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PubMed2003

A constrained EM algorithm for principal component analysis.

We propose a constrained EM algorithm for principal component analysis (PCA) using a coupled probability model derived from single-standard factor analysis models with isotropic noise structure. The single probabilistic PCA, especially for the case where there is no noise, can find only a vector set that is a linear superposition of principal components and requires postprocessing, such as diagonalization of symmetric matrices. By contrast, the proposed algorithm finds the actual principal components, which are sorted in descending order of eigenvalue size and require no additional calculation or postprocessing. The method is easily applied to kernel PCA. It is also shown that the new EM algorithm is derived from a generalized least-squares formulation.

Algorithms↗
PubMed1999

Probabilistic synaptic transmission in the associative net.

The associative net model of heteroassociative memory with binary-valued synapses has been extended to include recent experimental data indicating that in the hippocampus, one form of synaptic modification is a change in the probability of synaptic transmission. Pattern pairs are stored in the net by a version of the Hebbian learning rule that changes the probability of transmission at synapses where the presynaptic and post-synaptic units are simultaneously active from a low, base value to a high, modified value. Numerical calculations of the expected recall response of this stochastic associative net have been used to assess the performance for different values of the base and modified probabilities. If there is a cost incurred with generating the difference between these probabilities, then a difference of about 0. 4 is optimal. This corresponds to the magnitude of change seen experimentally. Performance can be greatly enhanced by using multiple cue presentations during recall.

Association↗
PubMed2006

Emotions and golf performance: An IZOF-based applied sport psychology case study.

A multiple case study investigation is reported in which emotions and performance were assessed within the probabilistic individual zone of optimal functioning (IZOF) model (Kamata, Tenenbaum,& Hanin, 2002) to develop idiosyncratic emotion-performance profiles. These profiles were incorporated into a psychological skills training (PST) intervention, with a focus on three emotional dimensions, that is, arousal, pleasantness, and functionality, and several psychological strategies employed during practice and competition. Two female varsity golfers at a major Division I university in the Southeast participated in the case study during the Spring 2002 season. The PST intervention resulted in enhanced emotional self-regulation skills and improved golf performance. Directions for future research into the IZOF model and implications for practical application of the model are discussed.

Achievement↗
PubMed2003

RNA analysis of B cell lines arrested at defined stages of differentiation allows for an approximation of gene expression patterns during B cell development.

The development of a mature B lymphocyte from a bone marrow stem cell is a highly ordered process involving stages with defined features and gene expression patterns. To obtain a deeper understanding of the molecular genetics of this process, we have performed RNA expression analysis of a set of mouse B lineage cell lines representing defined stages of B cell development using Affymetrix microarrays. The cells were grouped based on their previously defined phenotypic features, and a gene expression pattern for each group of cell lines was established. The data indicated that the cell lines representing a defined stage generally presented a high similarity in overall expression profiles. Numerous genes could be identified as expressed with a restricted pattern using dCHIP-based, quantitative comparisons or presence/absence-based, probabilistic state analysis. These experiments provide a model for gene expression during B cell development, and the correctly identified expression patterns of a number of control genes suggest that a series of cell lines can be useful tools in the elucidation of the molecular genetics of a complex differentiation process.

Animals↗
PubMed2026

Deconvolution of evolutionary architecture unmasks a high-risk, subclonal-rich subtype in treatment-naive small cell lung cancer.

