BackgroundPredicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.ResultsIn this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.ConclusionsPASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.

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Sentiment analytics, as a computational method to extract emotion and detect polarity, has gained increasing attention in tourism research. However, issues regarding how to properly apply sentiment analytics are seldom addressed in the tourism literature. This study addresses such methodological challenges by employing the metalearning perspective to examine the design effects on predictive accuracy using a sentiment analysis experiment for Chinese travel news. Our results reveal strong interactions among key design factors of sentiment analytics on predictive accuracy; accordingly, this study formulates a metalearning framework to improve predictive accuracy for computational tourism research. Our study attempts to highlight and improve the methodological relevance and appropriateness of sentiment analytics for future tourism studies.

Background: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. Results: We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified. Conclusions: Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.

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Antimicrobial peptides are the promising candidates for withstanding multidrug-resistant bacteria (MDRB) which were caused by the misuse and extensive use of antibiotics. In this research, in vitro activities of one antimicrobial cationic peptide, brevinin-2CE alone and in combination with five kinds of antibiotics were assessed against clinical isolates of extended-spectrum β-lactamase-producing Escherichia coli and methicillin-resistant Staphylococcus aureus. The results showed that most of the combination groups had synergistic effects. Also, it was obvious that brevinin-2CE had more rapid and severe action on the tested MDRBs which demonstrated that brevinin-2CE and the antibiotics had different antimicrobial mechanisms. Thus, it was presumed that the antimicrobial peptides destroyed the bacterial cells via pore formation mechanisms which lead to the increasing of membrane permeability; and then the other compounds like antibiotics might enter into the cells and accomplish the antimicrobial activities more rapidly and efficiently.

The progress in sentiment analytics and communication research provides a powerful scaffold by which to reexamine the long-debated research on residents’ attitudes toward tourism. To mitigate the limitations of the classical survey-based research method, this study takes a news media sentiment analytics perspective to unveil how the residents’ attitudes toward tourism evolve over time and how socioeconomic factors interact with such evolving attitudes in the context of Hong Kong. Drawn on a news data set containing 72,755 news articles published in Chinese language newspapers, this study computes the overall news sentiments for 156 calendar months since 2003, examines the face validity and nomological validity of the results, and discusses the long-run dynamics between residents’ attitudes and typical socioeconomic factors. This study adds a vital dimension to current residents’ attitudes research and practices from data-scarce to data-rich studies and from static snapshots to dynamic unfolding.

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