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PubMedDate not supplied

Development of proteomic patterns for detecting lung cancer.

Lung cancer is at present the number one cause of cancer death and no biomarker is available to detect early lung cancer in serum samples so far. The objective of this study is to find specific biomarkers for detection of lung cancer using Surface Enhanced Laser Desorption/Ionization (SELDI) technology. In this study, serum samples from 30 lung cancer patients and 51 age-and sex-matched healthy were analyzed by SELDI based ProteinChip reader, PBSII-C. The spectra were generated on WCX2 chips and protein peaks clustering and classification analyses were performed utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. Three protein peaks were automatically chosen for the system training and the development of a decision classification tree. The constructed model was then used to test an independent set of masked serum samples from 15 lung cancer patients and 31 healthy individuals. The analysis yielded a sensitivity of 93.3%, and a specificity of 96.7%. These results suggest that the serum is a capable resource for detection of specific lung cancer biomarkers. SELDI technique combined with an artificial intelligence classification algorithm can both facilitate the discovery of better biomarkers for lung cancer and provide a useful tool for molecular diagnosis in future.

Adenocarcinoma↗
PubMed2006

Biomarkers that discriminate multiple myeloma patients with or without skeletal involvement detected using SELDI-TOF mass spectrometry and statistical and machine learning tools.

Multiple Myeloma (MM) is a severely debilitating neoplastic disease of B cell origin, with the primary source of morbidity and mortality associated with unrestrained bone destruction. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to screen for potential biomarkers indicative of skeletal involvement in patients with MM. Serum samples from 48 MM patients, 24 with more than three bone lesions and 24 with no evidence of bone lesions were fractionated and analyzed in duplicate using copper ion loaded immobilized metal affinity SELDI chip arrays. The spectra obtained were compiled, normalized, and mass peaks with mass-to-charge ratios (m/z) between 2000 and 20,000 Da identified. Peak information from all fractions was combined together and analyzed using univariate statistics, as well as a linear, partial least squares discriminant analysis (PLS-DA), and a non-linear, random forest (RF), classification algorithm. The PLS-DA model resulted in prediction accuracy between 96-100%, while the RF model was able to achieve a specificity and sensitivity of 87.5% each. Both models as well as multiple comparison adjusted univariate analysis identified a set of four peaks that were the most discriminating between the two groups of patients and hold promise as potential biomarkers for future diagnostic and/or therapeutic purposes.

Adult↗
PubMed2006

Prognostic DNA methylation biomarkers in ovarian cancer.

PURPOSE: Aberrant DNA methylation, now recognized as a contributing factor to neoplasia, often shows definitive gene/sequence preferences unique to specific cancer types. Correspondingly, distinct combinations of methylated loci can function as biomarkers for numerous clinical correlates of ovarian and other cancers. EXPERIMENTAL DESIGN: We used a microarray approach to identify methylated loci prognostic for reduced progression-free survival (PFS) in advanced ovarian cancer patients. Two data set classification algorithms, Significance Analysis of Microarray and Prediction Analysis of Microarray, successfully identified 220 candidate PFS-discriminatory methylated loci. Of those, 112 were found capable of predicting PFS with 95% accuracy, by Prediction Analysis of Microarray, using an independent set of 40 advanced ovarian tumors (from 20 short-PFS and 20 long-PFS patients, respectively). Additionally, we showed the use of these predictive loci using two bioinformatics machine-learning algorithms, Support Vector Machine and Multilayer Perceptron. CONCLUSION: In this report, we show that highly prognostic DNA methylation biomarkers can be successfully identified and characterized, using previously unused, rigorous classifying algorithms. Such ovarian cancer biomarkers represent a promising approach for the assessment and management of this devastating disease.

Adenocarcinoma↗
PubMed2003

Multicolor spectral karyotyping of rat chromosomes.

