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1.
Nucleic Acids Res ; 48(13): 7079-7098, 2020 Jul 27.
Artigo em Inglês | MEDLINE | ID: mdl-32525984

RESUMO

We give results from a detailed analysis of human Ribosomal Protein (RP) levels in normal and cancer samples and cell lines from large mRNA, copy number variation and ribosome profiling datasets. After normalizing total RP mRNA levels per sample, we find highly consistent tissue specific RP mRNA signatures in normal and tumor samples. Multiple RP mRNA-subtypes exist in several cancers, with significant survival and genomic differences. Some RP mRNA variations among subtypes correlate with copy number loss of RP genes. In kidney cancer, RP subtypes map to molecular subtypes related to cell-of-origin. Pan-cancer analysis of TCGA data showed widespread single/double copy loss of RP genes, without significantly affecting survival. In several cancer cell lines, CRISPR-Cas9 knockout of RP genes did not affect cell viability. Matched RP ribosome profiling and mRNA data in humans and rodents stratified by tissue and development stage and were strongly correlated, showing that RP translation rates were proportional to mRNA levels. In a small dataset of human adult and fetal tissues, RP protein levels showed development stage and tissue specific heterogeneity of RP levels. Our results suggest that heterogeneous RP levels play a significant functional role in cellular physiology, in both normal and disease states.

2.
Aliment Pharmacol Ther ; 51(11): 1188-1197, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32298002

RESUMO

BACKGROUND: The development of accurate, non-invasive markers to diagnose and stage non-alcoholic fatty liver disease (NAFLD) is critical to reduce the need for an invasive liver biopsy and to identify patients who are at the highest risk of hepatic and cardio-metabolic complications. Disruption of steroid hormone metabolic pathways has been described in patients with NAFLD. AIM(S): To assess the hypothesis that assessment of the urinary steroid metabolome may provide a novel, non-invasive biomarker strategy to stage NAFLD. METHODS: We analysed the urinary steroid metabolome in 275 subjects (121 with biopsy-proven NAFLD, 48 with alcohol-related cirrhosis and 106 controls), using gas chromatography-mass spectrometry (GC-MS) coupled with machine learning-based Generalised Matrix Learning Vector Quantisation (GMLVQ) analysis. RESULTS: Generalised Matrix Learning Vector Quantisation analysis achieved excellent separation of early (F0-F2) from advanced (F3-F4) fibrosis (AUC receiver operating characteristics [ROC]: 0.92 [0.91-0.94]). Furthermore, there was near perfect separation of controls from patients with advanced fibrotic NAFLD (AUC ROC = 0.99 [0.98-0.99]) and from those with NAFLD cirrhosis (AUC ROC = 1.0 [1.0-1.0]). This approach was also able to distinguish patients with NAFLD cirrhosis from those with alcohol-related cirrhosis (AUC ROC = 0.83 [0.81-0.85]). CONCLUSIONS: Unbiased GMLVQ analysis of the urinary steroid metabolome offers excellent potential as a non-invasive biomarker approach to stage NAFLD fibrosis as well as to screen for NAFLD. A highly sensitive and specific urinary biomarker is likely to have clinical utility both in secondary care and in the broader general population within primary care and could significantly decrease the need for liver biopsy.

3.
J Clin Endocrinol Metab ; 105(3)2020 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-31665449

RESUMO

CONTEXT: Urine steroid metabolomics, combining mass spectrometry-based steroid profiling and machine learning, has been described as a novel diagnostic tool for detection of adrenocortical carcinoma (ACC). OBJECTIVE, DESIGN, SETTING: This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for postoperative recurrence detection after microscopically complete (R0) resection of ACC. PATIENTS AND METHODS: 135 patients from 14 clinical centers provided postoperative urine samples, which were analyzed by gas chromatography-mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by expert clinicians or when analyzed by random forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard. RESULTS: Imaging detected recurrent disease in 42 of 135 patients; 32 had provided pre- and post-recurrence urine samples. 39 patients remained disease-free for ≥3 years. The urine "steroid fingerprint" at recurrence resembled that observed before R0 resection in the majority of cases. Review of longitudinally collected urine steroid profiles by 3 blinded experts detected recurrence by the time of radiological diagnosis in 50% to 72% of cases, improving to 69% to 92%, if a preoperative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22% to 39% of patients. Specificities varied considerably, ranging from 61% to 97%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity = specificity = 81%). CONCLUSION: Urine steroid metabolomics is a promising tool for postoperative recurrence detection in ACC; availability of a preoperative urine considerably improves the ability to detect ACC recurrence.

