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PubMed2003

[Use of multilayer perception artificial neutral networks for the prediction of the probability of malignancy in adnexal tumors].

BACKGROUND: Advanced statistical methods are currently are more often used in the prediction of ovarian malignancy when adnexal tumor is detected. These methods include logistic regression analysis and artificial neural networks (ANN's), i.e. computer programs which are capable of learning from presented data and further predict various events, such as clinical diagnosis or outcome of a given treatment. MATERIALS AND METHODS: We have analyzed data of 307 women with adnexal tumors who were operated in the Ist Dept. of Gynecology, Medical University in Lublin between 2000-2002. Following clinical and sonographic variables were included: age, menopausal status, serum CA-125, bilaterality, tumor size and volume, papillary projections, septa, solid parts presence, Doppler blood flow indices (PI, RI, Vmax), and subjective-color Doppler score. A multiple layer perceptron (MLP) neural network with 13 input variables, 11 hidden neurons and one output variable was constructed to assess probability of malignancy in each women (Statistica v. 6.0 for Windows, Statsoft, USA). Sensitivity, specificity and accuracy of the model were calculated. Receiver-Operating Characteristics curves were generated and corresponding Areas Under ROC Curves (AUROC's) for all diagnostic tests were compared. RESULTS: Final histologic examination revealed 228 (74.3%) benign tumors and 79 (25.7%) malignant masses including 21 women with FIGO stage I ovarian cancer. With a 75% cut-off probability of malignancy level the sensitivity and specificity of the best network in the testing set was 96.7% and 100%, respectively. In the validation set the corresponding values of sensitivity and specificity were 82.3% and 97.5%. The highest of all used tests AUROC equal to 0.9749 was found for the ANN predictive model. CONCLUSIONS: ANN may help in the extraction of the most useful predictive clinical and ultrasound data. The sensitivity and specificity of the ANN's generated model were higher than currently used single clinical and diagnostic tests. However, a prospective testing in a new, much larger group of women with adnexal tumors is essential for the clinical usefulness of the proposed statistical model.

Adult↗
PubMed2000

A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder risk.

A new and improved method to feedforward neural network (FNN) development for application to data classification problems, such as the prediction of levels of low-back disorder (LBD) risk associated with industrial jobs, is presented. Background on FNN development for data classification is provided along with discussions of previous research and neighborhood (local) solution search methods for hard combinatorial problems. An analytical study is presented which compared prediction accuracy of a FNN based on an error-back propagation (EBP) algorithm with the accuracy of a FNN developed by considering results of local solution search (simulated annealing) for classifying industrial jobs as posing low or high risk for LBDs. The comparison demonstrated superior performance of the FNN generated using the new method. The architecture of this FNN included fewer input (predictor) variables and hidden neurons than the FNN developed based on the EBP algorithm. Independent variable selection methods and the phenomenon of 'overfitting' in FNN (and statistical model) generation for data classification are discussed. The results are supportive of the use of the new approach to FNN development for applications to musculoskeletal disorders and risk forecasting in other domains.

Back Injuries↗
PubMed1984

The effect of newly induced mutations on the fitness of genotypes and populations of yeast (Saccharomyces cerevisiae).

This paper analyses the fate of artificially induced mutations and their importance to the fitness of populations of the yeast, Saccharomyces cerevisiae, an increasingly important model organism in population genetics. Diploid strains, treated with UV and EMS, were cultured asexually for approximately 540 generations and under conditions where the asexual growth was interrupted by a sexual phase. Growth rates of 100 randomly sampled diploid clones were estimated at the beginning and at the end of the experiment. After the induction of sporulation the growth rates of 100 randomly sampled spores were measured. UV and EMS treatment decreases the average growth rate of the clones significantly but increases the variability in comparison to the untreated control. After selection over approximately 540 generations, variability in growth rates was reduced to that of the untreated control. No increase in mean population fitness was observed. However, the results show that after selection there still exists a large amount of hidden genetic variability in the populations which is revealed when the clones are cultivated in environments other than those in which selection took place. A sexual phase increased the reduction of the induced variability.

Ethyl Methanesulfonate↗
PubMed2006

Artificial neural network versus subjective scoring in predicting mortality in trauma patients.

