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At least 199 records · Page 11Linked to original sources
PubMed2006

Identifying biological concepts from a protein-related corpus with a probabilistic topic model.

BACKGROUND: Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE titles and abstracts by applying a probabilistic topic model. RESULTS: The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information. CONCLUSION: The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text.

Abstracting and Indexing↗
PubMed2000

Regulatory element detection using a probabilistic segmentation model.

The availability of genome-wide mRNA expression data for organisms whose genome is fully sequenced provides a unique data set from which to decipher how transcription is regulated by the upstream control region of a gene. A new algorithm is presented which decomposes DNA sequence into the most probable "dictionary" of motifs or words. Identification of words is based on a probabilistic segmentation model in which the significance of longer words is deduced from the frequency of shorter words of various length. This eliminates the need for a separate set of reference data to define probabilities, and genome-wide applications are therefore possible. For the 6,000 upstream regulatory regions in the yeast genome, the 500 strongest motifs from a dictionary of size 1,200 match at a significance level of 15 standard deviations to a database of cis-regulatory elements. Analysis of sets of genes such as those up-regulated during sporulation reveals many new putative regulatory sites in addition to identifying previously known sites.

Algorithms↗
PubMed2005

GenXHC: a probabilistic generative model for cross-hybridization compensation in high-density genome-wide microarray data.

MOTIVATION: Microarray designs containing millions to hundreds of millions of probes that tile entire genomes are currently being released. Within the next 2 months, our group will release a microarray data set containing over 12,000,000 microarray measurements taken from 37 mouse tissues. A problem that will become increasingly significant in the upcoming era of genome-wide exon-tiling microarray experiments is the removal of cross-hybridization noise. We present a probabilistic generative model for cross-hybridization in microarray data and a corresponding variational learning method for cross-hybridization compensation, GenXHC, that reduces cross-hybridization noise by taking into account multiple sources for each mRNA expression level measurement, as well as prior knowledge of hybridization similarities between the nucleotide sequences of microarray probes and their target cDNAs. RESULTS: The algorithm is applied to a subset of an exon-resolution genome-wide Agilent microarray data set for chromosome 16 of Mus musculus and is found to produce statistically significant reductions in cross-hybridization noise. The denoised data is found to produce enrichment in multiple gene ontology-biological process (GO-BP) functional groups. The algorithm is found to outperform robust multi-array analysis, another method for cross-hybridization compensation.

Animals↗
PubMed2025

Demixer: a probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample.

MOTIVATION: Multi-drug resistant or hetero-resistant tuberculosis (TB) hinders the successful treatment of TB. Hetero-resistant TB occurs when multiple strains of the TB-causing bacterium with varying degrees of drug susceptibility are present in an individual. Existing studies predicting the proportion and identity of strains in a mixed infection sample rely on a reference database of known strains. A main challenge then is to identify de novo strains not present in the reference database, while quantifying the proportion of known strains. RESULTS: We present Demixer, a probabilistic generative model that uses a combination of reference-based and reference-free techniques to delineate mixed infection strains in whole genome sequencing (WGS) data. Demixer extends a topic model widely used in text mining to represent known mutations and discover novel ones. Parallelization and other heuristics enabled Demixer to process large datasets like CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium). In both synthetic and experimental benchmark datasets, our proposed method precisely detected the identity (e.g. 91.67% accuracy on the experimental in vitro dataset) as well as the proportions of the mixed strains. In real-world applications, Demixer revealed novel high confidence mixed infections (101 out of 1963 Malawi samples analysed), and new insights into the global frequency of mixed infection (2% at the most stringent threshold in the CRyPTIC dataset) and its significant association to drug resistance. Our approach is generalizable and hence applicable to any bacterial and viral WGS data. AVAILABILITY AND IMPLEMENTATION: All code relevant to Demixer is available at https://github.com/BIRDSgroup/Demixer.

Mycobacterium tuberculosis↗
PubMedDate not supplied

Bayesian model selection for mining mass spectrometry data.

A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to microorganism detection from mass spectrometry data.

Algorithms↗
PubMed2006

A probabilistic methodology for integrating knowledge and experiments on biological networks.

Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, and systematically using these large datasets for refining existing knowledge is a major challenge. Here we introduce an extended computational framework that combines formalization of existing qualitative models, probabilistic modeling, and integration of high-throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge, and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph, and the framework accommodates partial measurements of diverse biological elements. We study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using hypothesis testing and derive p-values for learned model features. We test our methodology and algorithms on a simulated model and on two real yeast models. In particular, we use our method to explore uncharacterized relations among regulators in the yeast response to hyper-osmotic shock and in the yeast lysine biosynthesis system. Our integrative approach to the analysis of biological regulation is demonstrated to synergistically combine qualitative and quantitative evidence into concrete biological predictions.

Cell Physiological Phenomena↗
PubMed1990

A probabilistic contrast model of causal induction.

Deviations from the predictions of covariational models of causal attribution have often been reported in the literature. These include a bias against using consensus information, a bias toward attributing effects to a person, and a tendency to make a variety of unpredicted conjunctive attributions. It is contended that these deviations, rather than representing irrational biases, could be due to (a) unspecified information over which causal inferences are computed and (b) the questionable normativeness of the models against which these deviations have been measured. A probabilistic extension of Kelley's analysis-of-variance analogy is proposed. An experiment was performed to assess the above biases and evaluate the proposed model against competing ones. The results indicate that the inference process is unbiased.

Adult↗
PubMed2006

Hierarchical models for probabilistic dose-response assessment.

Probabilistic risk assessment is gaining acceptance as the most appropriate way to characterize and communicate uncertainties in estimates of human health risk and/or reference levels of exposure such as benchmark doses. Although probabilistic techniques are well established in the exposure-assessment component of the National Research Council's risk-assessment paradigm, they are less well developed in the dose-response-assessment component. This paper proposes the use of hierarchical statistical models as tools for implementing probabilistic dose-response assessments, in that such models provide a natural connection between the pharmacokinetic (PK) and pharmacodynamic (PD) components of dose-response models. The results show that incorporating internal dose information into dose-response assessments via the coupling of PK and PD models in a hierarchical structure can reduce the uncertainty in the dose-response assessment of risk. However, information on the mean of the internal dose distribution is sufficient; having information on the variance of internal dose does not affect the uncertainty in the resulting estimates of excess risks or benchmark doses. In addition, the complexity of a PK model of internal dose does not affect how the variability in risk is measured via the ultimate endpoint.

Dose-Response Relationship, Drug↗
PubMed2000

Technical report. The application of probability-generating functions to linear-quadratic radiation survival curves.

PURPOSE: To illustrate how probability-generating functions (PGFs) can be employed to derive a simple probabilistic model for clonogenic survival after exposure to ionizing irradiation. METHODS: Both repairable and irreparable radiation damage to DNA were assumed to occur by independent (Poisson) processes, at intensities proportional to the irradiation dose. Also, repairable damage was assumed to be either repaired or further (lethally) injured according to a third (Bernoulli) process, with the probability of lethal conversion being directly proportional to dose. Using the algebra of PGFs, these three processes were combined to yield a composite PGF that described the distribution of lethal DNA lesions in irradiated cells. RESULTS: The composite PGF characterized a Poisson distribution with mean, chiD+betaD2, where D was dose and alpha and beta were radiobiological constants. This distribution yielded the conventional linear-quadratic survival equation. To test the composite model, the derived distribution was used to predict the frequencies of multiple chromosomal aberrations in irradiated human lymphocytes. The predictions agreed well with observation. This probabilistic model was consistent with single-hit mechanisms, but it was not consistent with binary misrepair mechanisms. CONCLUSIONS: A stochastic model for radiation survival has been constructed from elementary PGFs that exactly yields the linear-quadratic relationship. This approach can be used to investigate other simple probabilistic survival models.

Cell Survival↗
PubMedDate not supplied

A probabilistic simulation model to assist the localization of nerve lesions.

