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PubMed2003

Geometrical bounding of data space and nonlinear classification of chemical data using MPGA algorithm.

This paper develops a multi-parturition genetic algorithm (MPGA) to be used in geometrical bounding of the overlapped clusters in a data set for the classification of chemical data. Two new operators have been introduced to modify the conventional genetic algorithm, namely, multi-parturition and decimation and orientated creation to improve the linear classification results and diminish the computational time. To circumvent the difficulty commonly encountered in the treatment of linearly inseparable chemical data sets, the optimized linear classifier is further modified to provide a complementary nonlinear classifier. For this reason the space regions of the overlapped clusters have been bounded by erection of half-hyperellipsoids over the linearly misclassified patterns. The proposed MPGA was applied to classify a number of chemical and other data sets with a dimension from 4 to 14. Experimental results have indicated that the proposed MPGA could classify seriously overlapped data sets with an acceptable error rate.

Journal Article↗
PubMed2003

General-purpose computation with neural networks: a survey of complexity theoretic results.

We survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic versus probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issues.

Algorithms↗
PubMed2004

Multistage classification and recognition that employs vector quantization coding and criteria extracted from nonorthogonal and preprocessed signal representations.

Classification decision tree algorithms have recently been used in pattern-recognition problems. In this paper, we propose a self-designing system that uses the classification tree algorithms and that is capable of recognizing a large number of signals. Preprocessing techniques are used to make the recognition process more effective. A combination of the original, as well as the preprocessed, signals is projected into different transform domains. Enormous sets of criteria that characterize the signals can be developed from the signal representations in these domains. At each node of the classification tree, an appropriately selected criterion is optimized with respect to desirable performance features such as complexity and noise immunity. The criterion is then employed in conjunction with a vector quantizer to divide the signals presented at a particular node in that stage into two approximately equal groups. When the process is complete, each signal is represented by a unique composite binary word index, which corresponds to the signal path through the tree, from the input to one of the terminal nodes of the tree. Experimental results verify the excellent classification accuracy of this system. High performance is maintained for both noisy and corrupt data.

Journal Article↗
PubMed2006

A fast SCOP fold classification system using content-based E-Predict algorithm.

BACKGROUND: Domain experts manually construct the Structural Classification of Protein (SCOP) database to categorize and compare protein structures. Even though using the SCOP database is believed to be more reliable than classification results from other methods, it is labor intensive. To mimic human classification processes, we develop an automatic SCOP fold classification system to assign possible known SCOP folds and recognize novel folds for newly-discovered proteins. RESULTS: With a sufficient amount of ground truth data, our system is able to assign the known folds for newly-discovered proteins in the latest SCOP v1.69 release with 92.17% accuracy. Our system also recognizes the novel folds with 89.27% accuracy using 10 fold cross validation. The average response time for proteins with 500 and 1409 amino acids to complete the classification process is 4.1 and 17.4 seconds, respectively. By comparison with several structural alignment algorithms, our approach outperforms previous methods on both the classification accuracy and efficiency. CONCLUSION: In this paper, we build an advanced, non-parametric classifier to accelerate the manual classification processes of SCOP. With satisfactory ground truth data from the SCOP database, our approach identifies relevant domain knowledge and yields reasonably accurate classifications. Our system is publicly accessible at http://ProteinDBS.rnet.missouri.edu/E-Predict.php.

Algorithms↗
PubMedDate not supplied

Determining the validity of a QSAR model--a classification approach.

The determination of the validity of a QSAR model when applied to new compounds is an important concern in the field of QSAR and QSPR modeling. Various scoring techniques can be applied to specific types of models. We present a technique with which we can state whether a new compound will be well predicted by a previously built QSAR model. In this study we focus on linear regression models only, though the technique is general and could also be applied to other types of quantitative models. Our technique is based on a classification method that divides regression residuals from a previously generated model into a good class and bad class and then builds a classifier based on this division. The trained classifier is then used to determine the class of the residual for a new compound. We investigated the performance of a variety of classifiers, both linear and nonlinear. The technique was tested on two data sets from the literature and a hand built data set. The data sets selected covered both physical and biological properties and also presented the methodology with quantitative regression models of varying quality. The results indicate that this technique can determine whether a new compound will be well or poorly predicted with weighted success rates ranging from 73% to 94% for the best classifier.

Algorithms↗
PubMed2003

[Algorithm for application of the "ethical guidelines for epidemiological research" and taxonomy of public health research].