Rat and mouse have become important animal models to study various human diseases such as cancer. Cytogenetic analysis of the respective karyotypes is frequently required to investigate the causative genetic defects and especially neoplastic cells often show complex chromosome aberrations and many different marker chromosomes. However, structural homogeneity of the chromosomes in these species as well as less pronounced differences in banding patterns make it difficult to assign genetic abnormalities to certain chromosomes by conventional banding techniques. Here we report for the first time the successful application of multicolor spectral karyotyping (SKY) to rat chromosomes, which allows unequivocal identification of all rat chromosomes with the exception of chromosomes 13 and 14 in different colors, thus enabling the elucidation of even complex rearrangements in the rat karyotype. Flow-sorted chromosome specific painting probes for all 22 rat chromosomes (20 autosomes, X, and Y) were combinatorially labeled by a set of five different fluorochromes and hybridized in situ to metaphase spreads of a healthy rat, to diakineses from testicular material, and to cells from a rat FAO hepatoma cell line. Measuring the complete spectrum at each image point by using the SpectraCube((R)) spectral imaging system and respective computer software allowed identification of the individual rat chromosomes by their specific emission spectra. Classification algorithms in the analysis software can then display the rat chromosomes in specific pseudo-colors and automatically order them in a karyotype table. After its successful application to human and mouse chromosomes, spectral karyotyping of rat chromosomes now also allows cytogenetic screening of the complete rat genome by a single hybridization.

Animals↗
PubMed2002

Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap.

Vulnerable plaque generally contains a thin fibrous cap, lipid pools, and reduced internal plaque collagen. Arterial fluorescence analysis can differentiate atherosclerotic lesions from normal arteries; however, the contribution of the lipid core to atherosclerotic arterial fluorescence remains controversial. This study aimed to identify lipid core fluorophores and to differentiate the lipid core from normal artery and atheroma. The helium-cadmium laser-induced fluorescence spectra of cadaveric arteries and known chemical constituents were recorded. Lipid core fluorescence spectra exhibited marked red shifts and broadening compared with the fluorescence spectra of normal tissue and atheroma. Similar fluorescence spectra were obtained for lipid core and oxidized low density lipoprotein, for atheroma and collagen, and for normal artery and elastin. A classification based on collagen, elastin, and oxidized low density lipoprotein spectral decomposition could discriminate the lipid core (n=29), normal artery (n=74), atheroma (n=73), and preatheroma (n=10) with 86% accuracy. Fibrous cap thickness was correlated with the spectral collagen content index (r=0.65, P<0.0001), especially at a thickness of <200 microm. We conclude that a classification algorithm based on chemical spectral decomposition can accurately classify the fluorescence spectra of normal artery, atheroma, and lipid core and may be useful in identifying vulnerable atheroma in vivo.

Adult↗
PubMed1995

Association of coronary disease with segment-specific intimal-medial thickening of the extracranial carotid artery.

BACKGROUND: Several investigators have evaluated relations between risk factors and intimal-medial thickness (IMT) of the extracranial carotid arteries and between IMT and clinical cardiovascular disease. Different indexes of IMT have been used as referents. We compared the strength of association of various IMT measurements with coronary artery disease as measured at coronary angiography. METHODS AND RESULTS: We quantified the mean of the IMT for 12 sites of the extracranial carotid arteries (common carotid, bifurcation, internal carotid, near and far walls, and left and right sides [mean aggregate]) as well as for various combinations of sites (eg, segment-specific means, far walls only, maximum of any site) in 270 patients with or free of coronary artery disease. Models including age and all the indexes of IMT identified the mean aggregate as the only variable independently associated with the status of coronary atherosclerosis for the group as a whole. Next most strongly correlated was the mean common plus bifurcation. When classification algorithms were tested for ability to correctly classify case patients and control subjects, the mean bifurcation, mean common plus bifurcation, and mean aggregate were most strongly related to case-control status; however, the predictive power of the mean common was also strong. CONCLUSIONS: These data support use of the mean aggregate extracranial carotid IMT for correlation with the status of coronary atherosclerosis; however, the data also support use of the mean common plus bifurcation, since there is little increase in predictive power of the mean aggregate over this index. Use of the common carotid alone is also justifiable and may be preferable for certain analyses.

Age Factors↗
PubMed2001

Automatic classification of HITS into artifacts or solid or gaseous emboli by a wavelet representation combined with dual-gate TCD.

