Proteome coverage prediction with variants of the Chinese restaurant process construction

Motivation: Modern sequencing technology now permits the sequencing of entire genomes, leading to thousands of new gene sequences in need of detailed annotation. It is too time consuming to predict the properties of each protein sequence manually and to organize the results of many prediction tools by hand. The prediction process must be automated so the predictions can be automatically organized, but the predictions must also be transparent. That is, the rationale for each prediction should be easily examinable by anyone that wishes to use the prediction. Results: Proteome Analyst (PA) is a web-based system for predicting the properties of each protein in a proteome. PA has three interesting features. First, it provides a single web-based tool that allows the user to select a wide range of analytic tools and automatically apply them to each protein in a proteome. In essence, PA provides one-stop automatic high-throughput analysis. Second, PA has the ability to explain its predictions to users. PA is based on established machine learning techniques, but makes every prediction transparent to its users. Third, PA allows users to easily create their own transparent custom predictors without programming. Availability: http://www.cs.ualberta.ca/~bioinfo/PA Supplementary Information: http://www.cs.ualberta.ca/~bioinfo/PA/Walkthrough http://www.cs.ualberta.ca/~bioinfo/PA/Experiments Contact: bioinfo@cs.ualberta.ca INTRODUCTION High-throughput sequencing technology has made it possible for even the smallest laboratory to sequence the genome of organelles, viruses and bacteria. Indeed there are now more than 1200 genome sequences deposited in public databases (http://www.ebi.ac.uk/genomes/) and this number is growing rapidly. Given the size and complexity of these data sets, most researchers are compelled to use automated annotation systems to identify or classify individual genes and proteins in their genomic data. A number of excellent systems have been developed over the past few years that permit automated genome-wide or proteome-wide annotation. These include GeneQuiz (Andrade et al., 1999), GeneAtlas (Kitson et al., 2002), EnsEMBL (Hubbard et al., 2002), PEDANT (Frishman et al., 2001), Genotator (Harris, 1997), Magpie (Gaasterland and Sensen, 1996) and GAIA (Overton et al., 1998). These use web-based tools to identify genes, parse data, translate sequences, search against public databases, identify domains or motifs and perform predictive analyses of protein sequences. Many of these packages provide user-customizable search schemas and graphical, hyperlinked output. The level of interpretation or inference offered by these annotation systems varies widely, with some offering only raw data (lists of homologues, calculated properties, etc.) in a consolidated format and others inferring function or ontology through detailed lexical analysis. Our PA system focuses on the task of prediction or classification. Our results show that classification/prediction can be used for many annotations. The most obvious is general-function, but we are also working on sub-cellular localization, specific function and protein-protein interaction are others we are working on. To use classification, we require an ontology or “dictionary” of class terms. Consequently, a number of controlled vocabularies or ontologies of protein function have started appearing. Among the first was the enzyme classification scheme (E.C. number) developed by the IUBMB and first employed in the ENZYME database (Bairoch, 1993). Later, Riley (1993) developed functional classification schemas for E. coli and other microbes. Variations on these functional vocabularies were added as other genomes and genome annotation systems were

The Pitman-Yor, or Chinese restaurant process, is a stochastic process that generates distributions following a power law with exponents lower than 2, as found in numerous physical, biological, technological, and social systems. We discuss its rich behavior with the tools and viewpoint of statistical mechanics. We show that this process invariably gives rise to a condensation, i.e., a distribution dominated by a finite number of classes. We also evaluate thoroughly the finite-size effects, finding that the lack of stationary state and self-averaging of the process creates realization-dependent cutoffs and behavior of the distributions with no equivalent in other statistical mechanical models.