4.
PLoS One ; 13(12): e0208389, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30521568

RESUMO

Prescription sequence symmetry analysis (PSSA), a case-only design introduced in 1996, has been increasingly used to identify unintentional drug effects, and has potential applications as a hypothesis-testing and a hypothesis-generating method, due to its easy application and effective control of time-invariant confounders. The aim of this study is to systematically compare effect estimates from the PSSA to effect estimates from conventional observational parallel group study designs, to assess the validity and constraints of the PSSA study design. We reviewed the MEDLINE, EMBASE, and Web of Science databases until February 2016 to identify studies that compared PSSA to a parallel group design. Data from the eligible articles was extracted and analyzed, including making a Bland-Altman plot and calculating the number of discrepancies between the designs. 63 comparisons (from two studies) were included in the review. There was a significant correlation (p < 0.001) between the effect estimates of the PSSA and the parallel group designs, but the bias indicated by the Bland-Altman plot (0.20) and the percentage of discrepancies (70-80%) showed that this correlation was not accompanied by a considerable similarity of the effect estimates. Overall, the effect estimates of the parallel group designs were higher than those of the PSSA, not necessarily further away from 1, and the parallel group designs also generated more significant signals. However, these results should be approached with caution, as the effect estimates were only retrieved from two separate studies. This review indicates that, even though PSSA has a lot of potential, the effect estimates generated by the PSSA are usually lower than the effect estimates generated by parallel group designs, and PSSA mostly has a lower power than the conventional study designs, but this is based on limited comparisons, and more comparisons are needed to make a proper conclusion.


Assuntos
Bases de Dados Factuais , Prescrições , Humanos , Modelos Teóricos
5.
JCI Insight ; 2(8)2017 Apr 20.
Artigo em Inglês | MEDLINE | ID: mdl-28422753

RESUMO

BACKGROUND: Adrenal aldosterone excess is the most common cause of secondary hypertension and is associated with increased cardiovascular morbidity. However, adverse metabolic risk in primary aldosteronism extends beyond hypertension, with increased rates of insulin resistance, type 2 diabetes, and osteoporosis, which cannot be easily explained by aldosterone excess. METHODS: We performed mass spectrometry-based analysis of a 24-hour urine steroid metabolome in 174 newly diagnosed patients with primary aldosteronism (103 unilateral adenomas, 71 bilateral adrenal hyperplasias) in comparison to 162 healthy controls, 56 patients with endocrine inactive adrenal adenoma, 104 patients with mild subclinical, and 47 with clinically overt adrenal cortisol excess. We also analyzed the expression of cortisol-producing CYP11B1 and aldosterone-producing CYP11B2 enzymes in adenoma tissue from 57 patients with aldosterone-producing adenoma, employing immunohistochemistry with digital image analysis. RESULTS: Primary aldosteronism patients had significantly increased cortisol and total glucocorticoid metabolite excretion (all P < 0.001), only exceeded by glucocorticoid output in patients with clinically overt adrenal Cushing syndrome. Several surrogate parameters of metabolic risk correlated significantly with glucocorticoid but not mineralocorticoid output. Intratumoral CYP11B1 expression was significantly associated with the corresponding in vivo glucocorticoid excretion. Unilateral adrenalectomy resolved both mineralocorticoid and glucocorticoid excess. Postoperative evidence of adrenal insufficiency was found in 13 (29%) of 45 consecutively tested patients. CONCLUSION: Our data indicate that glucocorticoid cosecretion is frequently found in primary aldosteronism and contributes to associated metabolic risk. Mineralocorticoid receptor antagonist therapy alone may not be sufficient to counteract adverse metabolic risk in medically treated patients with primary aldosteronism. FUNDING: Medical Research Council UK, Wellcome Trust, European Commission.

