorous (≥20 minutes per day) physical activity. Main Outcomes and Measures: The main outcomes were a positive diagnosis of SARS-CoV-2 infection and severe COVID-19 clinical events. Adjusted odds ratio (aORs) and 99% CIs were calculated using multivariable logistic regression analysis. Results: A total of 183 350 patients with COVID-19 (mean [SD] age, 51.9 [13.8] years; female, 89 369 [48.7%]; male, 93 981 [51.3%]) among 2 110 268 participants were identified. For participants with vs without COVID-19, the proportion of MVPA frequency at period 2 was 35.8% vs 35.9% for physically inactive, 18.9% vs 18.9% for 1 to 2 times per week, 17.7% vs 17.7% for 3 to 4 times per week, and 27.5% vs 27.4% for 5 or more times per week. Among unvaccinated, physically inactive patients at period 1, the odds for infection increased when engaged in MVPA 1 to 2 times per week (aOR, 1.08; 99% CI, 1.01-1.15), 3 to 4 times per week (aOR, 1.09; 99% CI, 1.03-1.16), or 5 or more times per week (aOR, 1.10; 99% CI, 1.04-1.17) at period 2. Conversely, among unvaccinated patients with MVPA of 5 or more times per week at period 1, the odds for infection decreased when engaged 1 to 2 times per week (aOR, 0.90; 99% CI, 0.81-0.98) or physically inactive (aOR, 0.80; 99% CI, 0.73-0.87) at period 2. The trend of MVPA and incident infection was mitigated when participants were fully vaccinated. Furthermore, the odds for severe COVID-19 showed significant but limited associations with MVPA. Conclusions and Relevance: The findings of this nested case-control study show a direct association of MVPA with risk of SARS-CoV-2 infection, which was mitigated after completion of the COVID-19 vaccination primary series. In addition, higher levels of MVPA were associated with a lower risk of severe COVID-19 outcomes to limited proportions.

Safety of COVID-19 Vaccines among Patients with Type 2 Diabetes Mellitus: Real-World Data Analysis.

BACKGROUND: Little is known about the adverse events (AEs) associated with coronavirus disease 2019 (COVID-19) vaccination in patients with type 2 diabetes mellitus (T2DM). METHODS: This study used vaccine AE reporting system data to investigate severe AEs among vaccinated patients with T2DM. A natural language processing algorithm was applied to identify people with and without diabetes. After 1:3 matching, we collected data for 6,829 patients with T2DM and 20,487 healthy controls. Multiple logistic regression analysis was used to calculate the odds ratio for severe AEs. RESULTS: After COVID-19 vaccination, patients with T2DM were more likely to experience eight severe AEs than controls: cerebral venous sinus thrombosis, encephalitis myelitis encephalomyelitis, Bell's palsy, lymphadenopathy, ischemic stroke, deep vein thrombosis (DVT), thrombocytopenia (TP), and pulmonary embolism (PE). Moreover, patients with T2DM vaccinated with BNT162b2 and mRNA-1273 were more vulnerable to DVT and TP than those vaccinated with JNJ-78436735. Among patients with T2DM administered mRNA vaccines, mRNA-1273 was safer than BNT162b2 in terms of the risk of DVT and PE. CONCLUSION: Careful monitoring of severe AEs in patients with T2DM may be necessary, especially for those related to thrombotic events and neurological dysfunctions after COVID-19 vaccination.

COVID-19 Vaccination Is Not Associated with Psychiatric Adverse Events: A Meta-Analysis.

Coronavirus disease 2019 (COVID-19) has been a global health problem since December 2019. Vaccination has been widely considered the best way to prevent COVID-19 pandemic, but public concerns about the safety of vaccines remain. There have been many studies reporting adverse events in the vaccinated. However, to date, no meta-analysis of the association of COVID-19 vaccination with psychiatric adverse events has been conducted yet. In this meta-analysis, studies on depression, anxiety and distress after COVID-19 vaccination were searched in the PubMed, Cochrane and Embase from January 2020 to April 2022. The OR of depression in four studies with a total sample size of 462,406 is obtained as 0.88 (95% CI; 0.75, 1.03), and the OR of anxiety as 0.86 (95% CI; 0.71, 1.05). However, there were no statistically significant differences between the groups. The mean difference of distress in two studies was −0.04 (95%CI; −0.05, −0.02; p < 0.0001). As a result of the moderator analysis, married people experienced less depression and anxiety after vaccination, and in White people, depression after vaccination was lower than others. We also found that people with a history of COVID-19 infection were more depressed and anxious after vaccination. We suggest that COVID-19 vaccination was not associated with a worsening of depression and anxiety.

Relationship between drug targets and drug-signature networks: a network-based genome-wide landscape.

Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score (KDSN, i.e., the degree to which gene expression is stimulated by the drug). Accordingly, we constructed two drug-gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in whole genome. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures.