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In pharmaceutical treatment, many non-cardiac drugs carry the risk of prolonging the QT interval, which can lead to fatal cardiac complications such as torsades de points (TdP). Although the unexpected blockade of ion channels has been widely considered to be one of the main reasons for affecting the repolarization phase of the cardiac action potential and leading to QT interval prolongation, the lack of knowledge regarding chemical structures in drugs that may induce the prolongation of the QT interval remains a barrier to further understanding the underlying mechanism and developing an effective prediction strategy. In this study, we thoroughly investigated the differences in chemical structures between QT-prolonging drugs and drugs with no drug-induced QT prolongation (DIQT) concerns, based on the Drug-Induced QT Prolongation Atlas (DIQTA) dataset. Three categories of structural alerts (SAs), namely amines, ethers, and aromatic compounds, appeared in large quantities in QT-prolonging drugs, but rarely in drugs with no DIQT concerns, indicating a close association between SAs and the risk of DIQT. Moreover, using the molecular descriptors associated with these three categories of SAs as features, the structure–activity relationship (SAR) model for predicting the high risk of inducing QT interval prolongation of marketed drugs achieved recall rates of 72.5% and 80.0% for the DIQTA dataset and the FDA Adverse Event Reporting System (FAERS) dataset, respectively. Our findings may promote a better understanding of the mechanism of DIQT and facilitate research on cardiac adverse drug reactions in drug development.

Drug-induced prolongation of the QT interval is common in a variety of pharmaceutical treatments and can lead to serious clinical outcomes. Although substantial efforts have been made to prevent drug-induced QT interval prolongation, the lack of a centralized data source remains the main obstacle to further study of the underlying mechanism and the development of effective prediction strategies. To fill this gap, we propose a schema for stratifying the risk of marketed QT prolonging drugs based on US Food and Drug Administration (FDA)-approved drug labeling and developed a Drug-Induced QT Prolongation Atlas (DIQTA). Potential application of DIQTA was shown by precision dosing in off-label use and therapeutic strategy optimization, as well as the facilitation of artificial intelligence (AI)-based modeling in predictive toxicity.

Teratogenicity is one of the main concerns in clinical medications of pregnant women. Prescription of antiseizure medications (ASMs) in women with epilepsy during pregnancy may cause teratogenic effects on the fetus. Although large scale epilepsy pregnancy registries played an important role in evaluating the teratogenic risk of ASMs, for most ASMs, especially the newly approved ones, the potential teratogenic risk cannot be effectively assessed due to the lack of evidence. In this study, the analyses are performed on any medication, with a focus on ASMs. We curated a list containing the drugs with potential teratogenicity based on the US Food and Drug Administration (FDA)-approved drug labeling, and established a support vector machine (SVM) model for detecting drugs with high teratogenic risk. The model was validated by using the post-marketing surveillance data from US FDA Spontaneous Adverse Events Reporting System (FAERS) and applied to the prediction of potential teratogenic risk of ASMs. Our results showed that our proposed model outperformed the state-of-art approaches, including logistic regression (LR), random forest (RF) and extreme gradient boosting (XGBoost), when detecting the high teratogenic risk of drugs (MCC and recall rate were 0.312 and 0.851, respectively). Among 196 drugs with teratogenic potential reported by FAERS, 136 (69.4%) drugs were correctly predicted. For the eight commonly used ASMs, 4 of them were predicted as high teratogenic risk drugs, including topiramate, phenobarbital, valproate and phenytoin (predicted probabilities of teratogenic risk were 0.69, 0.60 0.59, and 0.56, respectively), which were consistent with the statement in FDA-approved drug labeling and the high reported prevalence of teratogenicity in epilepsy pregnancy registries. In addition, the structural alerts in ASMs that related to the genotoxic carcinogenicity and mutagenicity, idiosyncratic adverse reaction, potential electrophilic agents and endocrine disruption were identified and discussed. Our findings can be a good complementary for the teratogenic risk assessment in drug development and facilitate the determination of pharmacological therapies during pregnancy.

Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.

Drug-induced rhabdomyolysis (DIR) is a rare and potentially life-threatening muscle injury that is characterized by low incidence and high risk. To our best knowledge, the performance of the current predictive models for the early detection of DIR is suboptimal because of the scarcity and dispersion of DIR cases. Therefore, on the basis of the curated drug information from the Drug-Induced Rhabdomyolysis Atlas (DIRA) database, we proposed a random forest (RF) model to predict the DIR severity of the marketed drugs. Compared with the state-of-art methods, our proposed model outperformed extreme gradient boosting, support vector machine, and logistic regression in distinguishing the Most-DIR concern drugs from the No-DIR concern drugs (Matthews correlation coefficient (MCC) and recall rate of our model were 0.46 and 0.81, respectively). Our model was subsequently applied to predicting the potentially serious DIR for 1402 drugs, which were reported to cause DIR by the postmarketing DIR surveillance data in the FDA Spontaneous Adverse Events Reporting System (FAERS). As a result, 62.7% (94) of drugs ranked in the top 150 drugs with the Most-DIR concerns in FAERS can be identified by our model. The top four drugs (odds ratio >30) including acepromazine, rapacuronium, oxyphenbutazone, and naringenin were correctly predicted by our model. In conclusion, the RF model can well predict the Most-DIR concern drug only based on the chemical structure information and can be a facilitated tool for early DIR detection.

Background : Stroke is one of the most important causes of epilepsy and we aimed to find if it is possible to predict patients with high risk of developing post-stroke epilepsy (PSE) at the time of discharge using machine learning methods. Methods : Patients with stroke were enrolled and followed at least one year. Machine learning methods including support vector machine (SVM), random forest (RF) and logistic regression (LR) were used to learn the data. Results : A total of 2730 patients with cerebral infarction and 844 patients with cerebral hemorrhage were enrolled and the risk of PSE was 2.8% after cerebral infarction and 7.8% after cerebral hemorrhage in one year. Machine learning methods showed good performance in predicting PSE. The area under the receiver operating characteristic curve (AUC) for SVM and RF in predicting PSE after cerebral infarction was close to 1 and it was 0.92 for LR. When predicting PSE after cerebral hemorrhage, the performance of SVM was best with AUC being close to 1, followed by RF ( AUC = 0.99) and LR (AUC = 0.85) . Conclusion : Machine learning methods could be used to predict patients with high risk of developing PSE, which will help to stratify patients with high risk and start treatment earlier. Nevertheless, more work is needed before the application of thus intelligent predictive model in clinical practice.

In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer. In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction. In addition, most of the conventional machine learning algorithms use a handcrafted gene set as features to construct models, which is generally selected by a statistical method with an arbitrary cut-off, e.g., p value < 0.05. The genes in the gene set are not necessarily related to the cancer and will make the model unreliable. Therefore, in our study, we thoroughly investigated the performance of different machine learning methods on stratifying breast cancer patients with a single type of genomic features. Then, we proposed a strategy, which can take into account the degree of correlation between genes and cancer patients, to identify the features from mRNAs and microRNAs, and evaluated the performance of the models with the new combined features of the multiscale genomic features. The results showed that, compared with the models constructed with a single type of features, the models with the multiscale genomic features generated by our proposed method achieved better performance on stratifying the ER status of breast cancer patients. Moreover, we found that the identified multiscale genomic features were closely related to the cancer by gene set enrichment analysis, indicating that our proposed strategy can well reflect the biological relevance of the genes to breast cancer. In conclusion, modelling with multiscale genomic features closely related to the cancer not only can guarantee the prediction performance of the models but also can effectively provide candidate genes for interpreting the mechanisms of cancer.