BACKGROUND: "Ethical Guidelines for Epidemiological Research" took effect in July 2002, with a moral duty of all researchers to comply when conducting epidemiological studies although it is not legally binding. Public health research entails various forms of studies including not only epidemiological studies but also attention to psychological, societal and economic aspects, which are outside of the jurisdiction of the guidelines. Hence, confusion may arise among members of Japanese Society of Public Health as to whether the study they conduct falls within the definition of epidemiological research. The author discusses legal interpretations of the guidelines arising in the course of translation work as part of government-funded project, "Dissemination of the 'Ethical Guidelines for Epidemiological Research' via Internet (principal investigator: Toru Doi)" and argues that a case-method approach would be best suited to enhance understanding by researchers with diverse, non-legal backgrounds. METHODS: The author proposes an algorithm for classification of studies as to whether the guideline applies, and applies it to all original articles published in the Japanese Journal of Public Health (JJPH) in one year (March 2002 thru February 2003). The rationale for classification is discussed from the strict legal viewpoint in each case. RESULTS: Sixteen out of 46 original articles published in JJPH for one year were classified as epidemiological studies to which the guidelines apply. Those classified otherwise were psychological studies (10), epidemiological studies not targeting specific diseases and are exempt form the guidelines (3), purely methodological studies (4), economics studies (3), fact-finding or opinion surveys with no hypothesis testing (2), as well as studies authorized by law (4) or using unlinkable anonymous data only (4), all of which are exempt from the guidelines. Reference to ethical considerations in the methodology section as required by the instructions for authors was generally well performed in epidemiological studies although some shortcomings were noted. DISCUSSIONS: The guidelines affect approximately a third of original articles published in the JJPH. The ratio of applicable to non-applicable articles was roughly 1 to 2. This gives both authors and reviewers confusion as to whether articles are subject to the guidelines and would require ethical approval. This case-method approach using actual articles readily available to members should help unnecessary confusion and enhance appropriate application of the guidelines. (366 words).

Algorithms↗
PubMed2001

Approach to sinus of Valsalva aneurysms: a review of 53 cases.

OBJECTIVE: The reported experience with sinus of Valsalva aneurysms (SVAs) is limited. Our approach to this subset of patients and an algorithm-dependent classification are presented. METHODS: Between 1985 and 2000, 53 patients (mean age: 24+/-12; range 4--60) underwent repair for ruptured (64%) or non-ruptured (36%) SVA. Associated lesions were present in 21 patients; VSD in 18, moderate to severe aortic insufficiency in five, aortic stenosis in four (two subaortic membrane and one bicuspid valve), PDA in two, mitral insufficiency in one, tetralogy of Fallot in one and endocarditis in one. Operative procedures included simple or Teflon pledgetted direct suturing (31 cases; 58%), patch repair (21 cases; 40%), and stentless porcine bioprosthetic aortic root replacement in a case with extensive involvement and aortic root distortion (2%). Concomitant procedures were VSD repair in 18 patients, aortic valve replacement in four, aortic valve resuspension in three, subaortic membrane resection in two, PDA ligation in two, mitral annuloplasty in one and total correction in one. RESULTS: Early mortality was 1.9%. A permanent pacemaker was inserted in one patient due to complete heart block. The survivors were followed up for 8.2+/-5 years (range: 21 days to 15 years). There were three reoperations due to suture dehiscence; patch repair was undertaken in these patients with no further unfavorable consequences. All patients were in NYHA Class I or II as of their last follow-up. CONCLUSIONS: Repair of SVA can be performed with an acceptably low operative risk and a good symptom-free long-term outcome expectation. Echocardiography provides all the necessary details for diagnosis. Dual exposure/patch repair strategy is advocated in the ruptured cases.

Adult↗
PubMed2004

Identification of patients with head and neck cancer using serum protein profiles.