BACKGROUND: Intratumoral heterogeneity (ITH) drives therapeutic resistance in small cell lung cancer (SCLC). However, conventional single-sample analysis has limited horizontal, cross-patient comparisons, leaving the overarching evolutionary architecture in treatment-naive tumors poorly understood. This study aims to deconvolve these architectures to identify clinically relevant evolutionary subtypes. METHODS: We analyzed whole-exome sequencing data from 41 treatment-naive SCLC patients. To overcome the cross-patient comparability bottleneck, we developed a novel probabilistic framework using a refined Gaussian Mixture Model (GMM). This standardized subclonal structures into four hierarchical strata, enabling the identification of evolutionary subtypes via unsupervised clustering. To address the scarcity of SCLC public data, prognostic concordance was robustly explored in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) based on shared smoking etiology, with lung adenocarcinoma (LUAD) serving as a negative control. RESULTS: The cohort robustly segregated into "Clonal-dominant" (Group 1, n=28) and "Subclonal-rich" (Group 2, n=13) subtypes. Group 1 evolution was primarily driven by tobacco signatures (SBS4). Conversely, Group 2 exhibited late-stage acquisition of a DNA mismatch repair deficiency (MMRd) signature (SBS15), fueling trace subclonal diversification. Clinically, Group 2 demonstrated a significantly lower objective response rate (ORR) to platinum-based regimens (25.0% vs. 81.3%, P=0.02). Furthermore, the Subclonal-rich architecture independently predicted inferior overall survival (OS) [adjusted hazard ratio (adj. HR) =2.93, P=0.02], driven predominantly by limited-stage disease. Cross-cancer analysis validated this histology-dependent, high-heterogeneity adverse pattern in early-stage LUSC but not in LUAD. CONCLUSIONS: This hypothesis-generating study demonstrates that a "Subclonal-rich" architecture, driven by acquired MMRd, identifies high-risk, chemo-resistant SCLC. Our GMM approach suggests that pre-existing heterogeneity may serve as a potential, histology-dependent prognostic marker that warrants prospective validation for tailoring future therapeutic regimens.

Gaussian Mixture Model (GMM)↗
PubMed2005

Empirical bayes microarray ANOVA and grouping cell lines by equal expression levels.

In the exploding field of gene expression techniques such as DNA microarrays, there are still few general probabilistic methods for analysis of variance. Linear models and ANOVA are heavily used tools in many other disciplines of scientific research. The usual F-statistic is unsatisfactory for microarray data, which explore many thousand genes in parallel, with few replicates. We present three potential one-way ANOVA statistics in a parametric statistical framework. The aim is to separate genes that are differently regulated across several treatment conditions from those with equal regulation. The statistics have different features and are evaluated using both real and simulated data. Our statistic B1 generally shows the best performance, and is extended for use in an algorithm that groups cell lines by equal expression levels for each gene. An extension is also outlined for more general ANOVA tests including several factors. The methods presented are implemented in the freely available statistical language R. They are available at http://www.math.uu.se/staff/pages/?uname=ingrid.

Journal Article↗
PubMed2026

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (1) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (2) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈ 25-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is freely available at: github.com/raphael-group/LAML-Pro.

Journal Article↗
PubMed1986

Computer-aided diagnosis of dermatologic disorders.

In conclusion, several computer programs have been developed to aid diagnosis in dermatology. These have used various methods to reach a diagnosis, with some earlier programs using probabilistic techniques and later programs using cognitive models. Several current trends, if they continue, would indicate that computer-assisted diagnosis may play a greater role. The growing number of physicians who own the prerequisite computing resources will probably want diagnostic software to accompany their office management and data base software. The increasing ease of use of diagnostic software, with rapid access and ease of updating, should make these programs more attractive to physicians. Some of the newer peripheral devices such as optical disks and voice input will probably make the software more interesting. Perhaps the most important factor governing the acceptance of computer diagnostic adjuncts is whether the software will provide a useful service to physicians. If these programs can be demonstrated to improve patient care, they may become commonplace in physicians' offices.

Artificial Intelligence↗
PubMedDate not supplied

Using objective and subjective information to develop distributions for probabilistic exposure assessment.

The propagation of variance through complex exposure models has been simplified by simulation software running on desktop computers; however, the appropriate representation of variability in, and uncertainty about, model inputs remains a challenge. The U.S. Environmental Protection Agency (EPA) has indicated that the Monte Carlo simulation approach to exposure assessment is acceptable as long as input distributions are credible, i.e., rooted in fact (EPA, 1992). The use of distributions based on empirical evidence is certainly desirable; however, it is not always possible to obtain measurements for all exposure model inputs under the conditions of interest. Where objective evidence is scarce or unavailable, subjective judgments are necessary. In this work, the application of both statistical and subjective approaches to the development of distributions for exposure model inputs is explored. The consequences of one's approach to distribution development, on the interpretation and applicability of the output, are also considered.

Data Interpretation, Statistical↗
PubMed2004

Working memory and syllogistic reasoning.

The purpose of this study was to examine the relation between working memory span and syllogistic reasoning performance. In addition, performance for the reasoning task was compared to predictions made by mental model theory and the probability heuristics model. According to mental model theory, syllogisms that require the use of more mental models are more difficult. According to the probability heuristics model difficulty is related to the number of probabilistic heuristics that must be applied, or (for invalid syllogisms) inconsistencies between the derived and correct conclusion. The predictions of these theories were examined across two experiments. In general, people with larger working memory capacities reasoned better. Also, the responses made by people with larger capacities were more likely to correspond to the predictions made by both mental model theory and the probability heuristics model. Relations between working memory span and performance were also consistent with both theories.