In this work, accelerated molecular dynamics (aMD) simulations were used to study different effects of G286F and R126 mutations on the activity of CCR5. Potential of Mean Force (PMF) results indicate that there are stable inactive-like states and active-like ones existing in the conformation space of the wild type (WT), confirming that CCR5 could possess to some extent constitutive activity. But the R126N mutation could constrain CCR5 in the inactive state through influencing the TXP motif and limiting the movements of TM5 and TM6. In contrast, the G286F mutation promotes the activity of the receptor by increasing the distance of TM2-TM6 and the flexibility of the intracellular part of TM5 and changing the H-bonding in the TXP motif. The observations from the cross correlation analysis further show that the R126N mutation dramatically reduces the motion correlations between TMs, which should partly contribute to the deactivation of CCR5. Compared with the WT system, TM6 and TM7 in the G286F mutant are loosely correlated with other regions, which should be conducive to drive the movement of TM6 and TM7 toward the active conformation. In addition, the result from the protein structure network (PSN) analysis reveals that the shortest pathways connecting the extracellular and the intracellular domains are highly conserved in the three systems despite the different mutations, in which the hydrogen bond plays a pivotal role. However, the G286F mutation shortens the lifetime of the pathway with respect to the R126N mutation, which may be associated with the different activities of the two mutants. The pathway connecting the ligand-binding site and the G-protein region reveals that the allosteric communication between TM6 and TM7 is enhanced by the R126N mutation while the G286F mutation induces the activation of the G-protein pocket by arousing more residues in the NPxxY region to participate in the pathway.

Pleomorphic adenoma (PA), Warthin's tumor (WT) and mucoepidermoid carcinoma (MEC) are three common subtypes of salivary gland tumors, for which the occurrence site is located in the parotid gland. Accurately diagnosing the subtypes of parotid tumors plays a vital role in the surgical treatment. Unfortunately, the current studies mainly focus on the binary classification of parotid tumors. The preoperative multi-classification of them has still been underexplored. For the purpose of broadening the application area of the predictive models and facilitating the clinical preoperative diagnosis, we suggested a multi-classification model, which was constructed by combining the variable combination population analysis (VCPA) algorithm with the partial least squares regression (PLSR), to simultaneously discriminate the three subtypes of parotid tumors as well as the normal parotid gland tissue based on the Raman spectra of the tissue samples. In addition, we investigated the impact of generating Raman spectra from different sampling locations on the reliability of the predictive models. For the validation set, the overall accuracy in predicting the subtypes of parotid tumors and the normal parotid gland tissue was 0.867. Similarly, the accuracies achieved by the models constructed with the Raman spectra from two different sampling locations were 0.877 and 0.883, respectively, indicating the minor influence of the sampling locations on the predictive models. Our findings can be helpful for establishing the method of rapidly diagnosing the salivary gland tumors preoperatively in clinics. Moreover, the characteristic wavenumbers used in model construction were highly associated with the variations of the structures and contents of nucleic acids, collagen, proteins, lipids and DNA/RNA in gland tissue, which revealed the mainly difference among three types of parotid tumors and can be conductive to better understanding the molecular mechanisms of them.

// Bo Li 1 , Zhi-Yu Gu 1 , Kai-Xiao Yan 1, 2 , Zhi-Ning Wen 3 , Zhi-He Zhao 1 , Long-Jiang Li 1, 2 and Yi Li 1, 2 1 State Key Laboratory of Oral Disease, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China 2 Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China 3 College of Chemistry, Sichuan University, Chengdu 610064, China Correspondence to: Long-Jiang Li, email: muzili63@163.com Yi Li, email: liyi1012@163.com Keywords: oral epithelial dysplasia, near-infrared Raman spectroscopy, support vector machine, classification system Received: November 10, 2016 Accepted: June 27, 2017 Published: July 18, 2017 ABSTRACT Until now, the classification system of oral epithelial dysplasia is still based on the architectural and cytological changes, which relies on the observation of pathologists and is relatively subjective. The purpose of present research was to discriminate the oral dysplasia by the near-infrared Raman spectroscope, in order to evaluate the classification system. We collected Raman spectra of normal mucosa, oral squamous cell carcinoma (OSCC) and dysplasia by near-infrared Raman spectroscope. The biochemical variations between different stages were analyzed by the characteristic peaks in the subtracted mean spectra. Gaussian radial basis function support vector machines (SVM) were used to establish the diagnostic models. At the same time, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to verify the results of SVM. Raman spectral differences were observed in the range between 730~1913 cm -1 . Compared with normal mucosa, high contents of protein and DNA in oral dysplasia and OSCC were observed. There were no significant or gradual variation of Raman peaks among different dysplastic grades. The accuracies of comparison between mild, moderate, severe dysplasia with OSCC were 100%, 44.44%, 71.15%, which elucidated the low modeling ability of support vector machines, especially for the moderate dysplasia. The analysis by PCA-LDA could not discriminate the stages, either. Combined with support vector machines, near-infrared Raman spectroscopy could detect the biochemical variations in oral normal, OSCC and dysplastic tissues, but could not establish diagnostic model accurately. The classification system needs further improvements.