Among the world's writing systems, the Chinese logographic system is considered to contrast markedly with alphabetic systems such as that used for English. Due to the very different grapheme-phoneme correspondences (GPC) in these writing systems, word reading has often been supposed to differ radically for English and Chinese readers. Original Dual Route model predicts one group resorting to sound but the other relying on visual graphic cues to achieve word recognition and comprehension. This is the essence of the original Dual Route word reading processing. However, recent studies have offered an alternative perspective on this, exploring a variety of factors in readers' cognitive processing, and finding that phonological processing is universally available to all readers regardless of their writing system background, of which saying even applies to readers with Chinese as L1 (reader's first language or mother tongue) who can then, in fact, learn English as L2 (reader's second language) more successfully as a result of it. Here we would like to review the evolution of Dual Route Model and its related experiments, first cross-linguistically and then specifically for Chinese word reading, followed by Parallel-Distributed (or Interactive) Processing Model as a contrast. The purpose is to establish background information on the available models which attempt to explain the process of word reading/recognition, as well as cognitive factors which might also affect readers' use of the available phonological and orthographic processing routes. The end goal is to depict how the development of the dual mechanism evolves to embrace the interactive components of word reading processing, thus provides pedagogical value which is universal in specifying readers' cognitive effects on how readers process their L1 and L2 word reading.

Based on the data of 30 sample trees from 15 permanent plots of Chinese fir in the national forest farm of Jiangle, at first we study the best function as the base model with the least square method among five growth profile equations. The nonlinear mixed model was constructed based on the base model and modeling data. We use the R for model fitting. Select the mixed model with the minimum value of AIC, BIC and the maximum value of Loglik as the best model by changing the number of mixed parameters in fitting progress. Using mixed model to predict growth profile of height and studying the characteristics of Empirical Best Linear Unbiased Predictor (EBLUP). Fitting results showed the simulation's precision of Weibull's including three random effect parameters(β1, β2 and β3) was maximal. In the analysis of prediction, prediction accuracy decreased as age interval of observations extended with the same number of previous observations. MSE decreased as the number of previous observations increased. EBLUP prediction could fully predict individual growth process, given that there were multiple previous observations with long-enough age intervals.

To screen serum proteome biomarkers and establish predictive model with relation to the formation of portal vein tumor thrombi (PVTT) in hepatocellular carcinoma (HCC) patients. Serum samples were collected from 135 HCC patients, which were divided, into training set (including 33 HCC patients with PVTT and 62 HCC patients without PVTT) and blind testing set (including 18 HCC patients with PVTT and 22 HCC patients without PVTT). Special serum protein or peptide pattern was determined by SELDI-TOF-MS measurement after treating the sample onto WCX2 protein chip for each case. The obtained data were analyzed by BioMarker Wizard software to screen serum proteome biomarkers with relation to the formation of PVTT, while decision tree classification algorithm and blind validation were determined by Biomarker Patterns Software. Ranging from 1100 to 30 000 at the m/z value, 100 protein features were detected in the serum protein pattern stably. Among them, 6 protein peaks with the m/z value of 3478, 1314, 1744, 1725, 2022 and 3380 were upregulated, 10 proteins peaks with the m/z value of 8901, 9353, 9415, 8773, 2766, 2745, 8697, 7773, 8569 and 1373 were downregulated respectively in the group of HCC with PVTT. The 7 candidate protein peaks with the m/z value of 3478, 2022, 8901, 9415, 8773, 2766 and 2745 were selected to establish predictive model by BPS with a sensitivity of 75.8% (25/33) and specificity of 82.3% (51/62). An accuracy of 87.5% (35/40), sensitivity of 100% (18/18), specificity of 77.3% (17/22), positive predictive value of 78.3% (18/23), and negative predictive value of 100% (17/17) were validated in blind testing set. Sixteen candidate proteome biomarkers may be related with the formation of PVTT in HCC patients. Decision tree classification algorithm may have great clinical significance in predicting the formation of PVTT.

