GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

Prediction of trabecular bone architectural features by deep learning models using simulated DXA images

Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that the DL models exhibited the capability of predicting the microstructural features using DXA images. This study also showed that the number and resolution of input simulated DXA images had considerable impacts on the prediction accuracy of the DL models. These findings support the hypothesis of this study and indicate a high potential of using DXA images in prediction of osteoporotic bone fracture risk

Impacts of COVID-19 Pandemic on Dietary Consumption among Chinese Residents: Evidence from Provincial-Level Panel Data

The COVID-19 pandemic has profoundly affected people’s daily lives, including their dietary behaviors. Using a panel data set of 31 provinces from 2015 to 2020, this study employed two-way fixed effects (FE) models to examine the impacts of the COVID-19 pandemic on dietary consumption among Chinese residents. The results showed that the COVID-19 pandemic positively affected residents’ consumption of grain, eggs, dairy, and white meat (poultry and aquatic products), while it had a negative effect on individuals’ red meat consumption in both urban and rural areas. These results were robust to different measures of the COVID-19 pandemic, including the number of confirmed cases, suspect cases, and dead cases. Comparatively, the changes in food consumption induced by the COVID-19 pandemic were more prominent for Chinese residents who lived in rural areas than urban areas. In addition, compared to their counterparts, the dietary consequences of the pandemic were more pronounced for residents living in the eastern region and regions with a high old-age dependency ratio and low illiteracy rate. Furthermore, the estimation results of the quantile regression model for panel data suggested that the COVID-19 pandemic had relatively larger impacts on the dietary consumption of Chinese residents at lower quantiles of food consumption compared with those at higher quantiles. Overall, the results of this study suggested that Chinese residents had a healthier diet after the outbreak of the COVID-19 pandemic. We discussed possible mechanisms, including health awareness, income, food supply and prices, and other behavioral changes during COVID-19 (e.g., physical activity and cooking). To further improve residents’ dietary behaviors and health, our study proposed relevant measures, such as increasing residents’ dietary knowledge, ensuring employment and income, and strengthening the food supply chain resilience during the pandemic

An Advanced Orthotopic Ovarian Cancer Model in Mice for Therapeutic Trials

A nude mouse received subcutaneous injection of human ovarian cancer cells HO-8910PM to form a tumor, and then the tumor fragment was surgically transplanted to the ovary of a recipient mouse to establish an orthotopic cancer model. Tumors occurred in 100% of animals. A mouse displayed an ovarian mass, ascites, intraperitoneal spread, and lung metastasis at natural death. The mean survival time was 34.1±17.2 days, with median survival time of 28.5 days. The findings indicated that the present mouse model can reflect the biological behavior of advanced human ovarian cancers. This in vivo model can be used to explore therapeutic means against chemoresistance and metastasis, and an effective treatment would prolong the survival time

Pharmacokinetic profiles of cancer sonochemotherapy

<p><b>Introduction:</b> Sonochemotherapy is a promising strategy for the treatment of cancer, however, there is limited understanding of its pharmacokinetics (PK).</p> <p><b>Area covered:</b> The PK profile of sonochemotherapy is evaluated based on released data. Preclinical investigations suggest that the blood PK of sonochemotherapy is similar to chemotherapy when using free anticancer drugs. When using encapsulated drugs, a lower plasma level usually occurs; however, the ultrasonic release of drugs within a tumor may lead to drugs leaking into circulation, causing a rebound in the plasma drug level; a higher drug level is detected in certain healthy organs, however this depends mostly on the pharmaceutical formulation. Sonochemotherapy increases both the level and retention time of drugs in a tumor. Clinical trials of combined chemotherapy and high intensity focused ultrasound (HIFU) are evaluated from the perspective of preclinical PK: the intratumoral PK and drug interactions under insonation, and a protocol to set the interval between drug administration and insonation are lacking.</p> <p><b>Expert opinion:</b> Insonation can alter the PK properties of chemotherapeutics, which may exacerbate the system and/or organ toxicity of anticancer drugs. Directly employing the PK parameters validated in conventional chemotherapy plays an important role in unsatisfactory clinical outcomes of chemotherapy combined with HIFU.</p

Characterization of the complete mitochondrial genome of blacktip shark Carcharhinus limbatus (Carcharhiniformes: Carcharhinidae)

In this study, we aimed to determine the complete mitochondrial genome of blacktip shark Carcharhinus limbatus. The mitochondrial genome was 16,705 bp in length, including 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a control region. Phylogenetic analysis was done using the Bayesian inference method, which showed a close relationship between C. limbatus and C. amblyrhynchoides

Fast Calculation Method for Transient Response of Transmission Line Based on Chebyshev Pseudospectral–Two-Step Three-Order Boundary Value Coupled Method

A Chebyshev pseudospectral&#8722;two-step three-order boundary value coupled method is proposed and presented for handling the issue associated with complicated calculation, low precision, and poor stability in the process of transient response of transmission line. The first order differential equation in time domain is obtained via dispersing the telegraph equation in space domain by utilizing the pseudospectral method (PSM) based on Chebyshev polynomial. Then the two-step three-order boundary value method (BVM3) is presented and employed to resolve the obtained differential equation, so the numerical solution of the space discrete points can be obtained. Furthermore, the Chebyshev pseudospectral&#8722;two-step three-order boundary value coupled method (PSM-BVM3) is presented and compared with the Chebyshev pseudospectral&#8722;two-step two order boundary value coupled method (PSM-BVM2), the pseudospectral&#8722;differential quadrature method (PSM-DQM), and the pseudospectral method&#8722;trapezoid rule (PSM-TR) to validate the feasibility of the new proposed method. Theoretical analysis and numerical simulation reveal that the proposed Chebyshev PSM-BVM3 has a higher performance than the conventional method. For the proposed Chebyshev PSM-BVM3, the higher precision, efficiency, and numerical stability can be obtained and achieved only with fewer discrete points in the space domain, which is suitable for solving the transient response of transmission line. The proposed PSM-BVM3 can improve the drawback of numerical instability of the PSM and can also improve the disadvantage of the BVM as it is not easy to change the latter&#8217;s timestep size