6.
Wiley Interdiscip Rev Cogn Sci ; 7(2): 92-111, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26800334

RESUMO

An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning.


Assuntos
Simulação por Computador , Mineração de Dados/métodos , Aprendizado de Máquina , Reconhecimento Automatizado de Padrão/métodos , Redes Neurais de Computação , Neurônios/fisiologia , Estatística como Assunto
7.
Front Comput Neurosci ; 9: 110, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-26441620

RESUMO

Internal coordination models hold that early nervous systems evolved in the first place to coordinate internal activity at a multicellular level, most notably the use of multicellular contractility as an effector for motility. A recent example of such a model, the skin brain thesis, suggests that excitable epithelia using chemical signaling are a potential candidate as a nervous system precursor. We developed a computational model and a measure for whole body coordination to investigate the coordinative properties of such excitable epithelia. Using this measure we show that excitable epithelia can spontaneously exhibit body-scale patterns of activation. Relevant factors determining the extent of patterning are the noise level for exocytosis, relative body dimensions, and body size. In smaller bodies whole-body coordination emerges from cellular excitability and bidirectional excitatory transmission alone. Our results show that basic internal coordination as proposed by the skin brain thesis could have arisen in this potential nervous system precursor, supporting that this configuration may have played a role as a proto-neural system and requires further investigation.

8.
Prev Chronic Dis ; 12: E33, 2015 Mar 12.
Artigo em Inglês | MEDLINE | ID: mdl-25764139

RESUMO

INTRODUCTION: This study combined information on the interventions of the US Department of Agriculture's Supplemental Nutrition Assistance Program-Education with 5,927 interview responses from the California Health Interview Survey to investigate associations between levels of intervention reach in low-income census tracts in California and self-reported physical activity and consumption of fruits and vegetables, fast food, and sugar-sweetened beverages. METHODS: We determined 4 levels of intervention reach (low reach, moderate reach, high reach, and no intervention) across 1,273 program-eligible census tracts from data on actual and eligible number of intervention participants. The locations of California Health Interview Survey respondents were geocoded and linked with program data. Regression analyses included measures for sex, age, race/ethnicity, and education. RESULTS: Adults and children from high-reach census tracts reported eating more fruits and vegetables than adults and children from no-intervention census tracts. Adults from census tracts with low, moderate, or high levels of reach reported eating fast food less often than adults from no-intervention census tracts. Teenagers from low-reach census tracts reported more physical activity than teenagers in no-intervention census tracts. CONCLUSION: The greatest concentration of Supplemental Nutrition Assistance Program-Education interventions was associated with adults and children eating more fruits and vegetables and adults eating fast food less frequently. These findings demonstrate the potential impact of such interventions as implemented by numerous organizations with diverse populations; these interventions can play an important role in addressing the obesity epidemic in the United States. Limitations of this study include the absence of measures of exposure to the intervention at the individual level and low statistical power for the teenager sample.


Assuntos
Comportamento Alimentar , Assistência Alimentar , Promoção da Saúde/normas , Atividade Motora/fisiologia , Ciências da Nutrição/educação , Adolescente , Adulto , Idoso , California , Censos , Criança , Pré-Escolar , Estudos Transversais , Grupos Étnicos/estatística & dados numéricos , Fast Foods , Feminino , Frutas , Conhecimentos, Atitudes e Prática em Saúde , Inquéritos Epidemiológicos , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Avaliação de Resultados em Cuidados de Saúde , Reprodutibilidade dos Testes , Classe Social , Inquéritos e Questionários , Verduras , Adulto Jovem
9.
Bioinformatics ; 31(4): 453-61, 2015 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-24994890

RESUMO

MOTIVATION: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. RESULTS: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. AVAILABILITY AND IMPLEMENTATION: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. CONTACT: meikelbiehl@gmail.com.


