Vehicle platoon has attracted much attention in recent years for its benefits in improving traffic efficiency, road safety, and energy consumption. In a vehicle platoon, vehicles can take on the roles of either a leading vehicle or a following vehicle, depending on the need. Selecting a reliable leading vehicle is crucial to improve the reliability of the vehicle platoon, and reputation management plays a vital role in this selection process. However, the existing reputation management schemes do not distinguish the leading vehicle's reputation value (LV-Reputation value) and the following vehicle's reputation value (FV-Reputation value), and merge them into a single reputation value, which exposes the reputation management scheme to the single reputation attack, an attack identified for the first time in this work. Additionally, some schemes fail to preserve the vehicle reputation value privacy, feedback score privacy, or identity privacy, and some overlook the security of the reputation management scheme. To address these issues, we propose a Privacy-Preserving Dual Reputation (PPDR) management scheme in vehicle platoon. The PPDR scheme separates a vehicle's reputation value into LV-Reputation value and FV-Reputation value, effectively mitigating the single reputation attack. Furthermore, under the premise of preserving reputation value privacy, feedback score privacy, and identity privacy, the PPDR scheme employs a score difference calculation algorithm to support the weighted average mechanism and the dual reputation management mechanism, which can significantly improve the accuracy of reputation management scheme. It also provides strong security with acceptable computation communication overheads. Comprehensive theoretical analysis and simulation evaluation have been carried out, and the results demonstrate that the PPDR scheme is significantly superior than the existing schemes in several aspects.

Multi-label text classification (MLTC) is essential in natural language processing. For real applications, MLTC is challenging when class distribution is long-tailed, that is, a few of labels (head labels) have many documents while most labels (tail labels) have a few documents. In this paper, we propose a Multi-Information Filter Encoding Network (MIFEN) to address the issue of long-tailed distribution. Specifically, MIFEN constructs Multi-Information Filter Encoder to filter useless information and optimize the text space and label space. Then, MIFEN enriches the tail label-related features by extracting filtered text-specific label information for each document and highlights significant features of the corresponding text and label information to generate the final document representation. Experiment results have demonstrated that our MIFEN outperforms better than the state-of-the-art methods.

In programming courses, providing students with concise and constructive feedback on faulty submissions (programs) is highly desirable. However, providing feedback manually is often time-consuming and tedious. To release tutors from the manual construction of concise feedback, researchers have proposed approaches such asCLARAandRefactoryto construct feedback automatically. The key to such approaches is to fix a faulty program by making it equivalent to one of its correct reference programs whose overall structure is identical to that of the faulty submission. However, for a newly released assignment, it is likely that there are no correct reference programs at all, let alone correct reference programs sharing identical structure with the faulty submission. Therefore, in this paper, we proposeAssignmentMendergenerating concise patches for newly released assignments. The key insight ofAssignmentMenderis that a faulty submission can be repaired by reusing fine-grained code snippets from submissions (even when they are faulty) for the same assignment. It automatically locates suspicious code in the faulty program and leverages static analysis to retrieve reference code from existing submissions with a graph-based matching algorithm. Finally, it generates candidate patches by modifying the suspicious code based on the reference code. Different from existing approaches,AssignmentMenderexploits faulty submissions in addition to bug-free submissions to generate patches. Another advantage ofAssignmentMenderis that it can leverage submissions whose overall structures are different from those of the to-be-fixed submission. Evaluation results on 128 faulty submissions from 10 assignments show thatAssignmentMenderimproves the state-of-the-art in feedback generation for newly released assignments. A case study involving 40 students and 80 submissions further provides initial evidence showing that the proposed approach is useful in practice.

MOTIVATION: Epistasis may play an etiologic role in complex diseases, but research has been hindered because identification of interactions among sets of single nucleotide polymorphisms (SNPs) requires exploration of immense search spaces. Current approaches using nuclear families accommodate at most several hundred candidate SNPs. RESULTS: GADGETS detects epistatic SNP-sets by applying a genetic algorithm to case-parent or case-sibling data. To allow for multiple epistatic sets, island subpopulations of SNP-sets evolve separately under selection for evident joint relevance to disease risk. The software evaluates the identified SNP-sets via permutation testing and provides graphical visualization. GADGETS correctly identified epistatic SNP-sets in realistically simulated case-parent triads with 10 000 candidate SNPs, far more SNPs than competitors can handle, and it outperformed competitors in simulations with many fewer SNPs. Applying GADGETS to family-based oral-clefting data from dbGaP identified SNP-sets with possible epistatic effects on risk. AVAILABILITY AND IMPLEMENTATION: GADGETS is part of the epistasisGA package at https://github.com/mnodzenski/epistasisGA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. RESULT: We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS: The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address tissue heterogeneity have drawbacks. Experimental techniques, such as fluorescence-activated cell sorting, and single cell RNA sequencing are expensive. Computational approaches that use expression data from heterogeneous samples are promising, but most of the current methods estimate either cell-type proportions or cell-type-specific expression profiles by requiring the other as input. Although such partial deconvolution methods have been successfully applied to tumor samples, the additional input required may be unavailable. We introduce a novel complete deconvolution method, CDSeq, that uses only RNA-Seq data from bulk tissue samples to simultaneously estimate both cell-type proportions and cell-type-specific expression profiles. Using several synthetic and real experimental datasets with known cell-type composition and cell-type-specific expression profiles, we compared CDSeq's complete deconvolution performance with seven other established deconvolution methods. Complete deconvolution using CDSeq represents a substantial technical advance over partial deconvolution approaches and will be useful for studying cell mixtures in tissue samples. CDSeq is available at GitHub repository (MATLAB and Octave code): https://github.com/kkang7/CDSeq.

