SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions about treatment strategies or the use of palliative care. (2) Methods: Gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected a total of 1187 RNA-seq data points from breast cancer patients (median age 58 years) in Fragments Per Kilobase Million (FPKM) format from the TCGA database. Among them, we selected 144 patients with date of death information to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to TPM to build the survival prediction model SaBrcada. After normalization and dimension raising, we used the differential gene expression data to test eight different deep learning architectures. Considering the effect of age on prognosis, we also performed a stratified random sampling test on all ages between the lower and upper quartiles of patient age, 48 and 69 years; (3) Results: Stratifying by age 61, the performance of SaBrcada built by GoogLeNet was improved to a highest accuracy of 0.798. We also built a free website tool to provide five predicted survival periods: within six months, six months to one year, one to three years, three to five years, or over five years, for clinician reference. (4) Conclusions: We built the prediction model, SaBrcada, and the website tool of the same name for breast cancer survival analysis. Through these models and tools, clinicians will be provided with survival interval information as a basis for formulating precision medicine.

Using Machine Learning Approaches to Predict Target Gene Expression in Rice T-DNA Insertional Mutants.

To change the expression of the flanking genes by inserting T-DNA into the genome is commonly used in rice functional gene research. However, whether the expression of a gene of interest is enhanced must be validated experimentally. Consequently, to improve the efficiency of screening activated genes, we established a model to predict gene expression in T-DNA mutants through machine learning methods. We gathered experimental datasets consisting of gene expression data in T-DNA mutants and captured the PROMOTER and MIDDLE sequences for encoding. In first-layer models, support vector machine (SVM) models were constructed with nine features consisting of information about biological function and local and global sequences. Feature encoding based on the PROMOTER sequence was weighted by logistic regression. The second-layer models integrated 16 first-layer models with minimum redundancy maximum relevance (mRMR) feature selection and the LADTree algorithm, which were selected from nine feature selection methods and 65 classified methods, respectively. The accuracy of the final two-layer machine learning model, referred to as TIMgo, was 99.3% based on fivefold cross-validation, and 85.6% based on independent testing. We discovered that the information within the local sequence had a greater contribution than the global sequence with respect to classification. TIMgo had a good predictive ability for target genes within 20 kb from the 35S enhancer. Based on the analysis of significant sequences, the G-box regulatory sequence may also play an important role in the activation mechanism of the 35S enhancer.

dBMHCC: A comprehensive hepatocellular carcinoma (HCC) biomarker database provides a reliable prediction system for novel HCC phosphorylated biomarkers.

Hepatocellular carcinoma (HCC), which is associated with an absence of obvious symptoms and poor prognosis, is the second leading cause of cancer death worldwide. Genome-wide molecular biology studies should provide biological insights into HCC development. Based on the importance of phosphorylation for signal transduction, several protein kinase inhibitors have been developed that improve the survival of cancer patients. However, a comprehensive database of HCC-related phosphorylated biomarkers (HCCPMs) and novel HCCPMs prediction platform has been lacking. We have thus constructed the dBMHCC databases to provide expression profiles, phosphorylation and drug information, and evidence type; gathered information on HCC-related pathways and their involved genes as candidate HCC biomarkers; and established a system for evaluating protein phosphorylation and HCC-related biomarkers to improve the reliability of biomarker prediction. The resulting dBMHCC contains 611 notable HCC-related genes, 234 HCC-related pathways, 17 phosphorylation-related motifs and their 255 corresponding protein kinases, 5955 HCC biomarkers, and 1077 predicted HCCPMs. Methionine adenosyltransferase 2B (MAT2B) and acireductone dioxygenase 1 (ADI1), which regulate HCC development and hepatitis C virus infection, respectively, were among the top 10 HCCPMs predicted by dBMHCC. Platelet-derived growth factor receptor alpha (PDGFRA), which had the highest evaluation score, was identified as the target of one HCC drug (Regorafenib), five cancer drugs, and four non-cancer drugs. dBMHCC is an open resource for HCC phosphorylated biomarkers, which supports researchers investigating the development of HCC and designing novel diagnosis methods and drug treatments. Database URL: http://predictor.nchu.edu.tw/dBMHCC.

QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding.

Many proteins exist in natures as oligomers with various quaternary structural attributes rather than as single chains. Predicting these attributes is an essential task in computational biology for the advancement of proteomics. However, the existing methods do not consider the integration of heterogeneous coding and the accuracy of subunit categories with limited data. To this end, we proposed a tool that can predict more than 12 subunit protein oligomers, QUATgo. Meanwhile, three kinds of sequence coding were used, including dipeptide composition, which was used for the first time to predict protein quaternary structural attributes, and protein half-life characteristics, and we modified the coding method of the functional domain composition proposed by predecessors to solve the problem of large feature vectors. QUATgo solves the problem of insufficient data for a single subunit using a two-stage architecture and uses 10-fold cross-validation to test the predictive accuracy of the classifier. QUATgo has 49.0% cross-validation accuracy and 31.1% independent test accuracy. In the case study, the accuracy of QUATgo can reach 61.5% for predicting the quaternary structure of influenza virus hemagglutinin proteins. Finally, QUATgo is freely accessible to the public as a web server via the site http://predictor.nchu.edu.tw/QUATgo.

Cancer risk in patients aged 30 years and above with type 2 diabetes receiving antidiabetic monotherapy: a cohort study using metformin as the comparator.

INTRODUCTION: Accumulating evidence suggests that metformin reduces incident cancer development. Few cohort studies have evaluated the risk of subsequent cancer development in diabetic cohorts receiving antidiabetic monotherapy. We conducted a population-based study in patients with new-onset type 2 diabetes treated with antidiabetic monotherapy. METHODS: We identified a cohort of patients with type 2 diabetics aged ≥30 years receiving hypoglycemic monotherapy (n=7,325) from the 1998-2007 Longitudinal Health Insurance Dataset. Patients were grouped according to the antidiabetic therapy they received into metformin (n=2,223), sulfonylurea (n=3,965), glitazone (n=53), meglitinide (n=128), acarbose (n=150), and insulin (n=806) groups. Patients with preexisting cancer were excluded. All patients were followed up until cancer development, dropout, death, or until December 31, 2008. Cox's model was used to estimate multivariable hazard ratios (HRs) adjusted for age, sex, Charlson comorbidity index, smoking-related comorbidities, alcohol use disorders, morbid obesity, pancreatitis, hypertension, monthly income, and urbanization level. The log-rank test was used to compare cumulative cancer incidence. Two-sided P-values <0.05 were required to reject the null hypothesis. RESULTS: The overall median follow-up duration was 2.5 years (interquartile range, 3.6 years). Totally, 367 and 124 cancers developed in the sulfonylurea and metformin groups, respectively, representing an adjusted HR of 1.36 (95% confidence interval [CI], 1.11-1.67; P<0.005). No significant differences were observed between other groups. Increased adjusted HRs were observed for colorectal cancer (adjusted HR, 1.94; 95% CI, 1.15-3.27; P<0.05) and lung cancer (adjusted HR, 1.76; 95% CI, 1.00-3.07; P<0.05). CONCLUSION: Metformin monotherapy may be associated with a reduction in the risk for cancer development compared with sulfonylurea monotherapy. Moreover, the use of an average defined daily dose of >0.25 of metformin when compared to lower dose will contribute to a reduction of 80% risk.

Metal-organic frameworks: new matrices for surface-assisted laser desorption-ionization mass spectrometry.

The cage-type MIL-100(Fe) metal-organic frameworks (MOFs) were used as matrices for surface assisted laser desorption-ionization mass spectrometry. The unique 3D cage frameworks and the iron-center feature good reproducibility of MS intensity and a high signal-to-noise ratio compared to organic or other nanoparticle matrices.