Display Omitted Metabolic genes are as important prognostic biomarkers as oncogenes.We found that significant differences exist in metabolic processes of AML patients.We identified 62 metabolic genes that highly associated with the prognosis of AML.Genes ALAS2, BCAT1, BLVRB and HK3 were distinctly expressed in the AML patients. Dysregulated and reprogrammed metabolism are one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR)=1.58, p=0.038) and in two test sets (HR=1.69 and 1.56 and p=0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation.

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post‐modified non‐negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

Despite of the low occurrence rate in the entire genomes, de novo mutation is proved to be deleterious and will lead to severe genetic diseases via impacting on the gene function. Considering the fact that the traditional family based linkage approaches and the genome-wide association studies are unsuitable for identifying the de novo mutations, in recent years, several pipelines have been proposed to detect them based on the whole-genome or whole-exome sequencing data and were used for calling them in the rare diseases. However, how the performance of these variant calling pipelines on detecting the de novo mutations is still unexplored. For the purpose of facilitating the appropriate choice of the pipelines and reducing the false positive rate, in this study, we thoroughly evaluated the performance of the commonly used trio calling methods on the detection of the de novo single-nucleotide variants (DNSNVs) by conducting a comparative analysis for the calling results. Our results exhibited that different pipelines have a specific tendency to detect the DNSNVs in the genomic regions with different GC contents. Additionally, to refine the calling results for a single pipeline, our proposed filter achieved satisfied results, indicating that the read coverage at the mutation positions can be used as an effective index to identify the high-confidence DNSNVs. Our findings should be good support for the committees to choose an appropriate way to explore the de novo mutations for the rare diseases.

A facile yet efficient strategy was proposed by means of a combination of ultraviolet (UV) spectrophotometry with multivariate calibration methods, through which 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene in water samples could be simultaneously determined without any pre-separation process. The competitive adaptive reweighted sampling combined with successive projections algorithm (CARS–SPA) approach was used to diminish uninformative variables and select the important ones from spectral data measured. The multivariate calibration models were constructed by partial least squares (PLS-1) regression with high accuracy, in which the coefficients of determination of prediction ( R pred 2 ) were 0.9935, 0.9969, and 0.9971 and the root mean square error of prediction (RMSEP) were 0.7850, 0.5411 and 0.5414 for 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene, respectively. The optimized model was successfully applied to simultaneously determine the content of the three studied analytes in several real water samples with good recovery close to 100%. Finally, the elliptical joint confidence region (EJCR) tests further confirm that the proposed method has no proportional and constant error in the predicted concentrations, providing a statistic support for the accuracy of the model. These results indicate that it is promise for UV spectroscopy coupled to the multivariate calibration technique to establish a simple, quick, accurate and reliable analysis method for simultaneous determination of some nitroaromatic compounds in real environments. Also, the strategy proposed by the work will advance the analytical methods used in the other complicated sample systems.