I present a formal, mathematical specification of a probabilistic expert system to assist the localization of nerve lesions. The program is based on an anatomical model of the peripheral nervous system of the human upper limb. The simulation model defines a joint probability distribution over the states of nerves and clinical manifestations. A simple, general-purpose heuristic algorithm is used to approximate conditional probabilities of interest. It is shown how an upper bound on the expected approximation error can be measured experimentally; this upper bound is 0.05 for the system described here, although the bound can be made arbitrarily small by expending more computational effort. The expert system is compared with the nearest-neighbour statistical classification rule on two databases of 26 and 25 cases respectively. The expert system makes fewer errors, although the observed difference does not reach statistical significance. Possible future refinements to the model are explored, and the advantages of specifying expert systems formally are discussed.

Algorithms↗
PubMedDate not supplied

Finding the biologically optimal alignment of multiple sequences.

OBJECTIVE: Deterministic annealing, which is derived from statistical physics, is a method for obtaining the global optimum in parameter space. During the annealing process, starting from high temperatures which are then lowered, deterministic annealing deterministically find the (global) optimum at each temperature. Thus, deterministic annealing is expected to be more computationally efficient than stochastic sampling strategies to obtain the global optimum. We propose to apply the deterministic annealing technique to the problem of efficiently finding the biologically optimal alignment of multiple sequences. METHODS AND MATERIAL: We take a strategy based on probabilistic models for aligning multiple sequences. That is, we train a probabilistic model using given training sequences and obtain their alignment by parsing, i.e. searching for the most likely parse of each sequence and gaps using the trained parameters of the model. In this scenario, we propose a new stochastic model, which is simple enough to be suited to multiple sequence alignment and, unlike existing stochastic models, say a profile hidden Markov model (HMM), allows us to use similarity scores between symbols (or a symbol and a gap). We further present a learning algorithm for our simple model by combining deterministic annealing with an expectation-maximization (EM) algorithm. We emphasize that our approach is time-efficient, even if the training is done through an annealing process. RESULTS: In our experiments, we used actual protein sequences whose three-dimensional (3D) structures are determined and which are all aligned based on their 3D structures. We compared the results obtained by our approach with those by other existing approaches. Experimental results clearly showed that our approach gave the best performance, in terms of the similarity to the structurally determined alignment, among the approaches tested. Experimental results further indicated that our approach was ten times more efficient in terms of actual computation time than a competing method.

Algorithms↗
PubMed1984

DNA segregation in Escherichia coli cells with 5-bromodeoxyuridine-substituted nucleoids.

The pattern of segregation of DNA in Escherichia coli K-12 was analyzed by labeling replicating DNA with 5-bromodeoxyuridine followed by differential staining of nucleoids. Three types of visible arrangement were found in four-nucleoid groups derived from a native nucleoid after two replication rounds. Type A, segregation of both old strands toward cell poles, appeared with the highest frequency (0.6 to 0.8). Type B, segregation of one old strand toward the cell pole and the other toward the cell center, was twice as frequent as type C, segregation of both old strands toward the cell center. These results confirm previous data showing that DNA segregation in E. coli is nonrandom while presenting a certain degree of randomness. The proportions of the three indicated types of arrangement suggest a new probabilistic model to explain the observed segregation pattern. It is proposed that DNA strands segregate either nonrandomly, with a probability of between 0 and 1, or randomly. In nonrandom segregation, both old strands are always directed toward cell poles. Experimental data reported here or by other authors fit better with the predictions of this model than with those of other previously proposed proposed deterministic or probabilistic models.

Bromodeoxyuridine↗
PubMed2006

Early assessment of the likely cost-effectiveness of a new technology: A Markov model with probabilistic sensitivity analysis of computer-assisted total knee replacement.