OBJECTIVE: Current methods of trauma outcome prediction rely on clinical knowledge and experience. This makes the system a subjective score, because of intra-rater variability. This project aims to develop a neural network for predicting survival of trauma patients using standard, measured, physiological variables, and compare its predictive power with that obtained from current trauma scores. METHODS: The project uses 7688 patients admitted to the Swedish Medical Center, Colorado, U.S.A. between the years 2000-2003 inclusive. Neural Network software was used for data analysis to determine the best network design on which to base the model to be tested. The model is created using a minimum number of variables to produce an effective outcome predicting score. Initial variables were based on the current variables used in calculating the Revised Trauma Score, replacing the Glasgow Coma Scale (GCS) with a modified motor component of the GCS. Additional variables are added to the model until a suitable model is achieved. RESULTS: The best model used Multi-Layer Perceptrons, with 8 input variables, 5 hidden neurons and 1 output. It was trained on 5881 cases and tested independently on 1807 cases. The model was able to accurately predict 91% patient mortality. CONCLUSIONS: An ANN developed using pre-hospital physiological variables without using subjective scores resulted in good mortality prediction when applied to a test set. Its performance was too sensitive and requires refinement.

Adolescent↗
PubMed1980

Assessment of variability within electromorphs of alcohol dehydrogenase in Drosophila melanogaster.

Ninety-six isochromosomal lines of Drosophila melanogaster from a natural population were screened electrophoretically for unusual mobility variants at the alcohol dehydrogenase locus, using a total of eight conditions of acrylamide electrophoresis. No additional mobility variation was found among the 50 "slow" and 46 "fast" mobility lines beyond that detected by standard methods of electrophoresis. However, two thermostability variants recovered by R. MILKMAN from a natural population, whose electrophoretic mobilities were previously thought to be distinguishable from those of "standard" alleles, are distinguishable from the standard electromorphs by these procedures. These results suggest that the Adh locus, although polymorphic, does not harbor substantial amounts of "hidden" allelic variability. This study also reports the appearance of substantial mobility variation among isogenic lines that can be induced under specific conditions of sample preparation involving the pretreatment of samples with NAD and acetone. However, genetic analysis demonstrates that this variability cannot be attributed to allelic differences at the structural locus, but instead appears to be dependent upon the concentration of the enzyme in a sample. These results are discussed in relation to the distribution of allelic variation at other enzyme loci.

Alcohol Oxidoreductases↗
PubMed1993

A hidden region in the third variable domain of HIV-1 IIIB gp120 identified by a monoclonal antibody.

The third variable domain (V3 domain) of HIV-1 gp120 is involved in virus neutralization by antibody, in determination of cell tropism, and in syncytium-inducing/non-syncytium-inducing capacity. Antibodies are highly specific tools to delineate the role of different V3 amino acid sequences in these processes, and to dissect events occurring during synthesis of gp120/160, gp120-CD4 interaction, cellular infection, and syncytium formation. We describe here an IgG1 murine monoclonal antibody (MAb), coded IIIB-V3-01, that was raised with a synthetic peptide (FVTIGKIGNMRQAHC) derived from the carboxy-terminal flank of the HIV-1 IIIB V3 domain. The binding site of this antibody was mapped to the sequence IGKIGNMRQ, using Pepscan analysis. In ELISA, this antibody binds to E. coli-derived gp120 from HIV-1 IIIB, which is denatured and not glycosylated. The antibody showed no neutralizing activity against HIV-1 IIIB, MN, SF2, or RF in a virus neutralization assay and in a syncytium formation inhibition assay. In addition, this antibody did not react with gp120 expressed on the surface of IIIB-infected MOLT-3 cells in FACS analysis. To assess whether the epitope defined by MAb IIIB-V3-01 is hidden on native gp120, reactivity of the antibody with SDS-DTT-denatured or DTT-denatured glycosylated gp120 (CHO cell produced) was tested. Both these treatments exposed the epitope for binding. From these data we conclude that the epitope defined by MAB IIIB-V3-01 is hidden on glycosylated recombinant gp120, and is not accessible on gp120 expressed on the membrane of HIV-1, IIIB-infected cells.(ABSTRACT TRUNCATED AT 250 WORDS)

Amino Acid Sequence↗
PubMed2003

Methodology for the scientific evaluation of complementary and alternative medicine.