BACKGROUND: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. OBJECTIVE: To determine if a new proteomic technology can correctly identify protein expression profiles for cancer in patient serum samples as well as detect the presence of a known tumor marker. DESIGN: Direct proteomic analysis and comparison. METHODS: The surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF) ProteinChip system was used to screen for differentially expressed proteins in serum samples from 99 patients with HNSCC, 25 "healthy" smokers, and 102 healthy (normal) controls. Protein peak clustering and classification analyses of the SELDI spectral data were performed. RESULTS: Several proteins, with masses ranging from 2778 to 20,800 Da, were differentially expressed between patients with HNSCC and the normal controls. The serum protein expression profiles were used to develop a classification tree algorithm, which achieved a sensitivity of 83.3% and a specificity of 90% in discriminating HNSCC from normal and healthy smoker controls. The positive and negative predictive values were 80% and 92%, respectively. A peak with an average mass of 10,068 Da was detected in sera from HNSCC patients and identified as the known biomarker metallopanstimulin-1 (MPS-1), based on mass. Peak relative intensity of the 10,068-Da protein correlated consistently with MPS-1 levels detected by radioimmunoassay in serum samples of HNSCC patients and controls. The 10,068-Da peak was provisionally identified as MPS-1 by SELDI immunoassay. CONCLUSION: We propose that this technique may allow for the development of a reliable screening test for the early detection and diagnosis of HNSCC, as well as the potential identification of tumor biomarkers.

Biomarkers, Tumor↗
PubMed2003

Regional atrophy in the brain of lissencephalic mouse lemur primates: measurement by automatic histogram-based segmentation of MR images.

Age-related regional cerebral atrophy was evaluated in a small lissencephalic primate, model of cerebral aging. Twelve mouse lemurs (Microcebus murinus), ages 1.9-10.9 years (maximum life span: 12 years), were studied. 3D inversion-recovery fast spin-echo MR images (isotropic resolution = 234 mum) were recorded at 4.7 T with a surface coil actively decoupled from the transmitting birdcage probe. The surface coil-related sensitivity gradient was corrected by normalization with images from an agar and NaCl phantom. An automatic statistical segmentation technique based on a classification-maximization algorithm was tested on digital phantoms that mimicked the brain and applied to the 3D brain images. Segmented 3D maps that displayed gray matter, white matter, and cerebro-spinal fluid (CSF) voxels were computed. The ventricles and peri-encephalic spaces were categorized into 14 regions, defined on brain atlases on the basis of cytoarchitectural and anatomical criteria. The volume of CSF voxels belonging to each of these regions was calculated as an index of regional atrophy. Dilation of the mammillary fossa was an early event in the aging process. CSF accumulation within the occipital, parieto-temporal, temporal, and frontal ventral peri-encephalic spaces was particularly marked in the oldest animals that also displayed ophthalmologic alterations.

Aging↗
PubMedDate not supplied

Biosystematics and diversity amongst novel carboxydotrophic actinomycetes.

Fifty-four carboxydotrophic actinomycetes isolated from soils and composts were compared through 119 unit characters with representative mesophilic and thermophilic streptomycetes. The data were examined using the Jaccard, pattern and simple matching coefficients and clustering achieved using the unweighted pair group method with arithmetic averages algorithm. Acceptable cophenetic correlation and test error values allowed confidence to be placed in the resultant numerical taxonomies. The carboxydotrophic actinomycetes, which were distinct from cluster-groups corresponding to the mesophilic and thermophilic streptomycetes, formed two major cluster-groups the members of which were examined for the presence of diagnostic chemical markers. All but two of the carboxydotrophic actinomycetes had a profile of chemical properties consistent with their assignment to the genus Streptomyces. Quantitative fatty acid data were examined using the SIMCA package and the two statistically significant groups obtained corresponded with the cluster-groups circumscribed in the numerical phenetic analysis. Members of the two groups were also distinguished on the basis of their phospholipid composition. The two strains that contained meso-as opposed to LL-diaminopimelic acid in their peptidoglycan also showed a distinct chemotaxonomic profile. It was concluded that the carboxydotrophic actinomycetes form a novel and taxonomically diverse group.

Actinomycetales↗
PubMed1992

Magnetoencephalography: a tool for functional brain imaging.

At present, one of the most promising windows to the functional organization of the human brain is magnetoencephalography (MEG). By mapping the magnetic field distribution outside the head the sites of neural events can be located with an accuracy of a few millimeters and the temporal evolution of the activation can be traced with a millisecond resolution. This paper reviews some forward field calculation approaches suitable for the interpretation of the brain's electromagnetic signals. Inverse modelling with multiple dipoles is described in detail. An example of the analysis of the somatosensory evoked-responses illustrates the potential of multiple signal classification (MUSIC) algorithm in finding optimal dipole positions.

Algorithms↗
PubMed2005

Implantable dual-chamber defibrillator for the selective treatment of spontaneous atrial and ventricular arrhythmias: arrhythmia incidence and device performance.