Assuntos
Brônquios/metabolismo , Células Epiteliais/metabolismo , Perfilação da Expressão Gênica , Fosfoproteínas/metabolismo , Software , Biologia de Sistemas/métodos , Algoritmos , Animais , Brônquios/citologia , Células Cultivadas , Bases de Dados Factuais , Células Epiteliais/citologia , Regulação da Expressão Gênica , Humanos , Análise de Sequência com Séries de Oligonucleotídeos , Fosforilação , Ratos , Especificidade da Espécie , Pesquisa Médica Translacional
10.
Bioinformatics ; 31(4): 492-500, 2015 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-25152231

RESUMO

MOTIVATION: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development. However, in many cases, a naive 'extrapolation' between the two species has not succeeded. As a result, clinical trials of new drugs sometimes fail even after considerable success in the mouse or rat stage of development. In addition to in vitro studies, inter-species translation requires analytical tools that can predict the enriched gene sets in human cells under various stimuli from corresponding measurements in animals. Such tools can improve our understanding of the underlying biology and optimize the allocation of resources for drug development. RESULTS: We developed an algorithm to predict differential gene set enrichment as part of the sbv IMPROVER (systems biology verification in Industrial Methodology for Process Verification in Research) Species Translation Challenge, which focused on phosphoproteomic and transcriptomic measurements of normal human bronchial epithelial (NHBE) primary cells under various stimuli and corresponding measurements in rat (NRBE) primary cells. We find that gene sets exhibit a higher inter-species correlation compared with individual genes, and are potentially more suited for direct prediction. Furthermore, in contrast to a similar cross-species response in protein phosphorylation states 5 and 25 min after exposure to stimuli, gene set enrichment 6 h after exposure is significantly different in NHBE cells compared with NRBE cells. In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods. AVAILABILITY AND IMPLEMENTATION: Implementation of all algorithms is available as source code (in Matlab) at http://bhanot.biomaps.rutgers.edu/wiki/codes_SC3_Predicting_GeneSets.zip, along with the relevant data used in the analysis. Gene sets, gene expression and protein phosphorylation data are available on request. CONTACT: hormoz@kitp.ucsb.edu.


Assuntos
Algoritmos , Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes , Proteômica/métodos , Biologia de Sistemas/métodos , Animais , Brônquios/citologia , Brônquios/metabolismo , Células Cultivadas , Citocinas/metabolismo , Interpretação Estatística de Dados , Bases de Dados Factuais , Células Epiteliais/citologia , Células Epiteliais/metabolismo , Regulação da Expressão Gênica , Humanos , Camundongos , Fosfoproteínas/metabolismo , Fosforilação , Ratos , Transdução de Sinais , Especificidade da Espécie
11.
Bioinformatics ; 31(4): 462-70, 2015 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-25061067

RESUMO

MOTIVATION: Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue. RESULTS: Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins. AVAILABILITY AND IMPLEMENTATION: Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively. CONTACT: gyanbhanot@gmail.com or luofeng@clemson.edu or atarca@med.wayne.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Células Epiteliais/metabolismo , Perfilação da Expressão Gênica , Pulmão/metabolismo , Fosfoproteínas/metabolismo , Software , Biologia de Sistemas/métodos , Algoritmos , Animais , Células Cultivadas , Bases de Dados Factuais , Células Epiteliais/citologia , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Pulmão/citologia , Análise de Sequência com Séries de Oligonucleotídeos , Fosforilação , Ratos , Especificidade da Espécie , Pesquisa Médica Translacional
12.
Cancer Cell ; 26(3): 319-330, 2014 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-25155756

RESUMO

We describe the landscape of somatic genomic alterations of 66 chromophobe renal cell carcinomas (ChRCCs) on the basis of multidimensional and comprehensive characterization, including mtDNA and whole-genome sequencing. The result is consistent that ChRCC originates from the distal nephron compared with other kidney cancers with more proximal origins. Combined mtDNA and gene expression analysis implicates changes in mitochondrial function as a component of the disease biology, while suggesting alternative roles for mtDNA mutations in cancers relying on oxidative phosphorylation. Genomic rearrangements lead to recurrent structural breakpoints within TERT promoter region, which correlates with highly elevated TERT expression and manifestation of kataegis, representing a mechanism of TERT upregulation in cancer distinct from previously observed amplifications and point mutations.


