Shifting from Population-wide to Personalized Cancer Prognosis with Microarrays

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment

a deep learning model to recognize food contaminating beetle species based on elytra fragments

Abstract Insect pests are often associated with food contamination and public health risks. Accurate and timely species-specific identification of pests is a key step to scale impacts, trace back the contamination process and promptly set intervention measures, which usually have serious economic impact. The current procedure involves visual inspection by human analysts of pest fragments recovered from food samples, a time-consuming and error-prone process. Deep Learning models have been widely applied for image recognition, outperforming other machine learning algorithms; however only few studies have applied deep learning for food contamination detection. In this paper, we describe our solution for automatic identification of 15 storage product beetle species frequently detected in food inspection. Our approach is based on a convolutional neural network trained on a dataset of 6900 microscopic images of elytra fragments, obtaining an overall accuracy of 83.8% in cross validation. Notably, the classification performance is obtained without the need of designing and selecting domain specific image features, thus demonstrating the promising prospects of Deep Learning models in detecting food contamination

Employing “FDAlabel” Database to Extract Pharmacogenomics Information from FDA Drug Labeling to Advance the Study of Precision Medicine

Pharmacogenomics (PGx) focuses on how genomics and genetic variants (inherited and acquired) affect drug response. A better understanding of the association between genetic markers and individual phenotypes may improve therapy by enhancing drug efficacy, safety, and advance precision medicine. The FDALabel database (https://rm2.scinet.fda.gov/druglabel/#simsearch-0) was developed from the FDA\u27s Structured Product Labeling (SPL) repository to allow users to perform full-text and customizable searches of the labeling section {e.g. Boxed Warning, Warning and Precautions, Adverse Reaction (AR) sections}. In this study, 48 known biomarkers were used to query PGx relevant contents from the FDALabel database, including Indication, Clinical Pharmacology, Clinical Studies, and Use in Specific Populations. As a result, we identified 162 drugs out of 1129 small molecule drugs with PGx biomarker information. Furthermore, statistical analysis, pattern recognition, and network visualization were applied to investigate association of drug efficacy and severe ARs with PGx biomarkers and subpopulation. The results indicated that these drugs have a higher association with certain ARs in specific patient subpopulations (e.g., a higher association between CYP2D6 poor metabolizers and ARs caused by drugs for the treatment of psychiatric disoders ), and cover a broad range of therapeutic classes (e.g., Psychiatry, Cardiology, Oncology, and Endocrinology). FDALabel database (free publicly available) provides a convenient tool to navigate and extract PGx information from FDA-approved drug. The knowledge gained from these drugs and biomarkers in this study will enhance the understanding of PGx to advance precision medicine

Enhanced photodegradation activity of methyl orange over Z-scheme type MoO3-g-C3N4 composite under visible light irradiation

National Natural Science Foundation of China [21003109, 51108424]; Opening-foundation of State Key Laboratory Physical Chemistry and Solid Surfaces, Xiamen University, China [201311]; School of Energy Resources at University of WyomingNovel Z-scheme type MoO3-g-C3N4 composites photocatalysts were prepared with a simple mixing-calcination method, and evaluated for their photodegradation activities of methyl orange (MO). The optimized MoO3-g-C3N4 photocatalyst shows a good activity with a kinetic constant of 0.0177 min(-1), 10.4 times higher than that of g-C3N4. Controlling various factors (MoO3-g-C3N4 amount, initial MO concentration, and pH value of MO solution) can lead to the enhancement of the photocatalytic activity of the composite. Only MoO3 and g-C3N4 are detected with X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) spectra. N-2 adsorption and UV-vis diffuse reflectance spectroscopy (DRS) results suggest that the addition of MoO3 slightly affects the specific surface area and the photoabsorption performance. The transmission electron microscopy (TEM) image of MoO3-g-C3N4 indicates a close contact between MoO3 and g-C3N4, which is beneficial to interparticle electron transfer. The high photocatalytic activity of MoO3-g-C3N4 is mainly attributed to the synergetic effect of MoO3 and g-C3N4 in electron-hole pair separation via the charge migration between the two semiconductors. The charge transfer follows direct Z-scheme mechanism, which is proven by the reactive species trapping experiment and the (OH)-O-center dot-trapping photoluminescence spectra

Does Applicability Domain Exist in Microarray-Based Genomic Research?

Constructing an accurate predictive model for clinical decision-making on the basis of a relatively small number of tumor samples with high-dimensional microarray data remains a very challenging problem. The validity of such models has been seriously questioned due to their failure in clinical validation using independent samples. Besides the statistical issues such as selection bias, some studies further implied the probable reason was improper sample selection that did not resemble the genomic space defined by the training population. Assuming that predictions would be more reliable for interpolation than extrapolation, we set to investigate the impact of applicability domain (AD) on model performance in microarray-based genomic research by evaluating and comparing model performance for samples with different extrapolation degrees. We found that the issue of applicability domain may not exist in microarray-based genomic research for clinical applications. Therefore, it is not practicable to improve model validity based on applicability domain

Enhanced QSAR Model Performance by Integrating Structural and Gene Expression Information

Despite decades of intensive research and a number of demonstrable successes, quantitative structure-activity relationship (QSAR) models still fail to yield predictions with reasonable accuracy in some circumstances, especially when the QSAR paradox occurs. In this study, to avoid the QSAR paradox, we proposed a novel integrated approach to improve the model performance through using both structural and biological information from compounds. As a proof-of-concept, the integrated models were built on a toxicological dataset to predict non-genotoxic carcinogenicity of compounds, using not only the conventional molecular descriptors but also expression profiles of significant genes selected from microarray data. For test set data, our results demonstrated that the prediction accuracy of QSAR model was dramatically increased from 0.57 to 0.67 with incorporation of expression data of just one selected signature gene. Our successful integration of biological information into classic QSAR model provided a new insight and methodology for building predictive models especially when QSAR paradox occurred

A Network Pharmacology Approach to Evaluating the Efficacy of Chinese Medicine Using Genome-Wide Transcriptional Expression Data

The research of multicomponent drugs, such as in Chinese Medicine, on both mechanism dissection and drug discovery is challenging, especially the approaches to systematically evaluating the efficacy at a molecular level. Here, we presented a network pharmacology-based approach to evaluating the efficacy of multicomponent drugs by genome-wide transcriptional expression data and applied it to Shenmai injection (SHENMAI), a widely used Chinese Medicine composed of red ginseng (RG) and Radix Ophiopogonis (RO) in clinically treating myocardial ischemia (MI) diseases. The disease network, MI network in this case, was constructed by combining the protein-protein interactions (PPI) involved in the MI enriched pathways. The therapeutic efficacy of SHENMAI, RG, and RO was therefore evaluated by a network parameter, namely, network recovery index (NRI), which quantitatively evaluates the overall recovery rate in MI network. The NRI of SHENMAI, RG, and RO were 0.876, 0.494, and 0.269 respectively, which indicated SHENMAI exerts protective effects and the synergistic effect of RG and RO on treating myocardial ischemia disease. The successful application of SHENMAI implied that the proposed network pharmacology-based approach could help researchers to better evaluate a multicomponent drug on a systematic and molecular level