No abstract

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. .

Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons as well as visual cortex, but lacks input from entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target perirhinal cortex, Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

BackgroundA living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery.ResultsWe have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation.ConclusionsContemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.

ObjectivesA better dosing strategy can improve clinical outcomes for patients. We sought to compare the extended or continuous infusion with conventional intermittent infusion of piperacillin/tazobactam, investigating which approach is better and worthy of recommendation for clinical use.MethodsArticles were gathered from PubMed, Web of Science, ProQuest, Science Direct, Cochrane, two Chinese literature databases (CNKI, Wan Fang Data) and related ICAAC and ACCP conferences. Randomized controlled and observational studies that compared extended or continuous infusion with conventional intermittent infusion of piperacillin/tazobactam were identified from the databases above and analyzed. Two reviewers independently extracted and investigated the data. A meta-analysis was performed using Revman 5.2 software. The quality of each study was assessed. Sensitivity analysis and publication bias were evaluated.ResultsFive randomized controlled trials and nine observational studies were included in this study. All included studies had high quality and no publication bias was found. Compared to the conventional intermittent infusion approach, the extended or continuous infusion group had a significantly higher clinical cure rate (OR 1.88, 95% CI 1.29-2.73, P = 0.0009) and a lower mortality rate (OR 0.67, 95% CI 0.50-0.89, P = 0.005). No statistical difference was observed for bacteriologic cure (OR 1.40, 95% CI 0.82-2.37, P = 0.22) between the two dosing regimens. The sensitivity analysis showed the results were stable.ConclusionsOur systematic review and meta-analysis suggested that the extended or continuous infusion strategy of piperacillin/tazobactam should be recommended for clinical use considering its higher clinical cure rate and lower mortality rate in comparison with conventional intermittent strategy. Data from this study could be extrapolated for other β-lactam antimicrobials. Therefore, this dosing strategy could be considered in clinical practice.