DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining

Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method. Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes. In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM. Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.

A Machine Learning Method for Drug Combination Prediction

Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-based methodologies are extremely costly in time and money. Many computational methods have been proposed to address these problems by starting from existing drug combinations. However, in most cases, only molecular structure information is included, which covers too limited a set of drug characteristics to efficiently screen drug combinations. Here, we integrated similarity-based multifeature drug data to improve the prediction accuracy by using the neighbor recommender method combined with ensemble learning algorithms. By conducting feature assessment analysis, we selected the most useful drug features and achieved 0.964 AUC in the ensemble models. The comparison results showed that the ensemble models outperform traditional machine learning algorithms such as support vector machine (SVM), naïve Bayes (NB), and logistic regression (GLM). Furthermore, we predicted 7 candidate drug combinations for a specific drug, paclitaxel, and successfully verified that the two of the predicted combinations have promising effects.

Numerical and experimental study on wind environment at near tower region of a bridge deck

The Large Eddy Simulation (LES) turbulence model was used to investigate the wind environment over the deck near bridge tower and was verified using the wind tunnel tests. Compared with the wind tunnel tests, the computational fluid dynamics (CFD) approach was more convenient for the investigations of the local wind field. It was found that the influence of bridge tower on the wind flow can increase rapidly the wind speed on vehicles while bearing off a narrow zone near the tower. The dangerous situation can be effectively compromised by installing a proper local windshield barrier (WSB) with varying heights and porosity ratios along the bridge span. The length of the influence region of tower on the wind environment over the bridge deck was about 7 times of the tower width, implying a proper length of local windshield barriers on each side of the tower. Parametric studies demonstrated that the length of local WSB with different porosity ratios could affect the slope of equivalent wind speeds, indicating that the shorter the length of local WSB was, the rapider the wind speed of the tower influence region varied.

Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates

Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.

Characteristics and formation of typical winter haze in Handan, one of the most polluted cities in China

Handan, a city within the North China Plain (NCP) region, is a typical city influenced by regional particulate matter (PM) pollution. One-year hourly semi-continuous observation was carried out in 2015 in Handan with the aim of identifying the chemical composition and variations in PM_2.5. Moreover, the concentration of aerosol precursors, meteorological factors, and secondary transformations are considered. The results demonstrate that the annual average PM_2.5 concentration in Handan is 122.35μgm^-3, approximately 3.5 times higher than the Chinese National Ambient Air Quality Standard (NAAQS) (35μgm^-3), and only 12days were below the guideline. As expected, PM concentrations are highest in winter, especially in December. In addition, we measure the concentrations of five species commonly found in PM, nitrate, sulfate, ammonium, inorganic carbon, and organic carbon. Of these, nitrate and sulfate account for the largest proportion of PM_2.5; during periods when the PM_2.5 concentration was below 400μgm^-3, nitrate dominates, while above this concentration, sulfate dominate. This is likely related to the nitrogen and sulfur oxidation ratios, which are in turn, especially the sulfur oxidation ratio, driven by high relative humidity (>60%). In addition, haze events are driven by other meteorological conditions, wind speed and direction, where low wind speeds from the south and southwest enable pollutant accumulation, which are infrequently interspersed with brief periods with high wind speeds that promote pollutant dispersal. Even though Handan is among the ten most polluted cities in China with regard to air pollution, few studies beyond model simulations have analyzed air pollutant concentrations in this city. Therefore, this study makes a significant contribution to understanding air pollution in Handan, which can further be used to improve our understanding of regional pollution in the highly populated North China Plain. These results have implications for the creation of policies and legislation, as well as other pollution control measures.

Recycling harvest water to cultivate Chlorella zofingiensis under nutrient limitation for biodiesel production

Harvest water recycling for Chlorella zofingiensis re-cultivation under nutrient limitation was investigated. Using 100% harvest water, four cultures were prepared: Full medium, P-limited medium, N-limited medium and N- and P-limited medium, while another full medium was also prepared using 50% harvest water. The results showed that the specific growth rate and biomass productivity ranged from 0.289 to 0.403 day(-1) and 86.30 to 266.66 mg L(-1) day(-1), respectively. Nutrient-limited cultures witnessed much higher lipid content (41.21-46.21% of dry weight) than nutrient-full cultures (26% of dry weight). The N- and P-limited medium observed the highest FAME yield at 10.95% of dry weight, while the N-limited culture and P-limited culture shared the highest biodiesel productivity at 20.66 and 19.91 mg L(-1) day(-1), respectively. The experiment on harvest water recycling times demonstrated that 100% of the harvest water could be recycled twice with the addition of sufficient nutrients.