Prevalence and Risk Factors of White Matter Lesions in Tibetan Patients Without Acute Stroke.

Background and Purpose- Studies on the prevalence and risk factors of white matter lesions (WMLs) in Tibetans living at high altitudes are scarce. We conducted this study to determine the prevalence and risks of WMLs in Tibetan patients without or with nonacute stroke. Methods- We undertook a retrospective analysis of medical records of patients treated at the People's Hospital of Tibetan Autonomous Region and identified a total of 301 Tibetan patients without acute stroke. WML severity was graded by the Fazekas Scale. We assessed the overall and age-specific prevalence of WMLs and analyzed associations between WMLs and related factors with univariate and multivariate methods. Results- Of the 301 patients, 87 (28.9%) had peripheral vertigo, 83 (27.3%) had primary headache, 52 (17.3%) had a history of stroke, 36 (12.0%) had an anxiety disorder, 29 (9.6%) had epilepsy, 12 (4.0%) had infections of the central nervous system, and 3 (1.0%) had undetermined diseases. WMLs were present in 245 (81.4%) patients, and 54 (17.9%) were younger than 40 years. Univariate analysis showed that age, history of cerebral infarction, hypertension, the thickness of the common carotid artery intima, and plaque within the intracarotid artery were related risks for WMLs. Ordered logistic analysis showed that age, history of cerebral ischemic stroke, hypertension, male sex, and atrial fibrillation were associated with WML severity. Conclusions- Risk factors for WMLs appear similar for Tibetans residing at high altitudes and individuals living in the plains. Further investigations are needed to determine whether Tibetans residing at high altitudes have a higher burden of WMLs than inhabitants of the plains.

Integrating random walk and binary regression to identify novel miRNA-disease association.

BACKGROUND: In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly. RESULTS: In this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database. CONCLUSIONS: We anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works.

GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.

A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases.

Motivation: Accumulating clinical observations have indicated that microbes living in the human body are closely associated with a wide range of human noninfectious diseases, which provides promising insights into the complex disease mechanism understanding. Predicting microbe-disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development. However, little efforts have been attempted to understand and predict human microbe-disease associations on a large scale until now. Results: In this work, we constructed a microbe-human disease association network and further developed a novel computational model of KATZ measure for Human Microbe-Disease Association prediction (KATZHMDA) based on the assumption that functionally similar microbes tend to have similar interaction and non-interaction patterns with noninfectious diseases, and vice versa. To our knowledge, KATZHMDA is the first tool for microbe-disease association prediction. The reliable prediction performance could be attributed to the use of KATZ measurement, and the introduction of Gaussian interaction profile kernel similarity for microbes and diseases. LOOCV and k-fold cross validation were implemented to evaluate the effectiveness of this novel computational model based on known microbe-disease associations obtained from HMDAD database. As a result, KATZHMDA achieved reliable performance with average AUCs of 0.8130 ± 0.0054, 0.8301 ± 0.0033 and 0.8382 in 2-fold and 5-fold cross validation and LOOCV framework, respectively. It is anticipated that KATZHMDA could be used to obtain more novel microbes associated with important noninfectious human diseases and therefore benefit drug discovery and human medical improvement. Availability and Implementation: Matlab codes and dataset explored in this work are available at http://dwz.cn/4oX5mS . Contacts: xingchen@amss.ac.cn or zhuhongyou@gmail.com or wangxuesongcumt@163.com. Supplementary information: Supplementary data are available at Bioinformatics online.

PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

BACKGROUND: Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. METHODS: In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. RESULTS: MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. CONCLUSIONS: All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model.