The brain is the most mysterious and intricate biological structure known to man. It dominates the way we live, think, reason and behave. It is built of billions of neurons that communicate with each other through trillions of connections using electrical signals. The neocortex is undoubtedly the most complex brain region, hosting all higher brain functions such as sensory perception and cognitive behavior. Scientists have been struggling for more than a century to identify the anatomical blueprint of its cellular organization and to understand its dynamic behavior. The Blue Brain Project aims to develop the first simulation-based research environment for modeling and studying the basic building blocks of the neocortex, the neocortical column. The model column is based on data obtained from the juvenile rat somatosensory cortex. The best way to build such a large-scale model is by following a data-constraint driven approach and integrating biological information at different levels. In this thesis, I present novel informatics approaches to systematically constrain the membrane protein composition of different neocortical neuron types. In the first study, I investigate the single-cell gene expression patterns of ion channels in different neuron types and present a combinatorial rule extractor model that reverse engineers the observed expression rules in ten different neocortical neuron types. In the second study, I present a physical constrain checker that mines different publicly available data resources regarding all known membrane proteins such as channels and receptors, and extracts available information relating to their specific location on different parts of the neuronal morphologies, their 3D morphological structures as well as their genetic and proteomic annotations. Combining the results from both studies allows us to build neuronal models that better reflect the biological reality of neocortical neurons, ensuring the correct localization and distribution of membrane proteins across neuronal morphologies and computing their maximum count number based on their molecular surface areas and the available neuronal membrane surface area. This novel data-constraint driven approach is the first step towards integrating existing biological knowledge and constraints from various data sources into the process of building neuron models, in this way improving the neuron models as they come closer to the biological reality of real neurons.

The Chinese restaurant process is a basic sequential construction of consistent random partitions. We consider random point measures describing the composition of small blocks in such partitions and show that their scaling limit is given by the projective limit of certain inhomogeneous Poisson measures on cones of increasing dimension. This result makes it possible to derive classical and functional limit theorems in the Skorokhod topology for various characteristics of the Chinese restaurant process.

We present a novel generative model for audio event transcription that recognizes “events” on audio signals including multiple kinds of overlapping sounds. In the proposed model, firstly, the overlapping audio events are modeled based on nonnegative matrix factorization into which Bayesian nonparametric approaches: the Markov Indian buffet process and the Chinese restaurant process, are incorporated. This approach allows us to automatically transcribe the events while avoiding the model selection problem by assuming a countably infinite number of possible audio events in the input signal. Then, Bayesian logistic regression annotates the audio frames with the multiple event labels in a semi-supervised learning setup. Experimental results show that our model can better annotate an audio signal in comparison with a baseline method. Additionally, we verify that our infinite generative model is also able to detect unknown audio events that are not included in the training data.

This paper presents an autonomous cognitive radio (CR) architecture, referred to as the Radiobot. This model goes beyond adaptive radio systems to exploit the main ingredients of cognition which, in this context, are mainly self-learning and self-reconfiguration. Without any prior knowledge of the RF environment, the Radiobot applies a sequence of increasingly sophisticated processing steps to detect and identify the sensed signals. In particular, in this paper, it applies a blind energy detection followed by a cyclostationary detection method to detect the active signals and extract their underlying periodic properties as reflected in cyclic frequencies. These extracted signal features are classified based on the Chinese restaurant process (CRP) and a learning algorithm is applied to achieve autonomous self-reconfiguration of the sensing module. We analyze the impact of fading and Doppler frequency shift on both the energy and cyclostationary detections, and show the receiver operating characteristic (ROC) of the carrier frequency detector. We show the robustness of the cyclostationary detection against channel fading and wide-sense stationary noise. Simulation results are presented to verify the multi-band operability and the reconfiguration ability of the Radiobot and to verify the convergence of the proposed learning algorithm.

Crowdsourcing has been proven to be an effective and efficient tool to annotate large data-sets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese restaurant process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms.

An important problem in evolutionary genomics is to investigate whether a certain trait measured on each sample is associated with the sample phylogenetic tree. The phylogenetic tree represents the shared evolutionary history of the samples and it is usually estimated from molecular sequence data at a locus or from other type of genetic data. We propose a model for trait evolution inspired by the Chinese Restaurant Process that includes a parameter that controls the degree of preferential attachment, that is, the tendency of nodes in the tree to subtend from nodes of the same type. This model with no preferential attachment is equivalent to a structured coalescent model with simultaneous migration and coalescence events and serves as a null model. We derive a test for phylogenetic binary trait association with linear computational complexity and empirically demonstrate that it is more powerful than some other methods. We apply our test to study the phylogenetic association of some traits in swordtail fish, breast cancer, yellow fever virus, and influenza A H1N1 virus. R-package implementation of our methods is available at https://github.com/jyzhang27/CRPTree.