Abstract Summary: We introduce a web-based tool, Peak Annotation and Visualization (PAVIS), for annotating and visualizing ChIP-seq peak data. PAVIS is designed with non-bioinformaticians in mind and presents a straightforward user interface to facilitate biological interpretation of ChIP-seq peak or other genomic enrichment data. PAVIS, through association with annotation, provides relevant genomic context for each peak, such as peak location relative to genomic features including transcription start site, intron, exon or 5′/3′-untranslated region. PAVIS reports the relative enrichment P-values of peaks in these functionally distinct categories, and provides a summary plot of the relative proportion of peaks in each category. PAVIS, unlike many other resources, provides a peak-oriented annotation and visualization system, allowing dynamic visualization of tens to hundreds of loci from one or more ChIP-seq experiments, simultaneously. PAVIS enables rapid, and easy examination and cross-comparison of the genomic context and potential functions of the underlying genomic elements, thus supporting downstream hypothesis generation. Availability and Implementation: PAVIS is publicly accessed at http://manticore.niehs.nih.gov/pavis. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

UNLABELLED: ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. AVAILABILITY: Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.

MOTIVATION: ChIP-seq data are enriched in binding sites for the protein immunoprecipitated. Some sequences may also contain binding sites for a coregulator. Biologists are interested in knowing which coregulatory factor motifs may be present in the sequences bound by the protein ChIP'ed. RESULTS: We present a finite mixture framework with an expectation-maximization algorithm that considers two motifs jointly and simultaneously determines which sequences contain both motifs, either one or neither of them. Tested on 10 simulated ChIP-seq datasets, our method performed better than repeated application of MEME in predicting sequences containing both motifs. When applied to a mouse liver Foxa2 ChIP-seq dataset involving ~ 12 000 400-bp sequences, coMOTIF identified co-occurrence of Foxa2 with Hnf4a, Cebpa, E-box, Ap1/Maf or Sp1 motifs in ~6-33% of these sequences. These motifs are either known as liver-specific transcription factors or have an important role in liver function. AVAILABILITY: Freely available at http://www.niehs.nih.gov/research/resources/software/comotif/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a Z-score or P-value is used as the test statistic. Error rates under multiple comparisons are not fully considered. METHODOLOGY: We propose a simple but novel approach, fdrMotif, that selects as many binding sites as possible while controlling a user-specified false discovery rate (FDR). Unlike existing iterative methods, fdrMotif combines model optimization [e.g. position weight matrix (PWM)] and significance testing at each step. By monitoring the proportion of binding sites selected in many sets of background sequences, fdrMotif controls the FDR in the original data. The model is then updated using an expectation (E)- and maximization (M)-like procedure. We propose a new normalization procedure in the E-step for updating the model. This process is repeated until either the model converges or the number of iterations exceeds a maximum. RESULTS: Simulation studies suggest that our normalization procedure assigns larger weights to the binding sites than do two other commonly used normalization procedures. Furthermore, fdrMotif requires only a user-specified FDR and an initial PWM. When tested on 542 high confidence experimental p53 binding loci, fdrMotif identified 569 p53 binding sites in 505 (93.2%) sequences. In comparison, MEME identified more binding sites but in fewer ChIP sequences than fdrMotif. When tested on 500 sets of simulated 'ChIP' sequences with embedded known p53 binding sites, fdrMotif, compared to MEME, has higher sensitivity with similar positive predictive value. Furthermore, fdrMotif is robust to noise: it selected nearly identical binding sites in data adulterated with 50% added background sequences and the unadulterated data. We suggest that fdrMotif represents an improvement over MEME. AVAILABILITY: C code can be found at: http://www.niehs.nih.gov/research/resources/software/fdrMotif/.

BACKGROUND: Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed. RESULTS: For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing. CONCLUSION: The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets.

