Allostery tweaks innumerable biological processes and plays a fundamental role in human disease and drug discovery. Exploration of allostery has thus been regarded as a crucial requirement for research on biological mechanisms and the development of novel therapeutics. Here, based on our previously developed allosteric data and methods, we present an interactive platform called AlloFinder that identifies potential endogenous or exogenous allosteric modulators and their involvement in human allosterome. AlloFinder automatically amalgamates allosteric site identification, allosteric screening and allosteric scoring evaluation of modulator–protein complexes to identify allosteric modulators, followed by allosterome mapping analyses of predicted allosteric sites and modulators in human proteome. This web server exhibits prominent performance in the reemergence of allosteric metabolites and exogenous allosteric modulators in known allosteric proteins. Specifically, AlloFinder enables identification of allosteric metabolites for metabolic enzymes and screening of potential allosteric compounds for disease-related targets. Significantly, the feasibility of AlloFinder to discover allosteric modulators was tested in a real case of signal transduction and activation of transcription 3 (STAT3) and validated by mutagenesis and functional experiments. Collectively, AlloFinder is expected to contribute to exploration of the mechanisms of allosteric regulation between metabolites and metabolic enzymes, and to accelerate allosteric drug discovery. The AlloFinder web server is freely available to all users at http://mdl.shsmu.edu.cn/ALF/.

Addressing the high false-positive rate of conventional low-dose computed tomography (LDCT) for lung cancer diagnosis, the efficacy of incorporating blood-based noninvasive testing for assisting practicing clinician's decision making in diagnosis of pulmonary nodules (PNs) is investigated. In this prospective observative study, next generation sequencing-(NGS-) based cell-free DNA (cfDNA) mutation profiling, NGS-based cfDNA methylation profiling, and blood-based protein cancer biomarker testing are performed for patients with PNs, who are diagnosed as high-risk patients through LDCT and subsequently undergo surgical resections, with tissue sections pathologically examined and classified. Using pathological classification as the gold standard, statistical and machine learning methods are used to select molecular markers associated with tissue's malignant classification based on a 98-patient discovery cohort (28 benign and 70 malignant), and to construct an integrative multianalytical model for tissue malignancy prediction. Predictive models based on individual testing platforms have shown varying levels of performance, while their final integrative model produces an area under the receiver operating characteristic curve (AUC) of 0.85. The model's performance is further confirmed on a 29-patient independent validation cohort (14 benign and 15 malignant, with power > 0.90), reproducing AUC of 0.86, which translates to an overall sensitivity of 80% and specificity of 85.7%.

At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.

Background: DNA-based next-generation sequencing has been widely used in the selection of target therapies for patients with nonsmall cell lung cancer (NSCLC).RNA-based next-generation sequencing has been proven to be valuable in detecting fusion and exon-skipping mutations and is recommended by National Comprehensive Cancer Network guidelines for these mutation types. Methods:The authors developed an RNA-based hybridization panel targeting actionable driver oncogenes in solid tumors. Experimental and bioinformatics pipelines were optimized for the detection of fusions, single-nucleotide variants (SNVs), and insertion/deletion (indels). In total, 1253 formalin-fixed, paraffinembedded samples from patients with NSCLC were analyzed by DNA and RNA panel sequencing in parallel to assess the performance of the RNA panel in detecting multiple types of mutations. Results:In analytical validation, the RNA panel achieved a limit of detection of 1.45-3.15 copies per nanogram for SNVs and 0.21-6.48 copies per nanogram for fusions. In 1253 formalin-fixed, paraffin-embedded NSCLC samples, the RNA panel identified a total of 124 fusion events and 26 MET exon 14-skipping events, in which 14 fusions and six MET exon 14-skipping mutations were missed by DNA panel sequencing. By using the DNA panel as the reference, the positive percent agreement and the positive predictive value of the RNA panel were 98.08% and 98.62%, respectively, for detecting targetable SNVs and 98.15% and 99.38%, respectively, for detecting targetable indels. Conclusions: Parallel DNA and RNA sequencing analyses demonstrated the accuracy and robustness of the RNA sequencing panel in detecting multiple types of clinically actionable mutations. The simplified experimental workflow and low sample consumption will make RNA panel sequencing a potentially effective method in clinical testing.See related editorial on pages 2294-6, this issue.

Currently, DNA and RNA are used separately to capture different types of gene mutations. DNA is commonly used for the detection of SNVs, indels and CNVs; RNA is used for analysis of gene fusion and gene expression. To perform both DNA sequencing (DNA-seq) and RNA-seq, material is divided into two copies, and two different proce-dures are required for sequencing. Due to overconsumption of samples and experimental process complexity, it is necessary to create an experimental method capable of analyzing SNVs, indels, fusions and expression. We developed an RNA-based hybridization capture panel targeting actionable driver oncogenes in solid tumors and corresponding sample preparation and bioinformatics workflows. Analytical validation with an RNA standard reference containing 16 known fusion mutations and 6 SNV mutations demonstrated a detection specificity of 100.0% [95% CI 88.7%~100.0%] for SNVs and 100.0% [95% CI 95.4%~100.0%] for fusions. The targeted RNA panel achieved a 0.73-2.63 copies/ng RNA lower limit of detection (LOD) for SNVs and 0.21-6.48 copies/ng RNA for fusions. Gene expression analysis revealed a correlation greater than 0.9 across all 15 cancer-related genes between the RNA-seq re-sults and targeted RNA panel. Among 1253 NSCLC FFPE tumor samples, multiple mutation types were called from DNA- and RNA-seq data and compared between the two assays. The DNA panel detected 103 fusions and 21 METex14 skipping events; 124 fusions and 26 METex14 skipping events were detected by the target RNA panel; 21 fusions and 4 METex14 skipping events were only detected by the target RNA panel. Among the 173 NSCLC samples negative for targetable mutations by DNA-seq, 15 (15/173, 8.67%) showed targetable gene fusions that may change clinical decisions with RNA-seq. In total, 226 tier I and tier II missense variants for NSCLC were analyzed at ge-nomic (DNA-seq) and transcriptomic (RNA-seq) levels. The positive percent agreement (PPA) was 97.8%, and the positive predictive value (PPV) was 98.6%. Interestingly, var-iant allele frequencies were generally higher at the RNA level than at the DNA level, suggesting relatively dominant expression of mutant alleles. PPA was 97.6% and PPV 99.38% for EGFR 19del and 20ins variants. We also explored the relationship of RNA expression with gene copy number and protein expression. The RPKM of EGFR transcripts assessed by the RNA panel showed a linear relationship with copy number quantified by the DNA panel, with an R of 0.8 in 1253 samples. In contrast, MET gene expression is regulated in a more complex manner. In IHC analysis, all 3+ samples exhibited higher RPKM levels; IHC level of 2+ and below showed lower RNA expression. Parallel DNA- and RNA-seq and systematic analysis demonstrated the accuracy and robustness of the RNA sequencing panel in identifying multiple types of variants for cancer therapy.