OBJECTIVES: The objective of this study is to apply a Markov model to compare cost-effectiveness of total knee replacement (TKR) using computer-assisted surgery (CAS) with that of TKR using a conventional manual method in the absence of formal clinical trial evidence. METHODS: A structured search was carried out to identify evidence relating to the clinical outcome, cost, and effectiveness of TKR. Nine Markov states were identified based on the progress of the disease after TKR. Effectiveness was expressed by quality-adjusted life years (QALYs). The simulation was carried out initially for 120 cycles of a month each, starting with 1,000 TKRs. A discount rate of 3.5 percent was used for both cost and effectiveness in the incremental cost-effectiveness analysis. Then, a probabilistic sensitivity analysis was carried out using a Monte Carlo approach with 10,000 iterations. RESULTS: Computer-assisted TKR was a long-term cost-effective technology, but the QALYs gained were small. After the first 2 years, the incremental cost per QALY of computer-assisted TKR was dominant because of cheaper and more QALYs. The incremental cost-effectiveness ratio (ICER) was sensitive to the "effect of CAS," to the CAS extra cost, and to the utility of the state "Normal health after primary TKR," but it was not sensitive to utilities of other Markov states. Both probabilistic and deterministic analyses produced similar cumulative serious or minor complication rates and complex or simple revision rates. They also produced similar ICERs. CONCLUSIONS: Compared with conventional TKR, computer-assisted TKR is a cost-saving technology in the long-term and may offer small additional QALYs. The "effect of CAS" is to reduce revision rates and complications through more accurate and precise alignment, and although the conclusions from the model, even when allowing for a full probabilistic analysis of uncertainty, are clear, the "effect of CAS" on the rate of revisions awaits long-term clinical evidence.

Arthroplasty, Replacement, Knee↗
PubMed2004

Efficient approximations for learning phylogenetic HMM models from data.

MOTIVATION: We consider models useful for learning an evolutionary or phylogenetic tree from data consisting of DNA sequences corresponding to the leaves of the tree. In particular, we consider a general probabilistic model described in Siepel and Haussler that we call the phylogenetic-HMM model which generalizes the classical probabilistic models of Neyman and Felsenstein. Unfortunately, computing the likelihood of phylogenetic-HMM models is intractable. We consider several approximations for computing the likelihood of such models including an approximation introduced in Siepel and Haussler, loopy belief propagation and several variational methods. RESULTS: We demonstrate that, unlike the other approximations, variational methods are accurate and are guaranteed to lower bound the likelihood. In addition, we identify a particular variational approximation to be best-one in which the posterior distribution is variationally approximated using the classic Neyman-Felsenstein model. The application of our best approximation to data from the cystic fibrosis transmembrane conductance regulator gene region across nine eutherian mammals reveals a CpG effect.

Algorithms↗
PubMed1993

Protein secondary structure modelling with probabilistic networks.

In this paper we study the performance of probabilistic networks in the context of protein sequence analysis in molecular biology. Specifically, we report the results of our initial experiments applying this framework to the problem of protein secondary structure prediction. One of the main advantages of the probabilistic approach we describe here is our ability to perform detailed experiments where we can experiment with different models. We can easily perform local substitutions (mutations) and measure (probabilistically) their effect on the global structure. Window-based methods do not support such experimentation as readily. Our method is efficient both during training and during prediction, which is important in order to be able to perform many experiments with different networks. We believe that probabilistic methods are comparable to other methods in prediction quality. In addition, the predictions generated by our methods have precise quantitative semantics which is not shared by other classification methods. Specifically, all the causal and statistical independence assumptions are made explicit in our networks thereby allowing biologists to study and experiment with different causal models in a convenient manner.

Algorithms↗
PubMed2003

Pfold: RNA secondary structure prediction using stochastic context-free grammars.

RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applications, but often it is possible to obtain related RNA sequences with conserved secondary structure. These should be included in structural analyses to give improved results. This work presents a practical way of predicting RNA secondary structure that is especially useful when related sequences can be obtained. The method improves a previous algorithm based on an explicit evolutionary model and a probabilistic model of structures. Predictions can be done on a web server at http://www.daimi.au.dk/~compbio/pfold.

Algorithms↗
PubMed2000

Using database matches with for HMMGene for automated gene detection in Drosophila.

The application of the gene finder HMMGene to the Adh region of the Drosophila melanogaster is described, and the prediction results are analyzed. HMMGene is based on a probabilistic model called a hidden Markov model, and the probabilistic framework facilitates the inclusion of database matches of varying degrees of certainty. It is shown that database matches clearly improve the performance of the gene finder. For instance, the sensitivity for coding exons predicted with both ends correct grows from 62% to 70% on a high-quality test set, when matches to proteins, cDNAs, repeats, and transposons are included. The specificity drops more than the sensitivity increases when ESTs are used. This is due to the high noise level in EST matches, and it is discussed in more detail why this is and how it might be improved.

Alcohol Dehydrogenase↗