Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

Structural Variants and Ultralow Detection Ability for Tryptamine in Two Polymorphs of a Zincophosphite Framework.

Two organic-inorganic hybrid zinc phosphites incorporating 1,2,4,5-tetrakis(imidazol-1-ylmethyl)benzene (TIMB) molecules were synthesized under hydro(solvo)thermal methods and structurally characterized by single-crystal X-ray diffraction (SCXD). Interestingly, the solvent ratio of water to dimethylformamide induced the formation of a new compound of Zn2(TIMB)0.5(HPO3)2·3H2O (1) and our previously reported structure of Zn2(TIMB)0.5(HPO3)2·H2O (2). Additionally, their dehydrated crystals (1a and 2a) were prepared through heat treatment at 150 °C. SCXD and powder X-ray diffraction showed that all four compounds share the same framework formula of Zn2(TIMB)0.5(HPO3)2 but exhibit a huge difference in their inorganic components and final structures. In 1 and 1a, the inorganic units formed two-dimensional zincophosphite layers, while in 2 and 2a, they formed one-dimensional chains. The inorganic parts of 1 (1a) and 2 (2a) were bridged with TIMB linkers, resulting in 3D structures with rectangular and tubular windows, respectively. Furthermore, 1 was coated on the screen-printed carbon electron as a hybrid material, displaying excellent performance while having a linear relationship with an R2 value of 0.99 within the concentration range of 10-10 to 10-6 mol/L for detecting tryptamine (Try) molecules. Moreover, the results showed that 1 exhibits an ultralow limit of detection of 5.43 × 10-11 mol/L and high specificity toward Try over histamine, ascorbic acid, uric acid, and glucose. The synthesis, structural diversity, stability, and sensing ability are also discussed.

CNNGRN: A Convolutional Neural Network-Based Method for Gene Regulatory Network Inference From Bulk Time-Series Expression Data.

Gene regulatory networks (GRNs) participate in many biological processes, and reconstructing them plays an important role in systems biology. Although many advanced methods have been proposed for GRN reconstruction, their predictive performance is far from the ideal standard, so it is urgent to design a more effective method to reconstruct GRN. Moreover, most methods only consider the gene expression data, ignoring the network structure information contained in GRN. In this study, we propose a supervised model named CNNGRN, which infers GRN from bulk time-series expression data via convolutional neural network (CNN) model, with a more informative feature. Bulk time series gene expression data imply the intricate regulatory associations between genes, and the network structure feature of ground-truth GRN contains rich neighbor information. Hence, CNNGRN integrates the above two features as model inputs. In addition, CNN is adopted to extract intricate features of genes and infer the potential associations between regulators and target genes. Moreover, feature importance visualization experiments are implemented to seek the key features. Experimental results show that CNNGRN achieved competitive performance on benchmark datasets compared to the state-of-the-art computational methods. Finally, hub genes identified based on CNNGRN have been confirmed to be involved in biological processes through literature.

Disease biomarker identification based on sample network optimization.

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.

Determination of the Bacterial Community of Mustard Pickle Products and Their Microbial and Chemical Qualities.

We assessed the microbial and chemical qualities and microbiomes of 14 mustard pickle products coded sequentially from A to N and sold in traditional Taiwanese markets. The results showed that the aerobic plate count and lactic acid bacteria count of commercially available mustard pickle products were 2.18-4.01 and <1.0-3.77 log CFU/g, respectively. Moreover, no coliform bacteria, Escherichia coli, Staphylococcus aureus, Salmonella spp., or Listeria monocytogenes were detected in any of the samples. Analysis of the chemical quality showed that the sulfite content of all samples exceeded 30 ppm, which is the food additive limit in Taiwan. Furthermore, the mean contents of eight biogenic amines in the mustard pickle product samples were below 48.0 mg/kg. The results of high-throughput sequencing showed that the dominant bacterial genera in sample A were Proteus spp. (25%), Vibrio (25%), and Psychrobacter (10%), in sample C they were Weissella (62%) and Lactobacillus (15%), in sample E it was Lactobacillus (97%), and in sample J it was Companilactobacillus (57%). Mustard pickle product samples from different sources contained different microbiomes. The dominant bacterial family was Lactobacillaceae in all samples except for sample A. In contrast, the microbiome of sample A mainly consisted of Morganellaceae and Vibrionaceae, which may have resulted from environmental contamination during storage and sales. The result of this work suggests it may be necessary to monitor sulfite levels and potential sources of bacterial contamination in mustard pickle products, and to take appropriate measures to rule out any public health risks.