Assuntos
Carcinoma de Células Renais/genética , Neoplasias Renais/genética , Sequência de Bases , Pontos de Quebra do Cromossomo , Deleção Cromossômica , Cromossomos Humanos/genética , Variações do Número de Cópias de DNA , Metilação de DNA , Análise Mutacional de DNA , DNA Mitocondrial/genética , Exoma , Genoma Humano , Humanos , Dados de Sequência Molecular , Regiões Promotoras Genéticas , Telomerase/genética , Transcriptoma
13.
PLoS One ; 8(3): e59401, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-23527184

RESUMO

Flow cytometry is a widely used technique for the analysis of cell populations in the study and diagnosis of human diseases. It yields large amounts of high-dimensional data, the analysis of which would clearly benefit from efficient computational approaches aiming at automated diagnosis and decision support. This article presents our analysis of flow cytometry data in the framework of the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukemia (AML) Challenge, 2011. In the challenge, example data was provided for a set of 179 subjects, comprising healthy donors and 23 cases of AML. The participants were asked to provide predictions with respect to the condition of 180 patients in a test set. We extracted feature vectors from the data in terms of single marker statistics, including characteristic moments, median and interquartile range of the observed values. Subsequently, we applied Generalized Matrix Relevance Learning Vector Quantization (GMLVQ), a machine learning technique which extends standard LVQ by an adaptive distance measure. Our method achieved the best possible performance with respect to the diagnoses of test set patients. The extraction of features from the flow cytometry data is outlined in detail, the machine learning approach is discussed and classification results are presented. In addition, we illustrate how GMLVQ can provide deeper insight into the problem by allowing to infer the relevance of specific markers and features for the diagnosis.


Assuntos
Inteligência Artificial , Biologia Computacional/métodos , Técnicas de Apoio para a Decisão , Diagnóstico por Computador/métodos , Citometria de Fluxo/métodos , Leucemia Mieloide Aguda/diagnóstico , Biomarcadores , Humanos
14.
Artif Intell Med ; 56(2): 91-7, 2012 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-23010586

RESUMO

OBJECTIVE: The generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images. METHODOLOGY: After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. 65 texture features were extracted from gray-level co-occurrence matrices (GLCMs). These features were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. The classification performance for different feature subsets was calculated for a k-nearest-neighbor (kNN) and a random forests classifier (RanForest), and support vector machines with a linear and a radial basis function kernel (SVMlin and SVMrbf). RESULTS: For all classifiers, feature sets selected by the relevance ranking assessed by GMLVQ had a significantly better classification performance (p<0.05) for many texture feature sets compared to the MI approach. For kNN, RanForest, and SVMrbf, some of these feature subsets had a significantly better classification performance when compared to the set consisting of all features (p<0.05). CONCLUSION: While this approach estimates the relevance of single features, future considerations of GMLVQ should include the pairwise correlation for the feature ranking, e.g. to reduce the redundancy of two equally relevant features.


Assuntos
Algoritmos , Doenças Pulmonares Intersticiais/classificação , Doenças Pulmonares Intersticiais/diagnóstico , Tomografia Computadorizada por Raios X/métodos , Análise por Conglomerados , Humanos , Máquina de Vetores de Suporte
16.
Neural Netw ; 26: 159-73, 2012 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-22041220

RESUMO

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method.


