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At least 667 records · Page 37Linked to original sources
PubMedDate not supplied

Integrated homology modelling and X-ray study of herpes simplex virus I thymidine kinase: a case study.

Knowledge-based homology modelling together with site-directed mutagenesis, epitope and conformational mapping is an approach to predict the structures of proteins and for the rational design of new drugs. In this study we present how this procedure has been applied to model the structure of herpes simplex virus type 1 thymidine kinase (HSV1 TK, HSV1 ATP-thymidine-5'-phosphotransferase, EC 2.7.1.21). We have used, and evaluated, several secondary structure prediction methods, such as the classical one based on Chou and Fastman algorithm, neural networks using the Kabsch and Sander classification, and the PRISM method. We have validated the algorithms by applying them to the porcine adenylate kinase (ADK), whose three-dimensional structure is known and that has been used for the alignment of the TKs as well. The resulting first model of HSV1-TK consisted of the first beta-strand connected to the phosphate binding loop and its subsequent alpha-helix, the fourth beta-strand connected to the conserved FDRH sequence and two alpha-helix with basic amino acids. The 3D structure was built using the X-ray structure of ADK as template and following the general procedure for homology modelling. We extended the model by means of COMPOSER, an automatic process for protein modelling. Site-directed mutagenesis was used to experimentally verify the predicted active-site model of HSV1-TK. The data measured in our lab and by others support the suggestion that the FDRH motif is part of the active site and plays an important role in the phosphorylation of substrates. The structure of HSV1 TK, recently solved in collaboration with Prof. G. Schulz at 2.7 A resolution, includes 284 of 343 residues of the N-terminal truncated TK. The secondary structures could be clearly assigned and fitted to the density. The comparison between crystallographically determined structure and the model shows that nearly 70% of the HSV1 TK structure has been correctly modelled by the described integrated approach to knowledge based ligand protein complex structure prediction. This indicate that computer assisted methods, combined with "manual" correction both for alignment and 3D construction are useful and can be successful.

Crystallography, X-Ray↗
PubMed1989

Diagnostic criteria for hospitalized acute myocardial infarction: the Minnesota experience.

Standardized diagnostic algorithms are needed for systematic surveillance of hospitalized acute myocardial infarction (AMI). Ambiguities in diagnostic classification are resolvable to the extent that objective information is available in the hospital chart. In this study of diagnostic algorithms, serum cardiac enzyme levels, especially creatine kinase total (CK-TOT) and creatine kinase myocardial band (CK-MB) isoenzyme, were most closely correlated with the physician-reviewer diagnostic assignment used for validation; chest pain and electrocardiographic findings were less closely correlated. In addition, a close relationship was noted between the clinician's diagnostic impression and testing procedures and the final hospital discharge diagnosis. Thus, the algorithm should include discharge diagnosis as a classification element. The algorithm for cases discharged as acute myocardial infarction should be very sensitive, tending to call cases acute myocardial infarction. Other discharge diagnoses may harbour some clinically unrecognized myocardial infarction cases; however, the algorithm for such cases should be restrictive and specific to minimize false positives. These findings indicate optimal ways of combining clinical characteristics to most completely and accurately identify cases of acute myocardial infarction based on hospital records examined in retrospect.

Algorithms↗
PubMedDate not supplied

Metatarsophalangeal joint capsule tears: an analysis by arthrography, a new classification system and surgical management.

Metatarsalgia is a common presenting symptom with an established list of differential diagnoses. The authors present a classification system and surgical treatment algorithm for chronic metatarsophalangeal pain due to metatarsophalangeal joint capsule tear. A series of 58 metatarsophalangeal joints with partial tear diagnosed by arthrogram and treated by surgical repair are reviewed. The authors propose a classification system based on preoperative arthrography and a surgical repair procedure for each type of three distinct patterns. A study was developed and funded to perform postoperative arthrograms on 15 patients who had undergone surgical repair using the procedures presented. The purpose of the study was to validate the utility of the arthrogram in the diagnosis and clarification of the nature of the capsular tear. The authors were also able to demonstrate that the arthrographic findings became normal postoperatively, and that surgical repair of a seemingly innocuous capsule tear relieves pain. Fifty-six patients in the series reported relief of their preoperative symptoms. Postoperative arthrograms in 15 patients demonstrated a normal pattern in 73%, 20% had decreased extravasation, and 7% were unchanged.