Replacing the Addition of Sulfite in Mustard Pickle Products by High-Hydrostatic-Pressure Processing to Delay Quality Deterioration during Storage.

This study aimed to assess the use of the high-hydrostatic-pressure (HHP) method (200-600 MPa, 5 min) for bleaching mustard pickle products as an alternative to the conventional method of sulfite addition. The aerobic plate count (APC) and lactic acid bacteria count (LAB) of the samples decreased with the increase in pressure, and the yeast count decreased to no detectable levels. Next, compared with the control group (no high-pressure treatment) the L* (lightness), W (whiteness), ΔE (color difference), and texture (hardness and chewiness) of the HHP-processed samples, which increased significantly with increasing pressure, while the a* (redness) and b* (yellowness) values decreased slightly. This indicates that HHP processing gave the mustard pickle a harder texture and a brighter white color and appearance. Furthermore, when the mustard pickle was treated with HHP 400 and 600 MPa for 5 min and stored at 25 °C for 60 days, it was found that the APC and LAB counts in the HHP-processed group recovered rapidly and did not differ from those in the control group (the non-HHP treated group) but significantly delayed the growth of yeast, the increase in pH value, and total volatile basic nitrogen (TVBN). The high-throughput sequencing (HTS) analysis revealed that the predominant bacterial genera in the non-HHP-treated mustard pickle were Lactiplantibacillus (74%), Lactilactobacillus (12%), and Levilactobacillus (6%); after 60 days of storage, Companilactobacillus (80%) became dominant. However, after 60 days of storage, Lactiplantibacillus (92%) became dominant in the samples processed at 400 MPa, while Levilactobacillus (52%), Pediococcus (17%), and Lactiplantibacillus (17%) became dominant in the samples processed at 600 MPa. This indicated that the HHP treatment changed the lactic acid bacterial flora of the mustard pickle during the storage period. Overall, it is recommended to treat the mustard pickle with HHP above 400 MPa for 5 min to improve its texture and color and delay the deterioration of quality during storage. Therefore, HHP technology has the potential to be developed as a treatment technique to replace the addition of sulfite.

iPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory.

BACKGROUND AND OBJECTIVE: The promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are located upstream at the transcription start site, which is used to initiate downstream gene expression. So far, promoter identification is mainly achieved by biological methods, which often require more effort. It has become a more effective classification and prediction method to identify promoter types through computational methods. METHODS: In this study, we proposed a new capsule network and recurrent neural network hybrid model to identify promoters and predict their strength. Firstly, we used one-hot to encode DNA sequence. Secondly, we used three one-dimensional convolutional layers, a one-dimensional convolutional capsule layer and digit capsule layer to learn local features. Thirdly, a bidirectional long short-time memory was utilized to extract global features. Finally, we adopted the self-attention mechanism to improve the contribution of relatively important features, which further enhances the performance of the model. RESULTS: Our model attains a cross-validation accuracy of 86% and 73.46% in prokaryotic promoter recognition and their strength prediction, which showcases a better performance compared with the existing approaches in both the first layer promoter identification and the second layer promoter's strength prediction. CONCLUSIONS: our model not only combines convolutional neural network and capsule layer but also uses a self-attention mechanism to better capture hidden information features from the perspective of sequence. Thus, we hope that our model can be widely applied to other components.

[Effects of Multicomponent Case Management on the Problem Behaviors of Patients With Dementia and Family Caregivers' Distress, Self-Efficacy, Depression, Burden and Health Promotion Behaviors].