It has been suggested that CAM research should establish efficacy before examining mechanism. This paper shows that the efficacy-mechanism distinction is a false one, as any test of efficacy assumes a particular mechanism and is a test of the theory underlying that mechanism. The term RCT is currently used in medicine for two different sorts of study. The randomised controlled trial (RConT) requires an experimental manipulation that can 'control' for the mechanism under consideration, and therefore tests the efficacy of that mechanism. The randomised comparison trial (RComT) requires only an experimental manipulation creating a therapeutically relevant comparison, and tests the effectiveness of that therapy. The ability to achieve control coupled with an assumed implausibility of hidden moderating variables characterises drug therapy and some CAM therapies where the RConT can be used. However, other CAM researchers assume a variety of holistic mechanisms, where control is necessarily poor and the hypothesis of complex interactions suggest the existence of multiple moderators. In these cases other experimental (e.g. RComT), quasi-experimental or non-experimental designs are needed to evaluate therapeutic practice. Researchers from both communities should make explicit their underlying assumptions and the mechanisms they seek to evaluate when carrying out empirical studies. Research design needs to be appropriate for the mechanism under test.

Complementary Therapies↗
PubMed2004

Response experiments for nonlinear systems with application to reaction kinetics and genetics.

A unified type of response experiment is suggested for complex systems made up of individual species (atoms, molecules, quasi-particles, biological organisms, etc.). We make the following assumptions: (i) some of the species may exist in two forms, labeled and unlabeled, respectively; (ii) the kinetic and transport properties of the labeled and unlabeled species are the same, respectively (neutrality assumption); (iii) the experiment preserves the total input and output fluxes; only the fractions of the labeled compounds in the input and output fluxes are varied. Under these circumstances a linear integral superposition law connects the fractions of labeled species in the input and output fluxes. This linear superposition law is valid for homogeneous and inhomogeneous systems and for systems with intrinsic (hidden) state variables; it arises from the neutrality condition and holds even though the underlying dynamics of the process may be highly nonlinear. Because this response law does not involve the linearization of the evolution equations it has great potential for the analysis of complex physical, chemical, and biological systems. We compare our approach with the linearization techniques used in biochemistry and genetics. We consider a simple reaction network involving replication, transformation, and disappearance steps and study the influence of experimental (measurement) and linearization errors on the evaluated values of rate coefficients. We show that the method involving the linearization of the kinetic equations leads to unpredictable results; because of the interference between measurement and linearization errors, either error compensation or error amplification occurs. Although our approach does not eliminate the effects of measurement errors, it leads to more consistent results. For a broad range of input fractions no error amplification or compensation occurs, and the error range for the rate coefficients is about the same as the error range of the measurements.

Genetics, Medical↗
PubMed2025

Simultaneous inference for generalized linear models with unmeasured confounders.

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It begins by disentangling marginal and uncorrelated confounding effects to recover the latent coefficients. Subsequently, latent factors and primary effects are jointly estimated through lasso-type optimization. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish the identification conditions of various effects and non-asymptotic error bounds. We show effective Type-I error control of asymptotic-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model.

Hidden variables↗
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↗
PubMedDate not supplied

Chromosomal analysis of D. melanogaster long-term selected lines.

Crosses and chromosomal substitutions among five selected lines of Drosophila melanogaster were carried out. These lines came from the same natural population after long-term selection for increasing dorsocentral bristle number. Two of them (Ac-27S and Ac-27P) show a bristle number around 16, and the other three (S-27S, S-27P, and N-21) present very extreme phenotypes, between 35 and 40 bristles. Selected genes were located on the three major chromosomes. Recessivity and synergistic interactions among the selected chromosomes account for the existence of hidden genetic variability, which can be released by selection, for a trait that scarcely shows phenotypic variability in populations. Genes responsible for selection response in lines Ac-27S and Ac-27P are present in all the selected lines, while lines S-27S, S-27P, and N-21 have accumulated additional increasing alleles common to the three of them. Long-term response and the extreme phenotypes achieved by these three lines point to variation that arose de novo during the selection process. However, this idea disagrees with the low genetic variability found between them. All these facts are consistent with the idea that variability originated in the course of selection by a low-probability nonrandom mechanism such as the occurrence of rare recombination events.

Animals↗
PubMed1994

The effect of the partial pressure of oxygen on blood glucose concentration examined using glucose oxidase with ferricyan ion.