BACKGROUND: Accumulating clinical researches have shown that specific microbes with abnormal levels are closely associated with the development of various human diseases. Knowledge of microbe-disease associations can provide valuable insights for complex disease mechanism understanding as well as the prevention, diagnosis and treatment of various diseases. However, little effort has been made to predict microbial candidates for human complex diseases on a large scale. METHODS: In this work, we developed a new computational model for predicting microbe-disease associations by combining two single recommendation methods. Based on the assumption that functionally similar microbes tend to get involved in the mechanism of similar disease, we adopted neighbor-based collaborative filtering and a graph-based scoring method to compute association possibility of microbe-disease pairs. The promising prediction performance could be attributed to the use of hybrid approach based on two single recommendation methods as well as the introduction of Gaussian kernel-based similarity and symptom-based disease similarity. RESULTS: To evaluate the performance of the proposed model, we implemented leave-one-out and fivefold cross validations on the HMDAD database, which is recently built as the first database collecting experimentally-confirmed microbe-disease associations. As a result, NGRHMDA achieved reliable results with AUCs of 0.9023 ± 0.0031 and 0.9111 in the validation frameworks of fivefold CV and LOOCV. In addition, 78.2% microbe samples and 66.7% disease samples are found to be consistent with the basic assumption of our work that microbes tend to get involved in the similar disease clusters, and vice versa. CONCLUSIONS: Compared with other methods, the prediction results yielded by NGRHMDA demonstrate its effective prediction performance for microbe-disease associations. It is anticipated that NGRHMDA can be used as a useful tool to search the most potential microbial candidates for various diseases, and therefore boosts the medical knowledge and drug development. The codes and dataset of our work can be downloaded from https://github.com/yahuang1991/NGRHMDA .

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning.

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.

RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.

Cumulative verified experimental studies have demonstrated that microRNAs (miRNAs) could be closely related with the development and progression of human complex diseases. Based on the assumption that functional similar miRNAs may have a strong correlation with phenotypically similar diseases and vice versa, researchers developed various effective computational models which combine heterogeneous biologic data sets including disease similarity network, miRNA similarity network, and known disease-miRNA association network to identify potential relationships between miRNAs and diseases in biomedical research. Considering the limitations in previous computational study, we introduced a novel computational method of Ranking-based KNN for miRNA-Disease Association prediction (RKNNMDA) to predict potential related miRNAs for diseases, and our method obtained an AUC of 0.8221 based on leave-one-out cross validation. In addition, RKNNMDA was applied to 3 kinds of important human cancers for further performance evaluation. The results showed that 96%, 80% and 94% of predicted top 50 potential related miRNAs for Colon Neoplasms, Esophageal Neoplasms, and Prostate Neoplasms have been confirmed by experimental literatures, respectively. Moreover, RKNNMDA could be used to predict potential miRNAs for diseases without any known miRNAs, and it is anticipated that RKNNMDA would be of great use for novel miRNA-disease association identification.

HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ±â€¯0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA.

LncRNADisease: a database for long-non-coding RNA-associated diseases.

In this article, we describe a long-non-coding RNA (lncRNA) and disease association database (LncRNADisease), which is publicly accessible at http://cmbi.bjmu.edu.cn/lncrnadisease. In recent years, a large number of lncRNAs have been identified and increasing evidence shows that lncRNAs play critical roles in various biological processes. Therefore, the dysfunctions of lncRNAs are associated with a wide range of diseases. It thus becomes important to understand lncRNAs' roles in diseases and to identify candidate lncRNAs for disease diagnosis, treatment and prognosis. For this purpose, a high-quality lncRNA-disease association database would be extremely beneficial. Here, we describe the LncRNADisease database that collected and curated approximately 480 entries of experimentally supported lncRNA-disease associations, including 166 diseases. LncRNADisease also curated 478 entries of lncRNA interacting partners at various molecular levels, including protein, RNA, miRNA and DNA. Moreover, we annotated lncRNA-disease associations with genomic information, sequences, references and species. We normalized the disease name and the type of lncRNA dysfunction and provided a detailed description for each entry. Finally, we developed a bioinformatic method to predict novel lncRNA-disease associations and integrated the method and the predicted associated diseases of 1564 human lncRNAs into the database.

Novel human lncRNA-disease association inference based on lncRNA expression profiles.