Adult↗
PubMed1997

Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information.

We demonstrate the applicability of our previously developed Bayesian probabilistic approach for predicting residue solvent accessibility to the problem of predicting secondary structure. Using only single-sequence data, this method achieves a three-state accuracy of 67% over a database of 473 non-homologous proteins. This approach is more amenable to inspection and less likely to overlearn specifics of a dataset than "black box" methods such as neural networks. It is also conceptually simpler and less computationally costly. We also introduce a novel method for representing and incorporating multiple-sequence alignment information within the prediction algorithm, achieving 72% accuracy over a dataset of 304 non-homologous proteins. This is accomplished by creating a statistical model of the evolutionarily derived correlations between patterns of amino acid substitution and local protein structure. This model consists of parameter vectors, termed "substitution schemata," which probabilistically encode the structure-based heterogeneity in the distributions of amino acid substitutions found in alignments of homologous proteins. The model is optimized for structure prediction by maximizing the mutual information between the set of schemata and the database of secondary structures. Unlike "expert heuristic" methods, this approach has been demonstrated to work well over large datasets. Unlike the opaque neural network algorithms, this approach is physicochemically intelligible. Moreover, the model optimization procedure, the formalism for predicting one-dimensional structural features and our previously developed method for tertiary structure recognition all share a common Bayesian probabilistic basis. This consistency starkly contrasts with the hybrid and ad hoc nature of methods that have dominated this field in recent years.

Algorithms↗
PubMed2000

Uncertainty in compartmental models for hazardous materials - a case study.

Performing uncertainty analysis on compartmental models is the main topic of this article. Elements of the methodology developed during a joint CEC/USNRC accident consequence code uncertainty analysis are introduced. The uncertainty is quantified using structured expert judgment. Experts are queried about physically observable quantities. Many code input parameters of the accident consequence codes are not physically observable but are used to predict observable quantities. Therefore, a probabilistic inversion technique was developed which 'transfers' the uncertainty from the physically observable quantities to the code input parameters. The probabilistic inversion technique is illustrated using the compartmental model of systemic retention of Sr in the human body. The article is concluded with a discussion on capturing uncertainty via compartmental models.

Hazardous Substances↗
PubMed2005

Seeing versus doing: two modes of accessing causal knowledge.

The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed ("seeing") or was actively manipulated ("doing"). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency.

Achievement↗
PubMed2006

Low-order conditional independence graphs for inferring genetic networks.

As a powerful tool for analyzing full conditional (in-)dependencies between random variables, graphical models have become increasingly popular to infer genetic networks based on gene expression data. However, full (unconstrained) conditional relationships between random variables can be only estimated accurately if the number of observations is relatively large in comparison to the number of variables, which is usually not fulfilled for high-throughput genomic data. Recently, simplified graphical modeling approaches have been proposed to determine dependencies between gene expression profiles. For sparse graphical models such as genetic networks, it is assumed that the zero- and first-order conditional independencies still reflect reasonably well the full conditional independence structure between variables. Moreover, low-order conditional independencies have the advantage that they can be accurately estimated even when having only a small number of observations. Therefore, using only zero- and first-order conditional dependencies to infer the complete graphical model can be very useful. Here, we analyze the statistical and probabilistic properties of these low-order conditional independence graphs (called 0-1 graphs). We find that for faithful graphical models, the 0-1 graph contains at least all edges of the full conditional independence graph (concentration graph). For simple structures such as Markov trees, the 0-1 graph even coincides with the concentration graph. Furthermore, we present some asymptotic results and we demonstrate in a simulation study that despite their simplicity, 0-1 graphs are generally good estimators of sparse graphical models. Finally, the biological relevance of some applications is summarized.

Algorithms↗
PubMed2000

Rasch models overview.

This overview of Rasch measurement models begins with a conceptualization of our continuous experiences that are often captured as discrete observations. It goes on to discuss the properties that are require of measures if they are to transcend the occasion in which they were collected, and concludes with a discussion the spiral of inferential development. This is followed by a discussion of the mathematical properties of the Rasch family of models that allow the transformation of discrete deterministic counts into continuous probabilistic abstractions on which science is based. The overview concludes with a discussion of six of the family of Rasch models, Binomial Trials, Poisson Counts, Rating Scale, Partial Credit, and Ranks and the types of data for which these models are appropriate.