Glucose oxidase with ferricyan ion (GOD-F) is widely applied in clinical settings as a glucose sensor. However, blood oxygen concentration affects this blood glucose value because oxygen, at increased concentrations, consumes blood glucose, which cannot then be measured by this sensor. We investigated the effect of PO2 on blood glucose concentration in 48 patients who were breathing high concentrations of oxygen. Arterial and pulmonary arterial blood glucose values were analyzed using the GOD-F method and, as a control, the hexokinase method. The respective PO2 values were also measured. The blood glucose concentrations measured by the GOD-F method show a significant linear relation with that measured by the hexokinase method in both arterial (y = -24.4 + 1.01x, r = 0.99) and pulmonary arterial blood (y = -3.4 + 1.01x, r = 0.96). The difference of intercepts is statistically significant, but because of the relatively large limits of agreement indicating any hidden extraneous variabilities, the error of the GOD-F method could not be assessed just by the difference. The equation defining the effect of PO2 on the percent change between blood glucose measured by the GOD-F method and that measured by the hexokinase method is -19.8/(1 + 203900/PO2(2.68) (r = 0.62). This formula generally follows our measured materials and introduces the relationship among blood glucose value, PO2, and the error of the GOD-F method. We hesitate to suggest that the arterial blood glucose concentration when measured by the GOD-F method could be underestimated by as much as 20% in patients with high arterial oxygen pressure.(ABSTRACT TRUNCATED AT 250 WORDS)

Anesthesiology↗
PubMed1996

Artificial neural network prediction of ascites in broilers.

An artificial neural network was trained to predict the presence or absence of ascites in broiler chickens. The neural network was a three-layer back-propagation neural network with an input layer of 15 neurons (defining 15 physiological variables), a hidden layer of 16 neurons, and an output layer of 2 neurons (the presence or absence of ascites). Male by-products of a breeder pullet line were brooded at 32 and 30 C during Weeks 1 and 2, respectively. The training set for the neural network consisted of data from birds subjected to cool temperatures (18 C) to induce ascites. After training, the predictive ability of the neural network was verified with two new data sets. The second data set was from birds subjected to cool temperatures (18 C). The third data set was from birds subjected to clamping of the pulmonary artery to simulate the physiological processes involved in ascites (the temperature was 24 C). A comparison was made between laboratory diagnostic results and the neural network predicted ascites incidence. The neural network accurately identified the presence or absence of ascites in the first (training) set. Two false positives and one false positive were identified in the second and third verification sets, respectively. The birds identified as false positives were determined to be in the developmental stages of ascites before the occurrence of fluid accumulation. Artificial neural networks were found to effectively identify broilers with and without ascites.

Animals↗
PubMed2006

Investigating indicators and determinants of asthma in young adults.

BACKGROUND: In epidemiological studies on asthma determinants an extreme variability in results exists, probably due to different criteria utilised for defining of an asthma 'case' and for measuring determinants. We aimed to assess multiple indicators and multiple determinants of asthma in young adults by applying latent variable mixture models (LVMMs), a novel statistical modelling with hidden (or latent) variables. METHODS: We consider the pooled data of 1103 subjects (aged 20-44 years) from the three Italian centres of the European Community Respiratory Health Survey (ECRHS 1), a standardised database. Underlying multiple asthma indicators (clinicians' diagnosis, self-report symptoms, respiratory trials) both a latent two-class of asthma syndrome, and three continuous latent variables (severity of diagnosed asthma, severity of asthma symptoms, and severity of respiratory function) were investigated. RESULTS: Family history was the more relevant predictor of the two-class of asthma syndrome with a risk increase of about 60% per 1 relative with early life events (OR = 1.60, 95% CI: 1.30-1.97). Smoking, active and passive, are predictive for the indicators of severity of asthma symptoms. On average the risk increase of about 10% (OR = 1.10, 95%CI: 1.01-1.20) either per 1 source point of environmental tobacco smoke (ETS) or per 1 packet a day per 10 years. While, the risk of the indicators of both severity of asthma symptoms (OR = 1.59, 95%CI: 1.23-2.06) and severity of respiratory function (OR = 1.37, 95%CI: 1.03-1.82) increase in women compared to men, the risk of the indicators of severity of diagnosed asthma (OR = 0.57, 95%CI: 0.35-0.91) decreases. CONCLUSIONS: Considering latent modelling perspective for formulating plausible hypotheses in asthma research, this study highlighted that the host (genetic) component measured as number of relatives with life-events of asthma and/or allergies seems to be the primary determinants of overall observed asthma indicators summarised by hidden two-class of asthma syndrome. Furthermore, a secondary (or trigger) role of smoking on the continuous latent variable of severity of asthma symptoms, and a gender reversal effect were suggested.