BACKGROUND AND PURPOSE: Transcranial Doppler (TCD) can detect high-intensity transient signals (HITS) in the cerebral circulation. HITS may correspond to artifacts or solid or gaseous emboli. The aim of this study was to develop an offline automated Doppler system allowing the classification of HITS. METHODS: We studied 600 HITS in vivo, including 200 artifacts from normal subjects, 200 solid emboli from patients with symptomatic internal carotid artery stenosis, and 200 gaseous emboli in stroke patients with patent foramen ovale. The study was 2-fold, each part involving 300 HITS (100 of each type). The first 300 HITS (learning set) were used to construct an automated classification algorithm. The remaining 300 HITS (validation set) were used to check the validity of this algorithm. To classify HITS, we combined dual-gate TCD with a wavelet representation and compared it with the current "gold standard," the human experts. RESULTS: A combination of the peak frequency of HITS and the time delay makes it possible to separate artifacts from emboli. On the validation set, we achieved a sensitivity of 97%, a specificity of 98%, a positive predictive value (PPV) of 99%, and a negative predictive value (NPV) of 94%. To distinguish between solid and gaseous emboli, where positive refers now to the solid emboli, we used the peak frequency, the relative power, and the envelope symmetry of HITS. On the validation set, we achieved a sensitivity of 89%, a specificity of 86%, a conditional PPV of 89%, and a conditional NPV of 89%. CONCLUSIONS: An automated wavelet representation combined with dual-gate TCD can reliably reject artifacts from emboli. From a clinical standpoint, however, this approach has only a fair accuracy in differentiating between solid and gaseous emboli.

Adult↗
PubMed2003

Accurate on-line support vector regression.

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented. In both scenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.

Algorithms↗
PubMed2006

Functional specialization within rostral prefrontal cortex (area 10): a meta-analysis.

One of the least well understood regions of the human brain is rostral prefrontal cortex, approximating Brodmann's area 10. Here, we investigate the possibility that there are functional subdivisions within this region by conducting a meta-analysis of 104 functional neuroimaging studies (using positron emission tomography/functional magnetic resonance imaging). Studies involving working memory and episodic memory retrieval were disproportionately associated with lateral activations, whereas studies involving mentalizing (i.e., attending to one's own emotions and mental states or those of other agents) were disproportionately associated with medial activations. Functional variation was also observed along a rostral-caudal axis, with studies involving mentalizing yielding relatively caudal activations and studies involving multiple-task coordination yielding relatively rostral activations. A classification algorithm was trained to predict the task, given the coordinates of each activation peak. Performance was well above chance levels (74% for the three most common tasks; 45% across all eight tasks investigated) and generalized to data not included in the training set. These results point to considerable functional segregation within rostral prefrontal cortex.

Algorithms↗
PubMed2005

General-medical conditions in older patients with serious mental illness.

OBJECTIVE: The burden of medical comorbidities was compared between older (> or =60 years) and younger patients with serious mental illness. METHODS: Patients (N=8,083) diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder in 2001 were identified from VA facilities in the mid-Atlantic region. Medical comorbidities were identified by an ICD-9-based clinical classification algorithm. RESULTS: Older, versus younger, patients were more likely to be diagnosed with cardiovascular or pulmonary conditions, and less likely to be diagnosed with substance-use disorders or hepatic conditions. CONCLUSIONS: More aggressive detection and management of general-medical comorbidities in older patients with serious mental illness is paramount.

Bipolar Disorder↗
PubMed2005

Propensity scores: an introduction and experimental test.

Propensity score analysis is a relatively recent statistical innovation that is useful in the analysis of data from quasi-experiments. The goal of propensity score analysis is to balance two non-equivalent groups on observed covariates to get more accurate estimates of the effects of a treatment on which the two groups differ. This article presents a general introduction to propensity score analysis, provides an example using data from a quasi-experiment compared to a benchmark randomized experiment, offers practical advice about how to do such analyses, and discusses some limitations of the approach. It also presents the first detailed instructions to appear in the literature on how to use classification tree analysis and bagging for classification trees in the construction of propensity scores. The latter two examples serve as an introduction for researchers interested in computing propensity scores using more complex classification algorithms known as ensemble methods.