MOTIVATION: Position weight matrices (PMWs) are simple models commonly used in motif-finding algorithms to identify short functional elements, such as cis-regulatory motifs, on genes. When few experimentally verified motifs are available, estimation of the PWM may be poor. The resultant PWM may not reliably discriminate a true motif from a false one. While experimentally identifying such motifs remains time-consuming and expensive, low-resolution binding data from techniques such as ChIP-on-chip and ChIP-PET have become available. We propose a novel but simple method to improve a poorly estimated PWM using ChIP data. METHODOLOGY: Starting from an existing PWM, a set of ChIP sequences, and a set of background sequences, our method, GAPWM, derives an improved PWM via a genetic algorithm that maximizes the area under the receiver operating characteristic (ROC) curve. GAPWM can easily incorporate prior information such as base conservation. We tested our method on two PMWs (Oct4/Sox2 and p53) using three recently published ChIP data sets (human Oct4, mouse Oct4 and human p53). RESULTS: GAPWM substantially increased the sensitivity/specificity of a poorly estimated PWM and further improved the quality of a good PWM. Furthermore, it still functioned when the starting PWM contained a major error. The ROC performance of GAPWM compared favorably with that of MEME and others. With increasing availability of ChIP data, our method provides an alternative for obtaining high-quality PWMs for genome-wide identification of transcription factor binding sites. AVAILABILITY: The C source code and all data used in this report are available at http://dir.niehs.nih.gov/dirbb/gapwm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Obtaining high quality alignments of divergent homologous sequences for cross-species sequence comparison remains a challenge. RESULTS: We propose a novel pairwise sequence alignment algorithm, ACANA (ACcurate ANchoring Alignment), for aligning biological sequences at both local and global levels. Like many fast heuristic methods, ACANA uses an anchoring strategy. However, unlike others, ACANA uses a Smith-Waterman-like dynamic programming algorithm to recursively identify near-optimal regions as anchors for a global alignment. Performance evaluations using a simulated benchmark dataset and real promoter sequences suggest that ACANA is accurate and consistent, especially for divergent sequences. Specifically, we use a simulated benchmark dataset to show that ACANA has the highest sensitivity to align constrained functional sites compared to BLASTZ, CHAOS and DIALIGN for local alignment and compared to AVID, ClustalW, DIALIGN and LAGAN for global alignment. Applied to 6007 pairs of human-mouse orthologous promoter sequences, ACANA identified the largest number of conserved regions (defined as over 70% identity over 100 bp) compared to AVID, ClustalW, DIALIGN and LAGAN. In addition, the average length of conserved region identified by ACANA was the longest. Thus, we suggest that ACANA is a useful tool for identifying functional elements in cross-species sequence analysis, such as predicting transcription factor binding sites in non-coding DNA. AVAILABILITY: ACANA software and test sequence data are publicly available at http://BioMedEmpire.org/

BACKGROUND: Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. RESULTS: We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at 1. CONCLUSION: Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods.

BACKGROUND: Recent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo. Instead, some circadian-related genes may be delayed by 4-8 hrs in peak expression in one tissue relative to the other. These interesting biological observations prompt a statistical question regarding how to distinguish the synchronized genes from genes that are systematically lagged in phase/peak expression time across two tissues. RESULTS: We propose a set of techniques from circular statistics to analyze phase angles of circadian-related genes in two tissues. We first estimate the phases of a cycling gene separately in each tissue, which are then used to estimate the paired angular difference of the phase angles of the gene in the two tissues. These differences are modeled as a mixture of two von Mises distributions which enables us to cluster genes into two groups; one group having synchronized transcripts with the same phase in the two tissues, the other containing transcripts with a discrepancy in phase between the two tissues. For each cluster of genes we assess the association of phases across the tissue types using circular-circular regression. We also develop a bootstrap methodology based on a circular-circular regression model to evaluate the improvement in fit provided by allowing two components versus a one-component von-Mises model. CONCLUSION: We applied our proposed methodologies to the circadian-related genes common to heart and liver tissues in Storch et al. 2, and found that an estimated 80% of circadian-related transcripts common to heart and liver tissues were synchronized in phase, and the other 20% of transcripts were lagged about 8 hours in liver relative to heart. The bootstrap p-value for being one cluster is 0.063, which suggests the possibility of two clusters. Our methodologies can be extended to analyze peak expression times of circadian-related genes across more than two tissues, for example, kidney, heart, liver, and the suprachiasmatic nuclei (SCN) of the hypothalamus.

SUMMARY: Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization-time-of-flight proteomics data. AVAILABILITY: The source code of the program and documentation on how to use it are freely available to non-commercial users at http://dir.niehs.nih.gov/dirbb/lifiles/softlic.htm

We propose an algorithm for selecting and clustering genes according to their time-course or dose-response profiles using gene expression data. The proposed algorithm is based on the order-restricted inference methodology developed in statistics. We describe the methodology for time-course experiments although it is applicable to any ordered set of treatments. Candidate temporal profiles are defined in terms of inequalities among mean expression levels at the time points. The proposed algorithm selects genes when they meet a bootstrap-based criterion for statistical significance and assigns each selected gene to the best fitting candidate profile. We illustrate the methodology using data from a cDNA microarray experiment in which a breast cancer cell line was stimulated with estrogen for different time intervals. In this example, our method was able to identify several biologically interesting genes that previous analyses failed to reveal.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: [email protected]