Adult↗
PubMed1999

A characterization of HRV's nonlinear hidden dynamics by means of Markov models.

A study of the 24-h heart rate variability's (HRV) hidden dynamic is performed hour by hour, in order to investigate the evolution of the nonlinear structure of the underlying nervous system. A hierarchy of null hypotheses of nonlinear Markov models with increasing order n is tested against the hidden dynamic of the HRV time series. The minimum accepted Markov order supplies information about the nonlinearity of the HRV's hidden dynamic and consequently of the underlying nervous system. The Markov model with minimum order is detected for each hour of the RR time series extracted from seven 24-h electrocardiogram records of patients in different pathophysiological conditions, some including ventricular tachycardia episodes. Heart rate, pNN30, and LF/HF index plots are reported to serve as a reference for the description of the patient's cardiovascular frame during each examined hour. The minimum Markov order shows to be a promising index for quantifying the average nonlinearity of the autonomic nervous system's activity.

Aged↗
PubMed1990

The detection of hidden visual loss in optic neuropathy: VISTECH test at variable illuminations.

The sensitivity of the VISTECH chart in the detection of hidden visual loss is debated. We tried to evaluate the diagnostic value of the test by using different illumination levels. Twelve MS-patients with normal acuity but a pathological VEP were examined at 9 different illuminations. We did not identify more abnormalities among patients, using VISTECH test at other illumination levels than the one recommended by the manufacturer.

Adult↗
PubMed2004

Biomagnetic source detection by maximum entropy and graphical models.

This article presents a new approach for detecting active sources in the cortex from magnetic field measurements on the scalp in magnetoencephalography (MEG). The solution of this ill-posed inverse problem is addressed within the framework of maximum entropy on the mean (MEM) principle introduced by Clarke and Janday. The main ingredient of this regularization technique is a reference probability measure on the random variables of interest. These variables are the intensity of current sources distributed on the cortical surface for which this measure encompasses all available prior information that could help to regularize the inverse problem. This measure introduces hidden Markov random variables associated with the activation state of predefined cortical regions. MEM approach is applied within this particular probabilistic framework and simulations show that the present methodology leads to a practical detection of cerebral activity from MEG data.

Algorithms↗
PubMed1996

Sequence heterogeneities among 16S ribosomal RNA sequences, and their effect on phylogenetic analyses at the species level.

We have analyzed what phylogenetic signal can be derived by small subunit rRNA comparison for bacteria of different but closely related genera (enterobacteria) and for different species or strains within a single genus (Escherichia or Salmonella), and finally how similar are the ribosomal operons within a single organism (Escherichia coli). These sequences have been analyzed by neighbor-joining, maximum likelihood, and parsimony. The robustness of each topology was assessed by bootstrap. Sequences were obtained for the seven rrn operons of E. coli strain PK3. These data demonstrated differences located in three highly variable domains. Their nature and localization suggest that since the divergence of E. coli and Salmonella typhimurium, most point mutations that occurred within each gene have been propagated among the gene family by conversions involving short domains, and that homogenization by conversions may not have affected the entire sequence of each gene. We show that the differences that exist between the different operons are ignored when sequences are obtained either after cloning of a single operon or directly from polymerase chain reaction (PCR) products. Direct sequencing of PCR products produces a mean sequence in which mutations present in the most variable domains become hidden. Cloning a single operon results in a sequence that differs from that of the other operons and of the mean sequence by several point mutations. For identification of unknown bacteria at the species level or below, a mean sequence or the sequence of a single nonidentified operon should therefore be avoided. Taking into account the seven operons and therefore mutations that accumulate in the most variable domains would perhaps increase tree resolution. However, if gene conversions that homogenize the rRNA multigene family are rare events, some nodes in phylogenetic trees will reflect these recombination events and these trees may therefore be gene trees rather than organismal trees.

Bacteria↗