Biomedical subjects

Mats G Gustafsson

Publications and source records attributed to Mats G Gustafsson.

11 recordsLinked to original sources

PubMed2006

Molecular markers for discrimination of benign and malignant follicular thyroid tumors.

OBJECTIVE: To identify molecular markers useful for the diagnostic discrimination of benign and malignant follicular thyroid tumors. METHODS: A panel of thyroid tumors was characterized with expression profiling using cDNA microarrays. A robust algorithm for gene selection was developed to identify molecular markers useful for the classification of heterogeneous tumor classes. The study included tumor tissue specimens from 10 patients with benign follicular adenomas and from 10 with malignant tumors. The malignant tumors mainly consisted of clinically relevant minimally invasive follicular carcinomas. The mRNA expression level of a candidate gene, FHL1, was evaluated in an independent series of 61 tumors. RESULTS: 22 gene expression markers were identified as differentially expressed. Several of the identified genes, for example DIO1, CITED1, CA12 and FN1, have previously been observed as differentially expressed in various thyroid tumors. FHL1 was significantly underexpressed in carcinomas compared to adenomas in the independent panel of tumors. The results indicate that a small number of genes can be useful to distinguish follicular adenomas from follicular carcinomas. CONCLUSIONS: Our findings clearly corroborate previous studies and identify novel candidate molecular markers. These genes have the potential for molecular classification of follicular thyroid tumors and for providing improved understanding of the molecular mechanisms involved in thyroid malignancies.

Adenocarcinoma, Follicular↗

PubMed2006

Improved variance estimation of classification performance via reduction of bias caused by small sample size.

BACKGROUND: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm that the classifier is robust with good generalization performance to new examples, or at least that it performs better than random guessing. A suggested alternative is to obtain a confidence interval of the error rate using repeated design and test sets selected from available examples. However, it is known that even in the ideal situation of repeated designs and tests with completely novel samples in each cycle, a small test set size leads to a large bias in the estimate of the true variance between design sets. Therefore different methods for small sample performance estimation such as a recently proposed procedure called Repeated Random Sampling (RSS) is also expected to result in heavily biased estimates, which in turn translates into biased confidence intervals. Here we explore such biases and develop a refined algorithm called Repeated Independent Design and Test (RIDT). RESULTS: Our simulations reveal that repeated designs and tests based on resampling in a fixed bag of samples yield a biased variance estimate. We also demonstrate that it is possible to obtain an improved variance estimate by means of a procedure that explicitly models how this bias depends on the number of samples used for testing. For the special case of repeated designs and tests using new samples for each design and test, we present an exact analytical expression for how the expected value of the bias decreases with the size of the test set. CONCLUSION: We show that via modeling and subsequent reduction of the small sample bias, it is possible to obtain an improved estimate of the variance of classifier performance between design sets. However, the uncertainty of the variance estimate is large in the simulations performed indicating that the method in its present form cannot be directly applied to small data sets.

Analysis of Variance↗

PubMed2006

Screening of an annotated compound library for drug activity in a resistant myeloma cell line.

PURPOSE: Resistance to anticancer drugs is a major problem in chemotherapy. In order to identify drugs with selective cytotoxic activity in drug-resistant cancer cells, the annotated compound library LOPAC1280, containing compounds from 56 pharmacological classes, was screened in the myeloma cell line RPMI 8226 and its doxorubicin-resistant subline 8226/Dox40. METHODS: Cell survival was measured by the Fluorometric Microculture Cytotoxicity Assay. RESULTS: Selective cytotoxic activity in 8226/Dox40 was obtained for 33 compounds, with the most pronounced difference observed for the glucocorticoids. A microarray analysis of the cells showed a difference in mRNA-expression for the glucocorticoid receptor suggesting potential mechanisms for the difference in glucocorticoid sensitivity. In the presence of the glucocorticoid-receptor antagonist RU486, the sensitivity to the glucocorticoids was reduced and a similar effect level in RPMI 8226 and 8226/Dox40 was achieved. CONCLUSION: In conclusion, screening of mechanistically annotated compounds on drug-resistant cancer cells can identify compounds with selective activity and provide a basis for the development of novel treatments of drug-resistant malignancies.

Antineoplastic Agents↗

PubMed2006

Bayesian detection of periodic mRNA time profiles without use of training examples.

BACKGROUND: Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time. RESULTS: A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented. CONCLUSION: In comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.

Algorithms↗

PubMed2006

Phenotype-based screening of mechanistically annotated compounds in combination with gene expression and pathway analysis identifies candidate drug targets in a human squamous carcinoma cell model.

The squamous cell carcinoma HeLa cell line and an epithelial cell line hTERT-RPE with a nonmalignant phenotype were interrogated for HeLa cell selectivity in response to 1267 annotated compounds representing 56 pharmacological classes. Selective cytotoxic activity was observed for 14 of these compounds dominated by cyclic adenosine monophosphate (cAMP) selective phosphodiesterase (PDE) inhibitors, which tended to span a representation of the chemical descriptor space of the library. The PDE inhibitors induced delayed cell death with features compatible with classical apoptosis. The PDE inhibitors were largely inactive when tested against a cell line panel consisting of hematological and nonsquamous epithelial phenotypes. In a genome-wide DNA microarray analysis, PDE3A and PDE2A were found to be significantly increased in HeLa cells compared to the other cell lines. The pathway analysis software PathwayAssist was subsequently used to extract a list of proteins and small molecules retrieved from Medline abstracts associated with the hit compounds. The resulting list consisted of major parts of the cAMP-protein kinase A pathway linking to ERK, P38, and AKT. This molecular network may provide a basis for further exploitation of novel candidate targets for the treatment of squamous cell carcinoma.