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while `multi-sense' methods have been proposed and tested on artificial word-similarity tasks, we don't know if they improve real natural language understanding tasks. In this paper we introduce a multi-sense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language understanding. We then test the performance of our model on part-of-speech tagging, named entity recognition, sentiment analysis, semantic relation identification and semantic relatedness, controlling for embedding dimensionality. We find that multi-sense embeddings do improve performance on some tasks (part-of-speech tagging, semantic relation identification, semantic relatedness) but not on others (named entity recognition, various forms of sentiment analysis). We discuss how these differences may be caused by the different role of word sense information in each of the tasks. The results highlight the importance of testing embedding models in real applications.

Traditional radar signal sorting (RSS) methods assume that all types of radar signals can be stored and trained offline. However, the presence of noncooperative radar emitters makes the assumption unrealistic in real-world RSS problems. This article proposes a real-time method for RSS in complex electromagnetic environments based on the Bayesian framework. Each type of radar signal is regarded as a collection of samples from Gaussian distribution. Bayesian inference is applied to calculate the likelihood of a new data point belonging to a known category, and the Chinese restaurant process (CRP) is adopted to provide some prior knowledge which helps calculate the probability of opening a new cluster. For the overclustering problem caused by dynamic and large-scale changes in pulse descriptor words (PDWs) of the signals from the same radar emitter, a dynamic cluster merging (DCM) algorithm is introduced. The Wasserstein distance and Hausdorff distance of the probability density functions (pdfs) of the PDWs between clusters are weighted, and the pulse repetition interval (PRI) is also considered to measure the similarity between clusters which helps merge clusters of the same type of signal. The method proposed in this article is verified under the conditions of large-scale changes in PDWs and dynamic changes in signal numbers over time. Higher accuracy of real-time signal sorting is achieved on the simulated dataset of signals from 30 types of radar emitters compared with existing methods. Even in the situations with a significant pulse loss rate, the proposed algorithm still attains high accuracy.

Metagenomes present assembly challenges, when assembling multiple genomes from mixed reads of multiple species. An assembler for single genomes can’t adapt well when applied in this case. A metagenomic assembler, Genovo, is a de novo assembler for metagenomes under a generative probabilistic model. Genovo assembles all reads without discarding any reads in a preprocessing step, and is therefore able to extract more information from metagenomic data and, in principle, generate better assembly results. Paired end sequencing is currently widely-used yet Genovo was designed for 454 single end reads. In this research, we attempted to extend Genovo by incorporating paired-end information, named Xgenovo, so that it generates higher quality assemblies with paired end reads.First, we extended Genovo by adding a bonus parameter in the Chinese Restaurant Process used to get prior accounts for the unknown number of genomes in the sample. This bonus parameter intends for a pair of reads to be in the same contig and as an effort to solve chimera contig case. Second, we modified the sampling process of the location of a read in a contig. We used relative distance for the number of trials in the symmetric geometric distribution instead of using distance between the offset and the center of contig used in Genovo. Using this relative distance, a read sampled in the appropriate location has higher probability. Therefore a read will be mapped in the correct location.Results of extensive experiments on simulated metagenomic datasets from simple to complex with species coverage setting following uniform and lognormal distribution showed that Xgenovo can be superior to the original Genovo and the recently proposed metagenome assembler for 454 reads, MAP. Xgenovo successfully generated longer N50 than Genovo and MAP while maintaining the assembly quality even for very complex metagenomic datasets consisting of 115 species. Xgenovo also demonstrated the potential to decrease the computational cost. This means that our strategy worked well. The software and all simulated datasets are publicly available online at http://xgenovo.dna.bio.keio.ac.jp.

Evaluation of glass evidence at activity level: A new distribution for the background population