BACKGROUND: Few of the interventions currently available for family caregivers (FCGs) of persons with dementia (PWDs) with long-term follow-ups have a grounding in theory and incorporate multicomponent case management formats. PURPOSE: Based on Pearlin's Caregiving and Stress Process model, this study was developed to examine the effectiveness of a family-centered case management program for PWDs with early to moderate dementia in terms of reducing PWDs behavioral problems and improve FCG outcomes, including distress, self-efficacy, depression, caregiver burden, and health-promoting behaviors. METHODS: This randomized, single-blind, parallel-controlled trial included 76 dyads of PWDs and their FCGs. The dyads were recruited from outpatient clinics at dementia centers in three district hospitals in northern Taiwan. The dyads were randomly assigned to the intervention group (IG, n = 39) and control group (CG, n = 37). The dyads in the IG received a four-month intervention with two home or clinic visits and two telephone interviews. The multi-component interventions provided assessment, education, consultations, support, and referrals to long-term care resources. The CG received routine care and two social phone calls. Data were collected upon enrollment (T0 = baseline) and at 4-,6-, and 12-months post-intervention (T1, T2, and T3, respectively). Generalized estimating equations were conducted to analyze the effects of the intervention. RESULTS: By controlling for the interaction between group and time, we made a comparison between IG and the CG. The results showed significant improvements from baseline measures in behavioral problems in the PWDs for mood, psychosis, and social engagement, and improvements in the FCGs for distress and self-efficacy for obtaining respite as well as for better control of distressing thoughts, feelings of depression, caregiver burden, and overall health promoting behaviors at T1 and T2 (p < 0.5). Significant improvements were also found in the IG for psychomotor regulation among PWDs and the self-efficacy of FCGs in managing the PWDs' disturbing behaviors and health promotion behaviors for nutrition at T1 (p < 0.5). There were no significant improvements in the outcome variables at T3. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: Significant interactions between group and time were found at the 6-month assessment (T2) for improvements in problem behaviors of PWDs and depression, caregiver burden, and distress in the FCGs. Positive effects on self-efficacy and health promotion behaviors among the FCGs were also achieved. The results suggest that a multicomponent case management intervention should be referenced in dementia care policymaking for FCGs and PWDs.

Promoter prediction in nannochloropsis based on densely connected convolutional neural networks.

Promoter is a key DNA element located near the transcription start site, which regulates gene transcription by binding RNA polymerase. Thus, the identification of promoters is an important research field in synthetic biology. Nannochloropsis is an important unicellular industrial oleaginous microalgae, and at present, some studies have identified some promoters with specific functions by biological methods in Nannochloropsis, whereas few studies used computational methods. Here, we propose a method called DNPPro (DenseNet-Predict-Promoter) based on densely connected convolutional neural networks to predict the promoter of Nannochloropsis. First, we collected promoter sequences from six Nannochloropsis strains and removed 80% similarity using CD-HIT for each strain to yield a reliable set of positive datasets. Then, in order to construct a robust classifier, within-group scrambling method was used to generate negative dataset which overcomes the limitation of randomly selecting a non-promoter region from the same genome as a negative sample. Finally, we constructed a densely connected convolutional neural network, with the sequence one-hot encoding as the input. Compared with commonly used sequence processing methods, DNPPro can extract long sequence features to a greater extent. The cross-strain experiment on independent dataset verifies the generalization of our method. At the same time, T-SNE visualization analysis shows that our method can effectively distinguish promoters from non-promoters.

Chitin scaffold combined with autologous small nerve repairs sciatic nerve defects.

Although autologous nerve transplantation is the gold standard for treating peripheral nerve defects, it has many clinical limitations. As an alternative, various tissue-engineered nerve grafts have been developed to substitute for autologous nerves. In this study, a novel nerve graft composed of chitin scaffolds and a small autologous nerve was used to repair sciatic nerve defects in rats. The novel nerve graft greatly facilitated regeneration of the sciatic nerve and myelin sheath, reduced atrophy of the target muscle, and effectively restored neurological function. When the epineurium of the small autogenous nerve was removed, the degree of nerve regeneration was similar to that which occurs after autogenous nerve transplantation. These findings suggest that our novel nerve graft might eventually be a new option for the construction of tissue-engineered nerve scaffolds. The study was approved by the Research Ethics Committee of Peking University People's Hospital (approval No. 2019PHE27) on October 18, 2019.