Carcinoma, Squamous Cell↗

PubMed2005

External cross-validation for unbiased evaluation of protein family detectors: application to allergens.

Key issues in protein science and computational biology are design and evaluation of algorithms aimed at detection of proteins that belong to a specific family, as defined by structural, evolutionary, or functional criteria. In this context, several validation techniques are often used to compare different parameter settings of the detector, and to subsequently select the setting that yields the smallest error rate estimate. A frequently overlooked problem associated with this approach is that this smallest error rate estimate may have a large optimistic bias. Based on computer simulations, we show that a detector's error rate estimate can be overly optimistic and propose a method to obtain unbiased performance estimates of a detector design procedure. The method is founded on an external 10-fold cross-validation (CV) loop that embeds an internal validation procedure used for parameter selection in detector design. The designed detector generated in each of the 10 iterations are evaluated on held-out examples exclusively available in the external CV iterations. Notably, the average of these 10 performance estimates is not associated with a final detector, but rather with the average performance of the design procedure used. We apply the external CV loop to the particular problem of detecting potentially allergenic proteins, using a previously reported design procedure. Unbiased performance estimates of the allergen detector design procedure are presented together with information about which algorithms and parameter settings that are most frequently selected.

Allergens↗

PubMed2005

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling.

BACKGROUND: Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis. RESULTS: A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small. CONCLUSION: The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.

Algorithms↗

PubMed2004

Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins.

MOTIVATION: Identification of potentially allergenic proteins is needed for the safety assessment of genetically modified foods, certain pharmaceuticals and various other products on the consumer market. Current methods in bioinformatic allergology exploit common features among allergens for the detection of amino acid sequences of potentially allergenic proteins. Features for identification still unexplored include the motifs occurring commonly in allergens, but rarely in ordinary proteins. In this paper, we present an algorithm for the identification of such motifs with the purpose of biocomputational detection of amino acid sequences of potential allergens. RESULTS: Identification of allergen-representative peptides (ARPs) with low or no occurrence in proteins lacking allergenic properties is the essential component of our new method, designated DASARP (Detection based on Automated Selection of Allergen-Representative Peptide). This approach consistently outperforms the criterion based on identical peptide match for predicting allergenicity recommended by ILSI/IFBC and FAO/WHO and shows results comparable to the alignment-based criterion as outlined by FAO/WHO. AVAILABILITY: The detection software and the ARP set needed for the analysis of a query protein reported here are properties of the Swedish National Food Agency and are available upon request. The protein sequence sets used in this work are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp. Allergenicity assessment for specific protein sequences of interest is also possible via ulfh@slv.se

Algorithms↗

PubMed2003

Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR.

This work shows that quantitative multivariate modeling is an emerging possibility for unraveling protein-protein interactions using a combination of designed mutations with sequence and structure information. Using this approach, it is possible to stereochemically determine which residue properties contribute most to the interaction. This is illustrated by results from modeling of the interaction of the wild-type and 17 single and double mutants of a camel antibody specific for lysozyme. Linear multivariate models describing association and dissociation rates as well as affinity were developed. Sequence information in the form of amino acid property scales was combined with 3D structure information (obtained using molecular mechanics calculations) in the form of coordinates of the alpha-carbons and the center of the side chains. The results show that in addition to the amino acid properties of the mutated residues 101 and 105, the dissociation rate is controlled by the side-chain coordinate of residue 105, whereas the association is determined by the coordinates of residues 99, 100, 105 (side chain), 111, and 112. The great difference between the models for association and dissociation rates illustrates that the event of molecular recognition and the property of binding stability rely on different physical processes.

Amino Acid Sequence↗

PubMed2003

Optimal detection of crack echo families in elastic solids.

Optimal detection of a striplike crack residing in an isotropic elastic solid with coarse microstructure by means of ultrasonic nondestructive evaluation (NDE) is considered. A physics-based approach to derive an optimal detector, which achieves the theoretical limitations constrained by the underlying physics, is presented. State-of-the-art physical models of crack echoes and of stochastic backscattering from the material structure in elastic solids are introduced and unified with the theory of optimal detection to yield a practically useful nonlinear filter bank implementation of the optimal detector. Monte Carlo simulations of the detection performance for the special case of a striplike crack with uncertain angular orientation are presented in the form of receiver operating characteristics (ROCs). These new results represent the physical limitations for detecting a crack under the stated conditions and serve as performance bounds to which other detectors should be compared. A physics-based generalized likelihood ratio (GLR) detector, which relies on the same nonlinear filter bank as the optimal detector, is also presented for the special case of a striplike crack. A comparison between the optimal and the GLR detectors shows that the GLR detector only slightly reduces the performance.

Journal Article↗

PubMedDate not supplied

Independent component analysis yields chemically interpretable latent variables in multivariate regression.

This work shows that independent component analysis (ICA) can be used to obtain statistically independent and, therefore, chemically interpretable latent variables (LVs) in multivariate regression. Two novel algorithms based on ICA are introduced and compared with two classical methods on simulated data: principal component regression and partial least-squares regression. All methods compared yield accurate predictions, but only those based on ICA yield LVs that are chemically interpretable. Practical limitations of ICA-based regression with respect to the underlying assumptions, sample size, and measurement noise are discussed and illustrated by means of simulations.

Journal Article↗