Numerical study of sediment scour at meander flume outlet of boxed culvert diversion work.

Background: Sediment scour at downstream of hydraulic structures is one of the main reasons threatening its stability. Several soil properties and initial input data have been studied to investigate its influence on scour hole geometry by both physical and numerical models. However, parameters of resistance affecting sedimentation and erosion phenomena have not been carried out in the literature.

DrGA: cancer driver gene analysis in a simpler manner.

Background: To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one.

Identification and Validation of a Novel Three Hub Long Noncoding RNAs With m6A Modification Signature in Low-Grade Gliomas.

It has been evident that N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) involves regulating tumorigenesis, invasion, and metastasis for various cancer types. In this study, we sought to pick computationally up a set of 13 hub m6A-lncRNAs in light of three state-of-the-art tools WGCNA, iWGCNA, and oCEM, and interrogated their prognostic values in brain low-grade gliomas (LGG). Of the 13 hub m6A-lncRNAs, we further detected three hub m6A-lncRNAs as independent prognostic risk factors, including and .

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules.

Background: When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated.

A network-based method for predicting disease-associated enhancers.

Background: Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases.

Re-Identification of Patient Subgroups in Uveal Melanoma.

Uveal melanoma (UM) is a comparatively rare cancer but requires serious consideration since patients with developing metastatic UM survive only for about 6-12 months. Fortunately, increasingly large multi-omics databases allow us to further understand cancer initiation and development. Moreover, previous studies have observed that associations between copy number aberrations (CNA) or methylation (MET) versus messenger RNA (mRNA) expression have affected these processes.

Integrating Molecular Graph Data of Drugs and Multiple -Omic Data of Cell Lines for Drug Response Prediction.

Previous studies have either learned drug's features from their string or numeric representations, which are not natural forms of drugs, or only used genomic data of cell lines for the drug response prediction problem. Here, we proposed a deep learning model, GraOmicDRP, to learn drug's features from their graph representation and integrate multiple -omic data of cell lines. In GraOmicDRP, drugs are represented as graphs of bindings among atoms; meanwhile, cell lines are depicted by not only genomic but also transcriptomic and epigenomic data.

Similarity Calculation, Enrichment Analysis, and Ontology Visualization of Biomedical Ontologies using UFO.

The rapid growth of biomedical ontologies observed in recent years has been reported to be useful in various applications. In this article, we propose two main-function protocols-term-related and entity-related-with the three most common ontology analyses, including similarity calculation, enrichment analysis, and ontology visualization, which can be done by separate methods. Many previously developed tools implementing those methods run on different platforms and implement a limited number of the methods for similarity calculation and enrichment analysis tools for a specific type of biomedical ontology, although any type can be acceptable.

Graph Convolutional Networks for Drug Response Prediction.

Background: Drug response prediction is an important problem in computational personalized medicine. Many machine-learning-based methods, especially deep learning-based ones, have been proposed for this task. However, these methods often represent the drugs as strings, which are not a natural way to depict molecules.

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.

The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer.

The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed.

UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

Background: Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications.

Machine learning-based approaches for disease gene prediction.

Disease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown quickly and covered almost genome and proteome; thus, network-based methods for the problem become prominent.

Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses.

Tyrosine is mainly degraded in the liver by a series of enzymatic reactions. Abnormal expression of the tyrosine catabolic enzyme tyrosine aminotransferase (TAT) has been reported in patients with hepatocellular carcinoma (HCC). Despite this, aberration in tyrosine metabolism has not been investigated in cancer development.

RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network.

Background: The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks.

An update on obesity: Mental consequences and psychological interventions.

Besides physical consequences, obesity has negative psychological effects, thereby lowering human life quality. Major psychological consequences of this disorder includes depression, impaired body image, low self-esteem, eating disorders, stress and poor quality of life, which are correlated with age and gender. Physical interventions, mainly diet control and energy balance, have been widely applied to treat obesity; and some psychological interventions including behavioral therapy, cognitive behavioral therapy and hypnotherapy have showed some effects on obesity treatment.

Drug Response Prediction by Globally Capturing Drug and Cell Line Information in a Heterogeneous Network.

One of the most important problem in personalized medicine research is to precisely predict the drug response for each patient. Due to relationships between drugs, recent machine learning-based methods have solved this problem using multi-task learning models. However, chemical relationships between drugs have not been considered.

An update on physical health and economic consequences of overweight and obesity.

Overweight and obesity (OW and OB) have been on the increase globally and posed health risks to the world's population of all ages, including pre-born babies, children, adolescents, adults and elderly people, via their comorbid conditions. Excellent examples of comorbidities associated with obesity include cancer, cardiovascular diseases (CVD) and type 2 diabetes mellitus (T2DM). In this article, we aimed to review and update scientific evidence regarding the relationships between obesity and its common physical health consequences, including CVD, T2DM, hypertension, ischemic stroke, cancer, dyslipidemia and reproductive disorders.

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs.

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs.

Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model.

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs.

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs.

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes.

HGPEC: a Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network.

Background: Finding gene-disease and disease-disease associations play important roles in the biomedical area and many prioritization methods have been proposed for this goal. Among them, approaches based on a heterogeneous network of genes and diseases are considered state-of-the-art ones, which achieve high prediction performance and can be used for diseases with/without known molecular basis.

Results: Here, we developed a Cytoscape app, namely HGPEC, based on a random walk with restart algorithm on a heterogeneous network of genes and diseases.

Cell source, differentiation, functional stimulation, and potential application of human thermogenic adipocytes in vitro.

Recent investigations have showed that the functional thermogenic adipocytes are present in both infants and adult humans. Accumulating evidence suggests that the coexistence of classical and inducible brown (brite) adipocytes in humans at adulthood and these adipocytes function to generate heat from energy resulting in reducing body fat and improving glucose metabolism. Human thermogenic adipocytes can be differentiated in vitro from stem cells, cell lines, or adipose stromal vascular fraction.

Systems Pharmacology: A Unified Framework for Prediction of Drug-Target Interactions.

Background: Drug discovery is one important issue in medicine and pharmacology area. Traditional methods using target-based approach are usually time-consuming and ineffective. Recently, the problems are approached in a system-level view and therefore it is called systems pharmacology.