Binomial Distribution↗
PubMed2006

Knowledge-based risk assessment under uncertainty for species invasion.

Management of invasive species depends on developing prevention and control strategies through comprehensive risk assessment frameworks that need a thorough analysis of exposure to invasive species. However, accurate exposure analysis of invasive species can be a daunting task because of the inherent uncertainty in invasion processes. Risk assessment of invasive species under uncertainty requires potential integration of expert judgment with empirical information, which often can be incomplete, imprecise, and fragmentary. The representation of knowledge in classical risk models depends on the formulation of a precise probabilistic value or well-defined joint distribution of unknown parameters. However, expert knowledge and judgments are often represented in value-laden terms or preference-ordered criteria. We offer a novel approach to risk assessment by using a dominance-based rough set approach to account for preference order in the domains of attributes in the set of risk classes. The model is illustrated with an example showing how a knowledge-centric risk model can be integrated with the dominance-based principle of rough set to derive minimal covering "if ... , then...," decision rules to reason over a set of possible invasion scenarios. The inconsistency and ambiguity in the data set is modeled using the rough set concept of boundary region adjoining lower and upper approximation of risk classes. Finally, we present an extension of rough set to evidence a theoretic interpretation of risk measures of invasive species in a spatial context. In this approach, the multispecies interactions in an invasion risk are approximated with imprecise probability measures through a combination of spatial neighborhood information of risk estimation in terms of belief and plausibility.

Animals↗
PubMed2004

[An epidemiological index to assess the nutritional status of children based in a polynomial model of values from Z punctuation for the age in Mexico].

A nutritional status index was built by modeling the mathematical function of the mean Z scores of weight for age, from 60,079 children under five years of age, selected in a probabilistic fashion from the Mexican population. The most precise mathematical model was a fifth degree polynomial. The correlation coefficient was between .937<R<.999. The proposed index is the integral of the polynomial function, which represents the nutritional gap between the observed and the reference population. This model is used to analyze the characteristics of the different study populations. The index shows an improvement of -39.6 to -16.8 at the national level, between the years 1988 and 1999. This improvement is greater in urban (-36-4 to -8.4) than in rural areas (-48.8 in 1989, to -37.7 in 1999). The indigenous rural population of the country showed the highest levels of malnutrition (-54.4), as compared to the non-indigenous rural population (-37.1). In Mexico, City, the index was -5.9 in 1995, which represents an average of extreme nutritional values: -17.3 in the lower socioeconomic strata and 18.0 in the higher strata, the latter suggesting the presence of childhood obesity. This index is useful to estimate the epidemiological burden and the characteristics of malnutrition at early ages, as well as to assess the impact of interventions, without being altered by common biases related to the utilization of malnutrition prevalence values.

Body Weight↗
PubMed1972

Inferences about membrane properties from electrical noise measurements.

Four sources of electrical noise in biological membranes, each with a different physical basis, are discussed; the analysis of each type of noise potentially yields a different sort of information about membrane properties. (a) From the thermal noise spectrum, the passive membrane impedance may be obtained, so that thermal noise measurements are essentially equivalent to the type of since wave analysis carried out by Cole and Curtis. (b) If adequately high frequency measurements could be made, the shot noise spectrum should give information about the average motion of a single ion within the membrane. (c) The number of charge carriers and single ion mobilities within the membrane can possibly be inferred from measurements of noise with a 1/f spectrum. Available data indicate, for example, that increases in axon membrane conductance are not achieved by modulations in the mobility of ions within the membrane. (d) Fluctuations arising from the mechanisms normally responsible for membrane conductance changes can produce a type of electrical noise. Analysis of such conductance fluctuations provides a way to assess the validity of various microscopic models for the behavior of individual channels. Two different probabilistic interpretations of the Hodgkin-Huxley equations are investigated here and shown to yield different predictions about the spectrum of conductance fluctuations; thus, appropriate noise measurements may serve to eliminate certain classes of microscopic models for membrane conductance changes. Further, it is shown how the analysis of conductance fluctuations can, in some circumstances, provide an estimate of the conductance of a single channel.

Analysis of Variance↗