Adult↗
PubMed1998

Bayesian protein family classifier.

A Bayesian procedure for the simultaneous alignment and classification of sequences into subclasses is described. This Gibbs sampling algorithm iterates between an alignment step and a classification step. It employs Bayesian inference for the identification of the number of conserved columns, the number of motifs in each class, their size, and the size of the classes. Using Bayesian prediction, inter-class differences in all these variables are brought to bare on the classification. Application to a superfamily of cyclic nucleotide-binding proteins identifies both similarities and differences in the sequence characteristics of the five subclasses identified by the procedure: 1) cNMP-dependent kinases, 2) prokaryotic cAMP-dependent regulatory proteins, CRP-type, 3) prokaryotic regulatory proteins, FNR-type, 4) cAMP gated ion channel proteins of animals, and 5) cAMP gated ion channels of plants.

Algorithms↗
PubMed1997

Supervised Learning Extensions to the CLAM Network.

The contextual layered associative memory (CLAM) has been developed as a self-generating structure which implements a probabilistic encoding scheme. The training algorithms are geared towards the unsupervised generation of a layerable associative mapping ([Thacker and Mayhew, 1989]). We show here that the resulting structure will support layers which can be trained to produce outputs that approximate conditional probabilities of classification. Unsupervised and supervised learning algorithms operate independently permitting the unsupervised representational layer to be developed before supervision is available. The system thus supports learning which is inherently more flexible than conventional node labelling schemes. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Journal Article↗
PubMed2001

[Radiologic screening for lung cancer: present status and future perspectives].

Radiologic screening for lung cancer: present status and future perspectives. Lung cancer is the most common cause of death from malignancy. This is predominantly due to the poor prognosis of the mostly advanced tumor stages at the time of presentation. Prognosis of early, usually asymptomatic stages is more favourable, particularly in non-small-cell histologic types. Therefore, early detection using diagnostic tests promises reduction of mortality from lung cancer. Due to its high sensitivity for small pulmonary nodules - the most common manifestation of early lung cancer - computed tomography appears suitable as a screening test particularly as the high radiation exposure associated with standard examination protocols can be significantly reduced for this purpose. Due to the high prevalence of benign small pulmonary nodules diagnostic algorithms are required for non-invasive classification of detected nodules. Preliminary studies of low-dose CT using algorithms based on size and density of detected nodules revealed a high proportion of asymptomatic lung cancers and early resectable tumor stages with a small number of invasive procedures for benign nodules. Prior to a wide application of this technique in clinical routine more data is required as to appropriate inclusion criteria, screening intervals and most importantly the effect of screening on reduction of mortality from lung cancer.

Adult↗
PubMed1997

Community-acquired pneumonia: can it be defined with claims data?

The use of administrative data to study pneumonia is limited because International Classification of Diseases, 9th revision, Clinical Modification (ICD9-CM) diagnosis codes do not specify whether pneumonia is community-acquired (CAP), a key clinical distinction. We classified 212 patients discharged with a diagnosis code for pneumonia as to whether or not they had CAP, using three administrative data-based systems (Diagnosis Related Groups (DRGs) alone, principal diagnosis alone, and a complex algorithm). We examined agreement with classification by clinician chart review. We also compared the length of stay (LOS) and mortality among the CAP populations identified with different methods. Agreement between the clinical review and the three administrative data methods ranged from 86 to 80%. Classification by DRG performed least well. Populations defined by claims data had similar mortality but shorter mean LOS (9.70, 9.40, and 7.91 days for the algorithm, principal diagnosis and DRG methods, respectively) than the clinically defined population (10.85 days). We conclude that studies of CAP using populations identified by claims may underestimate LOS.

Adult↗
PubMed2002

Strong feature sets from small samples.