Algorithms↗
PubMed2004

Autism in Angelman syndrome: an exploration of comorbidity.

The aim was to explore the comorbidity between Angelman syndrome and autism spectrum disorders (ASDs). Identification of autism in children with Angelman syndrome presents a diagnostic challenge. In the present study, 16 children with Angelman syndrome, all with a 15q11-13 deletion, were examined for ASDs. Thirteen children with Angelman syndrome received an ADOS-G algorithm classification of ASD; the remaining three were outside the autistic spectrum. Ten fulfilled the criteria for autism, and three for PDD-NOS. The 10 children with Angelman syndrome and comorbid autism were compared with eight children with only autism regarding their social and communicative skills. The results indicated that Angelman syndrome is better understood in terms of developmental delay, and autism in terms of developmental deviance. It is concluded that autism might have been overdiagnosed due to the extremely low mental age of the children with Angelman syndrome.

Adolescent↗
PubMed2003

GeneViTo: visualizing gene-product functional and structural features in genomic datasets.

BACKGROUND: The availability of increasing amounts of sequence data from completely sequenced genomes boosts the development of new computational methods for automated genome annotation and comparative genomics. Therefore, there is a need for tools that facilitate the visualization of raw data and results produced by bioinformatics analysis, providing new means for interactive genome exploration. Visual inspection can be used as a basis to assess the quality of various analysis algorithms and to aid in-depth genomic studies. RESULTS: GeneViTo is a JAVA-based computer application that serves as a workbench for genome-wide analysis through visual interaction. The application deals with various experimental information concerning both DNA and protein sequences (derived from public sequence databases or proprietary data sources) and meta-data obtained by various prediction algorithms, classification schemes or user-defined features. Interaction with a Graphical User Interface (GUI) allows easy extraction of genomic and proteomic data referring to the sequence itself, sequence features, or general structural and functional features. Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information, especially in cases of "poor" annotation, or an evaluation of available predictions. Moreover, desired information can be output in high quality JPEG image files for further elaboration and scientific use. A compilation of properly formatted GeneViTo input data for demonstration is available to interested readers for two completely sequenced prokaryotes, Chlamydia trachomatis and Methanococcus jannaschii. CONCLUSIONS: GeneViTo offers an inspectional view of genomic functional elements, concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes. The application is compatible with Linux or Windows ME-2000-XP operating systems, provided that the appropriate Java Runtime Environment is already installed in the system.

Bacterial Proton-Translocating ATPases↗
PubMed2006

Data processing and classification analysis of proteomic changes: a case study of oil pollution in the mussel, Mytilus edulis.

BACKGROUND: Proteomics may help to detect subtle pollution-related changes, such as responses to mixture pollution at low concentrations, where clear signs of toxicity are absent. The challenges associated with the analysis of large-scale multivariate proteomic datasets have been widely discussed in medical research and biomarker discovery. This concept has been introduced to ecotoxicology only recently, so data processing and classification analysis need to be refined before they can be readily applied in biomarker discovery and monitoring studies. RESULTS: Data sets obtained from a case study of oil pollution in the Blue mussel were investigated for differential protein expression by retentate chromatography-mass spectrometry and decision tree classification. Different tissues and different settings were used to evaluate classifiers towards their discriminatory power. It was found that, due the intrinsic variability of the data sets, reliable classification of unknown samples could only be achieved on a broad statistical basis (n > 60) with the observed expression changes comprising high statistical significance and sufficient amplitude. The application of stringent criteria to guard against overfitting of the models eventually allowed satisfactory classification for only one of the investigated data sets and settings. CONCLUSION: Machine learning techniques provide a promising approach to process and extract informative expression signatures from high-dimensional mass-spectrometry data. Even though characterisation of the proteins forming the expression signatures would be ideal, knowledge of the specific proteins is not mandatory for effective class discrimination. This may constitute a new biomarker approach in ecotoxicology, where working with organisms, which do not have sequenced genomes render protein identification by database searching problematic. However, data processing has to be critically evaluated and statistical constraints have to be considered before supervised classification algorithms are employed.

Journal Article↗
PubMed2006

A radial basis classifier for the automatic detection of aspiration in children with dysphagia.