We report a facile yet effective strategy of utilizing a combination of Fourier transform-infrared spectroscopy (FTIR) and multi-label algorithms, through which multi-components in polymer bonded explosives (PBXs) could be rapidly and simultaneously identified with high accuracy. The explosive components include 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclo-octane (HMX), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), 2,4,6-triamino-1,3,5-trinitrobenzene (TATB) and 2,4,6-trinitrotoluene (TNT) involved in single-component, binary-component and ternary-component PBXs. The train set contains 354 FTIR spectra of the explosives while the independent test set contains 84. Two multi-label strategies (viz., data decomposition and algorithm adaptation) were adopted to construct the classification model with an objective of testing their efficiency in the multi-classification application. Principal component analysis (PCA) was applied to reduce the variables. Both the two algorithms exhibit excellent performance with 100% accuracy for the training and the independent test sets. However, for real PBX samples, the performance of the algorithm adaptation strategy is sharply decreased to 40% accuracy. But, it is noteworthy that the data decomposition strategy still achieves the accuracy of 100% for the real samples, exhibiting stronger robustness for the background interference and high promise in practice. The strategy proposed by the work would provide valuable information for advancing analytical methods in the explosive detection system and the other complicated samples.

Drug-induced rhabdomyolysis (DIR) is an idiosyncratic, rare and fatal adverse drug reaction (ADR) characterized in severe muscle injuries accompanied by multiple-organ failure. Limited knowledge and evidence regarding pathophysiology and natural history of rhabdomyolysis are the big obstacles to developing early biomarkers and prevention strategies. Given that lack of centralized data resource to curate, organize, and standardize widespread DIR information, we present a Drug-induced Rhabdomyolysis Atlas (DIRA) to fulfill the gap. DIRA provides three folds of DIR related information including (1) a classification scheme for drugs’ potential to cause rhabdomyolysis based on drug labeling information; (2) post-marketing surveillance data of DIR; and (3) DIR drug property information. To elucidate the utility of DIRA, we employed precision dosing, concomitant use of DIR drugs, and predictive modeling development to exemplify strategies for idiosyncratic adverse drug reaction (IADR) management.

Background Endometrial cancers (ECs) are one of the most common types of malignant tumor in females. Substantial efforts had been made to identify significantly mutated genes (SMGs) in ECs and use them as biomarkers for the classification of histological subtypes and the prediction of clinical outcomes. However, the impact of non-significantly mutated genes (non-SMGs), which may also play important roles in the prognosis of EC patients, has not been extensively studied. Therefore, it is essential for the discovery of biomarkers in ECs to further investigate the non-SMGs that were highly associated with clinical outcomes. Results For the 9681 non-SMGs reported by the mutation annotation pipeline, there were 1053, 1273 and 395 non-SMGs differentially expressed between the patient groups divided by the clinical endpoints of histological grade, histological type as well as the International Federation of Gynecology and Obstetrics (FIGO) stage of ECs, respectively. In the gene set enrichment analysis, the cancer-related pathways, namely neuroactive ligand-receptor interaction signaling pathway, cAMP signaling pathway and calcium signaling pathway, were significantly enriched with the differentially expressed non-SMGs for all the three endpoints. We further identified 23, 19 and 24 non-SMGs, which were highly associated with histological grade, histological type and FIGO stage, respectively, from the differentially expressed non-SMGs by using the variable combination population analysis (VCPA) approach and found that 69.6% (16/23), 78.9% (15/19) and 66.7% (16/24) of the identified non-SMGs had been previously reported to be correlated with cancers. In addition, the averaged areas under the receiver operating characteristic curve (AUCs) achieved by the predictive models with identified non-SMGs as predictors in predicting histological type, histological grade, and FIGO stage were 0.993, 0.961 and 0.832, respectively, which were superior to those achieved by the models with SMGs as features (averaged AUCs = 0.928, 0.864 and 0.535, resp.). Conclusions Besides the SMGs, the non-SMGs reported in the mutation annotation analysis may also involve the crucial genes that were highly associated with clinical outcomes. Combining the mutation status with the gene expression profiles can efficiently identify the cancer-related non-SMGs as predictors for cancer prognostic prediction and provide more supplemental candidates for the discovery of biomarkers. Electronic supplementary material The online version of this article (10.1186/s12859-017-1891-6) contains supplementary material, which is available to authorized users.