Application of two occupational risk assessment methods in noise assessment of a beer manufacturing enterprise / 公共卫生与预防医学

Objective To study the applicability of two different occupational health risk assessment methods for noise positions in a beer manufacturing enterprise. Methods An occupational health investigation along with the detection of occupational hazard factors were carried out in the workplace of a beer manufacturing enterprise in Wuhan. Workers with 8-hour working day equivalent sound level (LEX,8 h) ≥ 80 dB (A) were selected as research subjects. The “Guidelines for Noise Occupational Disease Risk Management” method and occupational hazard risk index method were used to assess the risk of noise jobs in the beer manufacturing company. The assessment results of the two methods were compared. Results The noise exposure level of the enterprise was between 81.2 and 91.2dB(A). The guideline method predicted that the risk of high-frequency hearing loss and noise deafness for wine bottling workers and labelers on the bottling production line was high after 35 years exposure to noise. Washing，inspection and boxing on the bottling production line and bottling up on the canning production line were at medium risk, and others were at low risk. The evaluation results of the occupational hazard risk index method showed that the bottlers, bottling workers, wine inspectors, labelers and boxers on the bottling production line were at medium risk, and other positions were at low risk. Conclusion The occupational hazard risk index method is more comprehensive to consider all the factors of health risk, and the evaluation results are close to the “Guidelines for Noise Occupational Disease Risk Management” method. The guideline method can quantitatively predict the risk of high-frequency hearing loss and noise deafness, and the risk of hearing loss increases with the extension of years of noise exposure.

Identification of driver genes based on gene mutational effects and network centrality.

BACKGROUND: As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. RESULTS: To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. CONCLUSIONS: Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

Double matrix completion for circRNA-disease association prediction.

BACKGROUND: Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient. RESULTS: In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model. CONCLUSION: The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.

Experience of resources management on coronavirus disease 2019 epidemic at a tertiary medical center in Northern Taiwan.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes infectious symptoms including fever, cough, respiratory and gastrointestinal symptoms, and even loss of smell/taste and to date had caused 489 000 people to be infected with 32 000 deaths. This article aims to develop some strategies in dealing with the COVID-19 epidemic to prevent nosocomial infection and ensure the safety of healthcare workforce and employees. METHODS: This is a prospectively registered and retrospective descriptive study investigating the clinical characteristics, results of diagnostic tests, and patients' disposition from February 1, 2020, to April 30, 2020, at a tertiary medical center in Northern Taiwan. RESULTS: There is no nosocomial spreading of SARS-CoV-2 in our facility. The following strategies were followed: information transparency; epidemic prevention resources planning by authorities; multidisciplinary cooperation; informative technologies; immigration quarantine policies; travel restrictions; management of diversion/subdivision; self-health monitoring; social distancing; screening of travel, occupation, contact, and cluster (TOCC) history; traffic control bundling (TCB); training of using personal protective equipment; real-name visiting management; and employee care. The patients' basic characteristics and diagnostic results were gathered. Of the 3832 cases, about 25.9% had travel history. Most of them were traveling to Asia (419 people/time, 10.9%) and from China (256 people/time, 6.7%). Meanwhile, healthcare personnel accounted for 316 people/time (8.3%) and cleaning personnel, 6 people/time (0.16%). The 36 cases who care or have contact with confirmed cases have negative results from the COVID-19 test. The most frequent symptoms were fever and upper respiratory infection followed by gastrointestinal symptoms. CONCLUSION: The above strategies were followed. Patients were stratified based on the risk of TOCC history assessment to ensure the safety of healthcare personnel and patients' appropriate and timely medical services.

Prioritizing Cancer Genes Based on an Improved Random Walk Method.

Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.

Identifying Disease-Gene Associations With Graph-Regularized Manifold Learning.