For small samples, classifier design algorithms typically suffer from overfitting. Given a set of features, a classifier must be designed and its error estimated. For small samples, an error estimator may be unbiased but, owing to a large variance, often give very optimistic estimates. This paper proposes mitigating the small-sample problem by designing classifiers from a probability distribution resulting from spreading the mass of the sample points to make classification more difficult, while maintaining sample geometry. The algorithm is parameterized by the variance of the spreading distribution. By increasing the spread, the algorithm finds gene sets whose classification accuracy remains strong relative to greater spreading of the sample. The error gives a measure of the strength of the feature set as a function of the spread. The algorithm yields feature sets that can distinguish the two classes, not only for the sample data, but for distributions spread beyond the sample data. For linear classifiers, the topic of the present paper, the classifiers are derived analytically from the model, thereby providing an enormous savings in computation time. The algorithm is applied to cancer classification via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the algorithm is used to find gene sets whose expressions can be used to classify BRCA1 and BRCA2 tumors.

Breast Neoplasms↗
PubMedDate not supplied

A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores.

Shotgun tandem mass spectrometry-based peptide sequencing using programs such as SEQUEST allows high-throughput identification of peptides, which in turn allows the identification of corresponding proteins. We have applied a machine learning algorithm, called the support vector machine, to discriminate between correctly and incorrectly identified peptides using SEQUEST output. Each peptide was characterized by SEQUEST-calculated features such as delta Cn and Xcorr, measurements such as precursor ion current and mass, and additional calculated parameters such as the fraction of matched MS/MS peaks. The trained SVM classifier performed significantly better than previous cutoff-based methods at separating positive from negative peptides. Positive and negative peptides were more readily distinguished in training set data acquired on a QTOF, compared to an ion trap mass spectrometer. The use of 13 features, including four new parameters, significantly improved the separation between positive and negative peptides. Use of the support vector machine and these additional parameters resulted in a more accurate interpretation of peptide MS/MS spectra and is an important step toward automated interpretation of peptide tandem mass spectrometry data in proteomics.

Algorithms↗
PubMed1999

Boosted mixture of experts: an ensemble learning scheme.

We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hintnon, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classifical ensemble approaches.

Algorithms↗
PubMed2004

Classification of normal and dysphagic swallows by acoustical means.

This paper proposes a noninvasive, acoustic-based method to differentiate between individuals with and without dysphagia or swallowing dysfunction. Swallowing sound signals, both normal and abnormal (i.e., at risk of some degree of dysphagia) were recorded with accelerometers over the trachea. Segmentation based on waveform dimension trajectory (a distance-based technique) was developed to segment the nonstationary swallowing sound signals. Two characteristic sections emerged, Opening and Transmission, and 24 characteristic features were extracted and subsequently reduced via discriminant analysis. A discriminant algorithm was also employed for classification, with the system trained and tested using the leave-one-out approach. Overall, 350 signals were used from three bolus consistencies (semisolid, thick and thin liquids). A final screening algorithm correctly classified 13 of 15 control subjects and 11 of 11 subjects with some degree of dysphagia and/or neurological impairments. The proposed method has great potential to reduce the need for videofluoroscopic swallowing studies (the current gold standard method for swallowing assessment, which is invasive and nonportable) and to assist in the overall clinical assessment of swallowing sound signals.

Adolescent↗
PubMed1997

An artificial intelligent diagnostic system on differential recognition of hematopoietic cells from microscopic images.

Despite their advantages, none of the automated white blood cell differentiated counters have replaced the conventional microscopic evaluations of blood and bone marrow slides by hematologists. We have analyzed the smears of 39 patients and 8 control subjects to develop an artificial expert system that recognizes 16 different types of nucleated hematopoietic cells during the stages of differentiation. A charge coupled television camera and a special frame grabber were used for data acquisition, and 247 nucleated cell images were transferred from a microscope to an IBM 386 computer to be processed. One hundred sixty-five and 82 of these images were used for training and testing, respectively. Our system is composed of image processing and analysis (enhancement, thresholding/smoothing, edge detection), pattern recognition (feature extraction and classification with supervised artificial neural network), and expert system development. Image processing and analysis were used to obtain 13 cellular features to be used as the input parameters (neurons) of the artificial neural network. A supervised artificial neural network (back-propagation learning algorithm) was used in the classification of 16 different cells (output neurons of the neural network), which is the second step of pattern recognition. A confusion matrix has been developed to compare the similarities and dissimilarities between the differential recognitions of the hematologist and the expert system. The discriminatory power of the procedure is statistically significant: Q = (N - n.K)2/N.(K - 1) = 28.2. The sensitivity and the specificity of the expert system were 71.4% and 90.9%, respectively.