Aims: Although RNA-sequencing has been widely used to identify the differentially expressed genes (DEGs) as biomarkers to guide the therapeutic treatment, it is necessary to investigate the concordance of DEGs identified by microarray and RNA-sequencing for the clinical prognosis. Material & methods: By using The Cancer Genome Atlas data sets, we thoroughly investigated the concordance of DEGs identified from microarray and RNA-sequencing data and their molecular functions. Results: The DEGs identified by both technologies averaged ˜98.6% overlap. The cancer-related gene sets were significantly enriched with the DEGs and consistent between two technologies. Conclusions: The highly consistency of DEGs in their regulation directionality and molecular functions indicated the good reproducibility between microarray and RNA-sequencing in identifying potential oncogenes for clinical prognosis.

Explosive determination is of great importance in national defense and security fields. Simple, quick and reliable analytical techniques have been highly demanded in the field. In this work, we proposed a novel method for simultaneous determination of pentaerythritol tetranitrate (PETN), 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), and 2,4,6-trinaitrotolunene (TNT) in binary-component polymer bonded explosive (PBX) samples by means of a combination of ultraviolet (UV) spectrophotometry with multivariable calibration methods. An orthogonal array design (DAD) was employed to construct the calibration set, which contains 27 reference samples. The calibration models were constructed using Partial Least Square regression (PLS-1) and Multiple Linear Regression (MLR). The variables were selected by the Successive Projection Algorithm (SPA) in MLR model. The predictive ability of the optimized models was validated by a test set including 18 samples. Finally, the two optimized models were successfully applied to simultaneously determine the content of PETN, RDX and TNT in five real binary-component PBX samples. Satisfactory results were obtained for the two models, in which the recovery yields were close to 100% for all the analytes. The computed elliptical joint confidence region (EJCR) further shows that the two models have no proportional and constant errors in the predicted concentrations. In addition, the statistical analysis indicates that MLR model with reasonable variable selection (SPA-MLR) could exhibit a slight superiority toward PLS-1 in the system. In a word, UV-spectroscopy in combination with multivariable calibration techniques has high potential to be a simple, quick and accurate analysis method for explosive determination in practice.

In clinical research, DNA microarrays are widely applied in the identification of the oncogenes, which are differentially expressed between two clinical states and considered as predictors for the cancer prognosis. Due to the heterogeneity of clinical samples, the differentially expressed genes (DEGs) discovered by current statistical methods or machine learning algorithms involve a number of genes unrelated to the phenotypic differences between the compared samples and, consequently, will impact on the reliability of the predictive models in the cancer prognosis. In our study, we proposed Bayesian nonparametric variable selection algorithm, a stochastic random and hierarchical search method, to separate out the cancer-related genes from the DEG lists. The importance of the genes in the DEG lists can be inferred from the posterior distribution of the predicted clinical endpoints, which can be simulated by the Markov Chain Monte Carlo (MCMC) algorithm. The cancer-related genes were identified according to their importance and used to construct models for the prediction of three clinical endpoints, namely the estrogen receptor status (ER status) of the breast cancer patient, the preoperative treatment response of breast cancer and the overall survival milestone outcome of acute myeloma leukemia (OS of AML). The prediction accuracies of preoperative treatment response, ER status and OS of AML were 86%, 89% and 58%, and the Mathew’s correlation coefficients were 0.42, 0.77 and 0.33, which were higher than those reported in previous studies. Furthermore, most of the genes identified by our method were reported as oncogenes in previous literatures. Our results demonstrated that the Bayesian nonparametric variable selection algorithm proposed in current study can efficiently identify the oncogenes for cancer prognosis and enhance the performance of the predictive models.