GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.Comment: AAAI 2019; change the template and fix some typo

A kernel-free L1 norm regularized ν-support vector machine model with application

With a view to overcoming a few shortcomings resulting from the kernel-based SVM models, these kernel-free support vector machine (SVM) models are newly promoted and researched. With the aim of deeply enhancing the classification accuracy of present kernel-free quadratic surface support vector machine (QSSVM) models while avoiding computational complexity, an emerging kernel-free ν-fuzzy reduced QSSVM with L1 norm regularization model is proposed. The model has well-developed sparsity to avoid computational complexity and overfitting and has been simplified as these standard linear models on condition that the data points are (nearly) linearly separable. Computational tests are implemented on several public benchmark datasets for the purpose of showing the better performance of the presented model compared with a few known binary classification models. Similarly, the numerical consequences support the more elevated training effectiveness of the presented model in comparison with those of other kernel-free SVM models. What`s more, the presented model is smoothly employed in lung cancer subtype diagnosis with good performance, by using the gene expression RNAseq-based lung cancer subtype (LUAD/LUSC) dataset in the TCGA database

Recombination Monophosphoryl Lipid A-Derived Vacosome for the Development of Preventive Cancer Vaccines

Recently, there has been an increasing interest for utilizing the host immune system to fight against cancer. Moreover, cancer vaccines, which can stimulate the host immune system to respond to cancer in the long term, are being investigated as a promising approach to induce tumor-specific immunity. In this work, we prepared an effective cancer vaccine (denoted as vacosome) by reconstructing the cancer cell membrane, monophosphoryl lipid A as a toll-like receptor 4 agonist, and egg phosphatidylcholine. The vacosome triggered and enhanced bone marrow dendritic cell maturation as well as stimulated the antitumor response against breast cancer 4T1 cells in vitro. Furthermore, an immune memory was established in BALB/c mice after three-time preimmunization with the vacosome. After that, the immunized mice showed inhibited tumor growth and prolonged survival period (longer than 50 days). Overall, our results demonstrate that the vacosome can be a potential candidate for clinical translation as a cancer vaccine.Peer reviewe

QTL Mapping of Combining Ability and Heterosis of Agronomic Traits in Rice Backcross Recombinant Inbred Lines and Hybrid Crosses

BACKGROUND: Combining ability effects are very effective genetic parameters in deciding the next phase of breeding programs. Although some breeding strategies on the basis of evaluating combining ability have been utilized extensively in hybrid breeding, little is known about the genetic basis of combining ability. Combining ability is a complex trait that is controlled by polygenes. With the advent and development of molecular markers, it is feasible to evaluate the genetic bases of combining ability and heterosis of elite rice hybrids through QTL analysis. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we first developed a QTL-mapping method for dissecting combining ability and heterosis of agronomic traits. With three testcross populations and a BCRIL population in rice, biometric and QTL analyses were conducted for ten agronomic traits. The significance of general combining ability and special combining ability for most of the traits indicated the importance of both additive and non-additive effects on expression levels. A large number of additive effect QTLs associated with performance per se of BCRIL and general combining ability, and dominant effect QTLs associated with special combining ability and heterosis were identified for the ten traits. CONCLUSIONS/SIGNIFICANCE: The combining ability of agronomic traits could be analyzed by the QTL mapping method. The characteristics revealed by the QTLs for combining ability of agronomic traits were similar with those by multitudinous QTLs for agronomic traits with performance per se of BCRIL. Several QTLs (1-6 in this study) were identified for each trait for combining ability. It demonstrated that some of the QTLs were pleiotropic or linked tightly with each other. The identification of QTLs responsible for combining ability and heterosis in the present study provides valuable information for dissecting genetic basis of combining ability

The Somatic Genomic Landscape of Glioblastoma

We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer

Interfacial self-assembled GR/GO ultrathin membranes on a large scale for molecular sieving

Graphene oxide (GO) has superior molecular sieving abilities due to its unimpeded two-dimensional (2D) nano-channels and nacre-like lamellar structure. However, it remains a huge challenge to fully exploit its unmatched features to construct a GO-based membrane on a large scale for excellent screening performance. Here, we display a facile, ultrafast and environmentally friendly strategy to design an ultrathin (graphene/graphene oxide@polyetherimide) GR/GO@PEI composite membrane under the collective effects of a hydrophilic PEI molecular-bridge and an ordered GR/GO laminar structure. Excitingly, this composite membrane presents extraordinary structural stability, even when transferred to any given substrate, regardless of its chemical components, structure, or specification. Furthermore, this membrane shows high permeance of up to 191 L m(-2)h(-1)bar(-1)and over 99% rejection of Congo red, far superior to the majority of GO-based separation membranes reported so far. In addition, it can withstand several types of physical damage and chemical corrosion, as well as being further able to realize the purification of actual, complex, multi-component, domestic sewage. This work will open the door to developing scalable 2D lamellar membranes with ultrafast and precise separation channels for practical water purification