Complex diseases are known to be associated with disease genes. Uncovering disease-gene associations is critical for diagnosis, treatment, and prevention of diseases. Computational algorithms which effectively predict candidate disease-gene associations prior to experimental proof can greatly reduce the associated cost and time. Most existing methods are disease-specific which can only predict genes associated with a specific disease at a time. Similarities among diseases are not used during the prediction. Meanwhile, most methods predict new disease genes based on known associations, making them unable to predict disease genes for diseases without known associated genes.In this study, a manifold learning-based method is proposed for predicting disease-gene associations by assuming that the geodesic distance between any disease and its associated genes should be shorter than that of other non-associated disease-gene pairs. The model maps the diseases and genes into a lower dimensional manifold based on the known disease-gene associations, disease similarity and gene similarity to predict new associations in terms of the geodesic distance between disease-gene pairs. In the 3-fold cross-validation experiments, our method achieves scores of 0.882 and 0.854 in terms of the area under of the receiver operating characteristic (ROC) curve (AUC) for diseases with more than one known associated genes and diseases with only one known associated gene, respectively. Further de novo studies on Lung Cancer and Bladder Cancer also show that our model is capable of identifying new disease-gene associations.

MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks.

BACKGROUND: Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. METHODS: We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. RESULTS: We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. CONCLUSIONS: We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation's effect on the transcriptional network, but also assesses the differential expression's effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.

Proximal femoral nails antirotation and dynamic hip screws for fixation of unstable intertrochanteric fractures of femur: A meta-analysis / 北京大学学报(医学版)

OBJECTIVE@#To evaluate the efficacy and safety of proximal femoral nail antirotation (PFNA) and dynamic hip screw (DHS) for unstable intertrochanteric fractures using meta-analysis.@*METHODS@#The PubMed, Embase, Cocharane Central Register of Controlled Trials, Google Scholar, China Science and Technology Papers and Citation Database (CSTPCD) and China Journal Full-text Database (CNKI) were searched for published randomized controlled trials before January 1, 2019. Two researchers independently screened the literature in the light of the inclusion and exclusion criteria, evaluated the quality of the studies and extracted the data which were consisted of clinical efficacy indexes, such as incision length, operation time,intraoperative blood loss, weight-bearing time,fracture-healing time, Harris hip score and safety indicators like complications. Meta-analysis was performed with the Revman 5.3 software provided by Cochrane Community in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) standard.@*RESULTS@#Nine randomized controlled trials met the requirement with a total of 779 patients, of whom 383 were fixed with PFNA and 396 with DHS. Meta-analysis demonstrated that PFNA was associated with smaller surgical incision length [MD=-7.43, 95%CI (-9.31, -5.55), P<0.05], shorter operation time [MD=-22.76, 95%CI (-29.57, -11.95), P<0.05], less intraoperative blood loss [MD=-216.34, 95%CI (-275.18, - 157.49), P<0.05], earlier weight bearing after surgery [MD=-12.34, 95%CI (-17.71, -6.97), P<0.05], shorter fracture healing time [MD=-5.00, 95%CI (-7.73, -2.26), P<0.05], higher postoperative Harris hip score [MD=12.22, 95%CI (3.88, 20.55), P<0.05], higher rate of excellent Harris hip score [OR=3.56, 95%CI (1.44, 8.81), P<0.05] and lower incidence rate of postoperative complications [OR=0.48, 95%CI (0.33, 0.70), P<0.05], such as hip varus, wound infection, urinary tract infection, pulmonary infection, pressure sore, deep vein thrombosis, pulmonary embolism, heart failure and cerebral infraction when compared with DHS. No statistical difference was shown between the groups when it came to subgroup analysis by age. However, there was no significant difference (P>0.05) in the duration of hospitalization and the complications resulting in the occurrences of internal fixation loosening, such as femoral shaft fracture (during or post operation), internal fixation fracture, cut-out, displacement or retraction.@*CONCLUSION@#Current published evidence supports the superiority of PFNA to DHS for unstable intertrochanteric fractures in terms of clinical efficacy. The conclusion was limited because of the relatively low quality of evidence with low strength of confidence. Large scale and high-quality randomized controlled trials are required to validate the safety of PFNA and DHS for unstable intertrochanteric fractures.