INTRODUCTION: Atrial tachyarrhythmias are a common co-morbidity in patients with an ICD indication. Recently introduced ICD's are equipped to independently detect and treat atrial and ventricular tachyarrhythmias. The purpose of this prospective study was to evaluate the incidence and termination of spontaneous atrial and ventricular tachyarrythmias in patients with a history of atrial tachyarrhythmias. METHODS AND RESULTS: Ninety patients, 70% male with an ICD indication and history of atrial tachyarrhythmia (LVEF 45 +/- 6%, [AT/AF indication 55 +/- 10, AT/VT 45 +/- 16], 46% CAD) were enrolled and 89 were implanted with a VENTAK PRIZM AVT (Guidant). Spontaneous atrial and ventricular tachyarrhythmias were printed and evaluated during an average follow-up period of 272 +/- 72 days utilizing the stored intracardial electrogram function of the device. Nineteen patients (21%) presented had only atrial tachyarrhythmias, 32 patients (36%) had both atrial and ventricular tachyarrhythmias and 18 patients (20%) had only ventricular tachyarrhythmias. Patients with only atrial tachyarrhythmias had a total of 3274 atrial episodes; 2002 terminated spontaneously, 1264 were treated with ATP and 8 with shock therapy. ATP was successful in 735 (58%) of 1264 episodes. Patients with both atrial and ventricular tachyarrhythmias had 7277 documented atrial tachyarrhythmias, 5231 terminated spontaneously, 1153 of 2009 were terminated by ATP (57.4%) and 37 by shock therapy (20 patient controlled). Atrial tachyarrhythmias identified as atrial flutter (AT) by the atrial rhythm classification (ARC) algorithm had a higher ATP conversion success rate than episodes identified as atrial fibrillation (AF); 66.7% for AT and 26.4% for AF. Patients with only ventricular tachyarrhythmias had 690 documented episodes, 401 terminated spontaneously, 248 (85.8%) were terminated by ATP and 41 by shock. CONCLUSION: Seventy-seven percent of patients with an ICD indication had spontaneous atrial and/or ventricular tachyharrhythmias within the first 6 months after ICD implantation. ATP therapy terminated 58% of all atrial tachyarrhytmias and 66.7% of the atrial flutters. The dual chamber ICD detected, classified and terminated all ventricular tacharrhythmias appropriately.

Aged↗
PubMed2000

Advances in the management of pediatric constipation.

Constipation in children is a common concern. There is no single treatment; many children do not respond and continue to have chronic problems. This lack of response is multifactorial, but it is most likely related to the fact that the exact pathophysiology of constipation in children is not known. Diagnostic criteria (Rome II classification) and algorithms proposed by the North American Society for Pediatric Gastroenterology and Nutrition (NASPGN) for evaluation and treatment of children with constipation were recently published and are summarized here. The effectiveness of new treatments such as dietary interventions, prokinetic agents, biofeedback, and polyethylene-glycol electrolyte (PEG) solutions is discussed in this review.

Biofeedback, Psychology↗
PubMed1994

The agreement metric for labeled binary trees.

Let S be a set of n objects. A binary tree of S is a binary tree whose leaves are labeled without repetition from S. The operation of pruning a tree T is that of removing some leaves from T and suppressing all inner vertices of degree 2 which are formed by this deletion. Given two trees T and U, an agreement tree is a tree that can be obtained from T as well as from U by pruning the fewest number of leaves from the two trees. A quadratic algorithm is presented for doing this and two metrics are defined based on agreement trees.

Algorithms↗
PubMed2005

Robustness, evolvability, and optimality of evolutionary neural networks.

In a typical optimization problem, the main goal is to search for the appropriate values of the variables that provide the optimal solution of the given function. In artificial neural networks (ANN), this translates to the minimization of the error surface during training such that misclassification is minimized during generalization. However, since optimal training performance does not necessarily imply optimal generalization due to the possibility of overfitting or underfitting, we developed SEPA (Structure Evolution and Parameter Adaptation) which addressed these issues by simultaneously evolving ANN structure and weights. Since SEPA primarily relies on the perturbation function to bring variation in its population, this follow-up study aims to find out SEPAs evolvability, optimality, and robustness in other perturbation functions. Our findings indicate that SEPAs optimal generalization performances are stable and robust from the effect of the different perturbation functions. This is due to the feedback loop between its architecture evolution and weight adaptation such that any shortcoming of the former is compensated by the latter, and vice versa. Our results strongly suggest that proper ANN design requires simultaneous adaptation of ANN structure and weights to avoid one-sided or bias convergence to either the weight or architecture space.