MOTIVATION: More and more evidences have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Therefore, mutations and dysregulations of these lncRNAs would contribute to the development of various complex diseases. Developing powerful computational models for potential disease-related lncRNAs identification would benefit biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention. RESULTS: In this article, we proposed the assumption that similar diseases tend to be associated with functionally similar lncRNAs. Then, we further developed the method of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) in the semisupervised learning framework. Although known disease-lncRNA associations in the database are rare, LRLSLDA still obtained an AUC of 0.7760 in the leave-one-out cross validation, significantly improving the performance of previous methods. We also illustrated the performance of LRLSLDA is not sensitive (even robust) to the parameters selection and it can obtain a reliable performance in all the test classes. Plenty of potential disease-lncRNA associations were publicly released and some of them have been confirmed by recent results in biological experiments. It is anticipated that LRLSLDA could be an effective and important biological tool for biomedical research. AVAILABILITY: The code of LRLSLDA is freely available at http://asdcd.amss.ac.cn/Software/Details/2.

Mental states in caregivers toward people with Alzheimer's disease at different stages.

Introduction: Caring for people with Alzheimer's disease (AD) is burdensome, especially when family members act as caregivers. This multicenter survey first aimed to investigate caregivers' mental states as well as its influencing factors in caring for people with different severities of AD in China. Methods: People with AD and their caregivers from 30 provincial regions in mainland China were enrolled from October 2020 to December 2020 to be surveyed for caregivers' mental states and living conditions, as well as caregivers' attitudes toward treatment and caring. Logistic regression was used to explore the factors that influence the positive and negative states of caregivers who care for people with different stages of AD. Results: A total of 1,966 valid questionnaires were analyzed (mild AD: 795, moderate AD: 521, severe AD: 650). A total of 73.6% of caregivers maintained normal states (mild group: 71.9%, moderate group: 73.9%, severe group: 75.2%; X2 = 2.023, p = 0.364), and the proportions of caregivers with positive and negative states were 26.3% (mild group: 38.4%, moderate group: 24.6%, severe group: 13.1%; X2 = 119.000, p < 0.001) and 36.5% (mild group: 25.2%, moderate group: 36.9%, severe group: 50.2%; X2 = 96.417, p < 0.001), respectively. The major factors that both influenced caregivers' positive and negative states were the severity of AD, perceived efficacy of treatment, safety issues after AD dementia diagnosis and perceived social support (p < 0.005), while neuropsychiatric symptoms causing stress in caregivers (p < 0.001) only affected the negative states of caregivers. The results of further analysis according to disease severity showed that safety issues after AD dementia diagnosis (p < 0.005) only made significant differences in the mild-to-moderate group. Conclusion: To reduce negative states and promote positive states among caregivers, flexible and sensitive caregiving support could be built on caregivers' demands in caring for people with different stages of AD. The support of emotion, social functioning and nursing skills is one of the significant ways for health workers to enhance caregivers' competency.

A New Estimation Method for the Biological Interaction Predicting Problems.

For the past decades, computational methods have been developed to predict various interactions in biological problems. Usually these methods treated the predicting problems as semi-supervised problem or positive-unlabeled(PU) learning problem. Researchers focused on the prediction of unlabeled samples and hoped to find novel interactions in the datasets they collected. However, most of the computational methods could only predict a small proportion of undiscovered interactions and the total number was unknown. In this paper, we developed an estimation method with deep learning to calculate the number of undiscovered interactions in the unlabeled samples, derived its asymptotic interval estimation, and applied it to the compound synergism dataset, drug-target interaction(DTI) dataset and MicroRNA-disease interaction dataset successfully. Moreover, this method could reveal which dataset contained more undiscovered interactions and would be a guidance for the experimental validation. Furthermore, we compared our method with some mixture proportion estimators and demonstarted the efficacy of our method. Finally, we proved that AUC and AUPR were related with the number of undiscovered interactions, which was regarded as another evaluation indicator for the computational methods.

[Effects of chemotherapy combined with Chinese herbal medicine Kangliu Zengxiao decoction on tumor markers of patients with advanced non-small-cell lung cancer: a randomized, controlled trial].