Assuntos
Inteligência Artificial , Aprendizagem , Algoritmos , Análise Discriminante , Humanos , Reconhecimento Automatizado de Padrão
17.
J Clin Endocrinol Metab ; 96(12): 3775-84, 2011 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-21917861

RESUMO

CONTEXT: Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values. OBJECTIVE: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy. DESIGN: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19-84 yr) and 45 ACC patients (20-80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26-201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids. RESULTS: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers. CONCLUSIONS: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.


Assuntos
Corticosteroides/urina , Neoplasias do Córtex Suprarrenal/diagnóstico , Adenoma Adrenocortical/diagnóstico , Carcinoma Adrenocortical/diagnóstico , Neoplasias do Córtex Suprarrenal/patologia , Neoplasias do Córtex Suprarrenal/urina , Adenoma Adrenocortical/patologia , Adenoma Adrenocortical/urina , Carcinoma Adrenocortical/patologia , Carcinoma Adrenocortical/urina , Adulto , Idoso , Idoso de 80 Anos ou mais , Biomarcadores/urina , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Espectrometria de Massas , Metabolômica , Pessoa de Meia-Idade
18.
IEEE Trans Neural Netw ; 21(5): 831-40, 2010 May.
Artigo em Inglês | MEDLINE | ID: mdl-20236882

RESUMO

In this paper, we present a regularization technique to extend recently proposed matrix learning schemes in learning vector quantization (LVQ). These learning algorithms extend the concept of adaptive distance measures in LVQ to the use of relevance matrices. In general, metric learning can display a tendency towards oversimplification in the course of training. An overly pronounced elimination of dimensions in feature space can have negative effects on the performance and may lead to instabilities in the training. We focus on matrix learning in generalized LVQ (GLVQ). Extending the cost function by an appropriate regularization term prevents the unfavorable behavior and can help to improve the generalization ability. The approach is first tested and illustrated in terms of artificial model data. Furthermore, we apply the scheme to benchmark classification data sets from the UCI Repository of Machine Learning. We demonstrate the usefulness of regularization also in the case of rank limited relevance matrices, i.e., matrix learning with an implicit, low-dimensional representation of the data.


Assuntos
Inteligência Artificial , Retroalimentação , Aprendizagem/fisiologia , Redes Neurais de Computação , Algoritmos , Humanos
19.
Neural Comput ; 21(12): 3532-61, 2009 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-19764875

RESUMO

We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure of the data appropriately. Large margin generalization bounds can be transferred to this case, leading to bounds that are independent of the input dimensionality. This also holds for local metrics attached to each prototype, which corresponds to piecewise quadratic decision boundaries. The algorithm is tested in comparison to alternative learning vector quantization schemes using an artificial data set, a benchmark multiclass problem from the UCI repository, and a problem from bioinformatics, the recognition of splice sites for C. elegans.


Assuntos
Inteligência Artificial , Teoria da Informação , Aprendizagem/fisiologia , Aprendizagem por Probabilidade , Algoritmos , Encéfalo/fisiologia , Processamento Eletrônico de Dados , Humanos
20.
Neural Comput ; 21(10): 2942-69, 2009 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-19635012

RESUMO

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of the methods to more general metric structures have been proposed, such as relevance adaptation in generalized LVQ (GLVQ) and matrix learning in GLVQ. In these approaches, metric parameters are learned based on the given classification task such that a data-driven distance measure is found. In this letter, we consider full matrix adaptation in advanced LVQ schemes. In particular, we introduce matrix learning to a recent statistical formalization of LVQ, robust soft LVQ, and we compare the results on several artificial and real-life data sets to matrix learning in GLVQ, a derivation of LVQ-like learning based on a (heuristic) cost function. In all cases, matrix adaptation allows a significant improvement of the classification accuracy. Interestingly, however, the principled behavior of the models with respect to prototype locations and extracted matrix dimensions shows several characteristic differences depending on the data sets.


Assuntos
Algoritmos , Inteligência Artificial , Simulação por Computador , Aprendizagem/fisiologia , Redes Neurais de Computação , Animais , Encéfalo/fisiologia , Humanos , Rede Nervosa/fisiologia , Dinâmica não Linear
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