BACKGROUND: Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. METHODS: Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. RESULTS: Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 +/- 7.3%, a sensitivity of 79.4 +/- 11.7% and specificity of 80.3 +/- 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 +/- 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. CONCLUSION: The proposed aspiration classification algorithm provides promising accuracy for aspiration detection in children. The classifier is conducive to hardware implementation as a non-invasive, portable "aspirometer". Future research should focus on further enhancement of accuracy rates by considering other signal features, classifier methods, or an augmented variety of training samples. The present study is an important first step towards the eventual development of wearable intelligent intervention systems for the diagnosis and management of aspiration.

Journal Article↗
PubMed2002

Preoperative prediction of pediatric patients with effusions and edema following cardiopulmonary bypass surgery by serological and routine laboratory data.

AIM: Postoperative effusions and edema and capillary leak syndrome in children after cardiac surgery with cardiopulmonary bypass constitute considerable clinical problems. Overshooting immune response is held to be the cause. In a prospective study we investigated whether preoperative immune status differences exist in patients at risk for postsurgical effusions and edema, and to what extent these differences permit prediction of the postoperative outcome. METHODS: One-day preoperative serum levels of immunoglobulins, complement, cytokines and chemokines, soluble adhesion molecules and receptors as well as clinical chemistry parameters such as differential counts, creatinine, blood coagulation status (altogether 56 parameters) were analyzed in peripheral blood samples of 75 children (aged 3-18 years) undergoing cardiopulmonary bypass surgery (29 with postoperative effusions and edema within the first postoperative week). RESULTS: Preoperative elevation of the serum level of C3 and C5 complement components, tumor necrosis factor-alpha, percentage of leukocytes that are neutrophils, body weight and decreased percentage of lymphocytes (all P < 0.03) occurred in children developing postoperative effusions and edema. While single parameters did not predict individual outcome, >86% of the patients with postoperative effusions and oedema were correctly predicted using two different classification algorithms. Data mining by both methods selected nine partially overlapping parameters. The prediction quality was independent of the congenital heart defect. CONCLUSION: Indicators of inflammation were selected as risk indicators by explorative data analysis. This suggests that preoperative differences in the immune system and capillary permeability status exist in patients at risk for postoperative effusions. These differences are suitable for preoperative risk assessment and may be used for the benefit of the patient and to improve cost effectiveness.

Adolescent↗
PubMed2006

Classification methods for the development of genomic signatures from high-dimensional data.

Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions (CERP) for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical/histopathological measurements to the genomic data.

Algorithms↗
PubMed2006

Chemotherapy for advanced germ cell tumors.

PURPOSE: Germ cell tumors constitute the most curable of all cancers. Standard treatment of previously untreated and treated patients has evolved on the basis of prospective clinical trials and prognostic factors. This review summarizes the prognostic criteria on which treatment decisions may be based, and outlines the current treatment approaches. PATIENTS AND METHODS: Randomized and nonrandomized trials of first-line, salvage, and palliative therapy and the role of surgery after chemotherapy were reviewed. In the treatment of previously untreated patients, emphasis was placed on interpretation of data of trials according to the International Germ Cell Cancer Collaborative Group model, which has evolved into a universally accepted classification algorithm for determining appropriate risk-directed chemotherapy. This system permits treatment choices based on the balance between benefit and toxicity and allows comparison of results across multiple clinical trials. RESULTS: Standard therapy for good-risk patients is four cycles of etoposide plus cisplatin or three cycles of cisplatin, etoposide plus bleomycin (BEP x 3); both approaches cure approximately 90% of patients. After chemotherapy and normalization of markers, patients should generally undergo resection of residual masses. Approximately 75% of intermediate-risk and 45% of poor-risk patients group achieve a durable complete response with BEP x 4. Potentially curative options in the salvage setting include ifosfamide plus cisplatin-containing standard dose therapy and high-dose carboplatin plus stem-cell rescue. Surgery remains an essential component of care. CONCLUSION: Curative therapy exists even in patients with resistant disease, and treatment choices can be based on established clinical criteria. Serum tumor markers and surgery after chemotherapy have essential roles in patient management

Antineoplastic Combined Chemotherapy Protocols↗