Algorithms↗
PubMed2025

Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study.

OBJECTIVE: Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts and behaviors (STB) included a limited number of data modalities and/or focused on adult populations. This study aimed to prospectively predict first-onset STB across 4-year follow-ups in adolescents using an existing STB history classification model that was previously applied to baseline data and a new machine learning model with 195 biopsychosocial features. METHOD: Participants were 7,503 unrelated adolescents (54.5% female, ages 9-11 years at baseline) from the multisite, longitudinal Adolescent Brain Cognitive Development (ABCD) Study. An existing baseline STB history classification model was applied to predict longitudinal first-onset STB in adolescents compared with healthy controls and clinical controls (individuals with a mental health disorder but no STB). A new elastic net logistic regression model with 195 features was trained on data from 14 sites (n = 5,220), and the resulting top 15 features were validated at 7 independent sites (n = 2,283). RESULTS: The previously developed model to classify STB lifetime history also prospectively predicted first-onset STB in adolescents with an area under the curve (AUC) [95% CI] of 0.73 [0.70, 0.75], p < .001, compared with healthy controls and AUC [95% CI] of 0.63 [0.60, 0.66], p < .001, compared with clinical controls. The newly trained model with top 15 features performed similarly with AUC [95% CI] of 0.73 [0.71, 0.76], p < .001, and AUC [95% CI] of 0.64 [0.60, 0.66], p < .001, for the same comparison groups. The most consistent predictors across models included female sex, sleep disturbances, and maladaptive home and school environments. CONCLUSION: The models predicted first-onset STB in adolescents with moderate accuracy. This study also confirmed the roles of well-established psychological risk factors for STB and identified several novel neurocognitive and brain imaging risk factors. Future studies should validate these models in large-scale diverse samples before clinical translation. PLAIN LANGUAGE SUMMARY: This study followed over 7,500 adolescents for 4 years and tested 2 machine learning models using psychological, social, and brain data to identify those at risk of experiencing suicidal thoughts or behaviors. Both models predicted first-time suicidal thoughts or behaviors with moderate accuracy. Key risk factors that were identified included being female, experiencing sleep problems, and negative home and school environments. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Adolescent↗
PubMed2006

Identification of protein biomarkers for schizophrenia and bipolar disorder in the postmortem prefrontal cortex using SELDI-TOF-MS ProteinChip profiling combined with MALDI-TOF-PSD-MS analysis.

This paper describes the high-throughput proteomic analysis of the dorsolateral prefrontal cortex (DLPFC) from schizophrenia (SCHIZ), bipolar (BD), and normal control cohorts from the Harvard Brain Tissue Resource Center performed using ProteinChip technology based on the surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS). The resultant profiles were utilized in classification-tree algorithms for selection of protein biomarker peaks contributing maximally to the differentiation between the examined diagnostic cohorts. Twenty-four such protein biomarker peaks were identified. All of them had lower levels in the SCHIZ cohort as compared to the BD cohort. Also, 21 of these peaks were down-regulated in the SCHIZ cohort vs. the control cohort, and 7 peaks were up-regulated in the BD cohort vs. the control cohort. The proteins constituting these biomarker peaks were recognized via matrix-assisted laser desorption time of flight/postsource decay mass spectrometry (MALDI-TOF-PSD-MS). These proteins represent a wide range of functional groups involved in cell metabolism, signaling cascades, regulation of gene transcription, protein and RNA chaperoning, and other aspects of cellular homeostasis. Finally, after statistical evaluation suggesting that the selected protein biomarkers are not significantly impacted by epidemiological/tissue storage parameters (although, influence of antipsychotic and mood stabilizing drugs could not be fully excluded), the ProteinChip-based profiling was engaged again to demonstrate that the detected SCHIZ-associated changes in the levels of our protein biomarkers could also be seen in DLPFC samples from the brain collection of the Mount Sinai Medical School/Bronx Veteran Affairs Medical Center. This study demonstrates the usefulness of ProteinChip-based SELDI-TOF protein profiling in gaining insight into the molecular pathology of SCHIZ and BD as it points to changes in protein levels characterizing these diseases.

Aged↗
PubMedDate not supplied

A hierarchical classifier using new support vector machines for automatic target recognition.

A binary hierarchical classifier is proposed for automatic target recognition. We also require rejection of non-object (non-target) inputs, which are not seen during training or validation, thus producing a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is used at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform (|FT|) features, which provide shift-invariance, initial test results on infra-red (IR) data are excellent.

Algorithms↗