BACKGROUND: Tumor markers are widely used in clinical practice and have become important indicators in assessing cancer progress. There is increasing concern that chemotherapy combined with traditional Chinese medicine has effects in decreasing the level of tumor markers. OBJECTIVE: To investigate the effects of chemotherapy combined with Kangliu Zengxiao Decoction (KLZX), a compound Chinese herbal drug, on tumor markers carbohydrate antigen 50 (CA 50), cytokeratin 19 fragment (CYFRA21-1) and carcinoembryonic antigen (CEA) in patients with advanced non-small-cell lung cancer (NSCLC) and to explore the relationships between clinical efficacy and tumor markers. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: Patients were included from Punan Hospital of Shanghai Pudong New District and Longhua Hospital between October 2008 and December 2009. Seventy-four subjects with advanced NSCLC were randomly assigned into treatment group (n=37) and control group (n=37). Patients in the control group were treated with chemotherapy alone while patients in the treatment group were treated with chemotherapy combined with KLZX. Chemotherapy of NP (vinorelbine + cisplatin) was given for two cycles and patients in the treatment group were administered with KLZX during chemotherapy. MAIN OUTCOME MEASURES: Levels of CA50, CYFRA21-1 and CEA before and after treatment were evaluated and the relationship between changes in levels of tumor makers and tumor size, clinical symptoms and living condition score (Karnofsky score) was analyzed. RESULTS: No patients achieved a complete remission. The disease control rates (complete remission (CR)+partial remission (PR)+no change (NC)) were 89.20% (33/37) and 70.30% (26/37) in the treatment and control group respectively (P<0.05). The levels of CA50, CYFRA21-1 and CEA were clearly decreased in the treatment group after treatment (P<0.05) while also decreased in the patients without progression of disease. There were no obvious changes of CA50, CYFRA21-1 and CEA in the control group, and there was even a trend of increase. Furthermore, the improvement rates of clinical syndrome were 51% (19/37) vs 11% (4/37) (P<0.05) in the treatment group and control group respectively. The total response rates of quality of life were 91.89% (34/37) vs 56.76% (21/37) (P<0.01) in the treatment and control group respectively. CONCLUSION: Combined chemotherapy with KLZX in treating advanced NSCLC can acquire better stabilizing tumor foci, decrease levels of tumor markers and improve the clinical symptoms and Karnofsky score.

Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly affects the elderly; early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most brain regions related to AD were identified based on imaging methods, and only some atrophic brain regions could be identified. In this work, the authors used mathematical models to identify the potential brain regions related to AD. In this study, 20 patients with AD and 13 healthy controls (non-AD) were recruited by the neurology outpatient department or the neurology ward of Peking University First Hospital from September 2017 to March 2019. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, the authors set a new local feature index 2hop-connectivity to measure the correlation between different regions. Compared with the traditional graph theory index, 2hop-connectivity exploits the higher-order information of the graph structure. And for this purpose, the authors proposed a novel algorithm called 2hopRWR to measure 2hop-connectivity. Then, a new index global feature score (GFS) based on a global feature was proposed by combing five local features, namely degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are related to AD. As a result, the top ten brain regions identified using the GFS scoring difference between the AD and the non-AD groups were associated to AD by literature verification. The results of the literature validation comparing GFS with the local features showed that GFS was superior to individual local features. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the Mini-Mental State Examination (MMSE) scale and the Montreal Cognitive Assessment (MoCA) scale. Therefore, the authors believe the GFS can also be used as a new biomarker to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method for network analysis in other domains.

Corrigendum: IDLDA: An Improved Diffusion Model for Predicting LncRNA-Disease Associations.

[This corrects the article DOI: 10.3389/fgene.2019.01259.].

Simple probabilistic algorithm for detecting community structure.

With the growing number of available social and biological networks, the problem of detecting the network community structure is becoming more and more important which acts as the first step to analyze these data. The community structure is generally regarded as that nodes in the same community tend to have more edges and less if they are in different communities. We propose a simple probabilistic algorithm for detecting community structure which employs expectation-maximization (SPAEM). We also give a criterion based on the minimum description length to identify the optimal number of communities. SPAEM can detect overlapping nodes and handle weighted networks. It turns out to be powerful and effective by testing simulation data and some widely known data sets.

IDLDA: An Improved Diffusion Model for Predicting LncRNA-Disease Associations.

It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA-disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA-disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA-disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.