Bone Marrow↗
PubMedDate not supplied

Cluster analysis by testing the statistical hypothesis of uniformity.

This paper presents a cluster algorithm that defines the number of clusters and allows classification of data points. The basic task of the algorithm is to identify accumulations of vectors in the analysis sample of vectors. The accumulations of vectors are determined by testing the statistical hypothesis of uniformity. On the basis of accumulations, clusters are formed.

Algorithms↗
PubMed1993

Likelihood linkage analysis (LLA) classification method: an example treated by hand.

This paper describes a very general method of data analysis using a hierarchical classification. The data can be provided by observation, experiment or knowledge; their nature can be numerical, qualitative or logical. First, the classical view of the context of data representation, in which the algorithm of hierarchical ascendant construction of the classification tree is set, is treated in a synthetic manner. The main notion in our method is one of 'similarity'. This must be elaborated in the best way, taking into account the mathematical nature of the objects to be compared. Here we adopt a set of theoretical and combinatorial representation of the descriptive attributes, which are interpreted in terms of relations. Then we introduce a probability scale for similarity measurement by using a likelihood concept. The largest part of the paper concerns an illustrating example, moderately sized, detailing very minutely the different steps and the different calculations assumed by the method. The data structure handled with this example is the simplest possible. Then, general aspects and methodological extensions are evoked. We end by indicating the interest of the described approach in future works, in which we are involved, concerning typological organization of genetic sequences. We emphasize the 'explanation' aspect of the obtained results, with respect to a given description. For this purpose, classifications (on the object set and on the attribute set) on the one hand and machine learning techniques on the other, intervene efficiently.

Algorithms↗
PubMed1993

Band features as classification measures for G-banded chromosome analysis.

Modern automatic and semiautomatic karyotyping systems employ algorithms that use chromosome length and centromeric index as well as other intact chromosome measures. These measures offer correct classification rates near 95%. An algorithm is presented that utilizes local dark band features and position (position from one end of the chromosome, band-width, band-height above light band background, integrated optical density above light band background, and a shape feature) and is based on maximum likelihood of the multivariate normal distribution for the feature vector. The algorithm was tested on two data sets: 179 metaphases from C. Lundsteen at the Rigshospitalet, Copenhagen, and 50 metaphases from The University of Texas M. D. Anderson Cancer Center. The Copenhagen set achieved an overall correct classification rate of 94.6% when classifying itself, a rate comparable to other algorithms. This classifier relies on local band features rather than global chromosome characteristics and is therefore directly extensible to metaphase and prophase chromosome subsegments and to structural abnormalities.

Algorithms↗
PubMed2004

BCI Competition 2003--Data set III: probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements.

Brain-computer interfaces require effective online processing of electroencephalogram (EEG) measurements, e.g., as a part of feedback systems. We present an algorithm for single-trial online classification of imaginary left and right hand movements, based on time-frequency information derived from filtering EEG wideband raw data with causal Morlet wavelets, which are adapted to individual EEG spectra. Since imaginary hand movements lead to perturbations of the ongoing pericentral mu rhythm, we estimate probabilistic models for amplitude modulation in lower (10 Hz) and upper (20 Hz) frequency bands over the sensorimotor hand cortices both contra- and ipsilaterally to the imagined movements (i.e., at EEG channels C3 and C4). We use an integrative approach to accumulate over time evidence for the subject's unknown motor intention. Disclosure of test data labels after the competition showed this approach to succeed with an error rate as low as 10.7%.

Algorithms↗
PubMed1989

Absence of the septum pellucidum: a useful sign in the diagnosis of congenital brain malformations.

In a review of more than 2000 MR images of the brain we identified 35 patients with absence of the septum pellucidum. These patients were divided into seven basic groups as follows: septooptic dysplasia; schizencephaly; holoprosencephaly; agenesis of the corpus callosum; chronic, severe hydrocephalus; basilar encephaloceles; and porencephaly/hydranencephaly. Absence of the septum pellucidum was never seen as an isolated finding. By using data gathered from the review of the MR scans of patients in this study, we devised a diagnostic algorithm to aid in the classification of these patients. Absence of the septum pellucidum can provide a valuable clue to the diagnosis of malformations of the brain.

Abnormalities, Multiple↗