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PubMedDate not supplied

Robust feature detection and local classification for surfaces based on moment analysis.

The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.

Algorithms↗

PubMed2004

Comparison of two exploratory data analysis methods for fMRI: unsupervised clustering versus independent component analysis.

Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, "neural gas" network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised Clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.

Adult↗

PubMed2006

A support method for the contextual interpretation of biomechanical data.

In the clinical field, biomechanical data provided by advanced technical devices are still underexploited. Data analysis usually consists of extracting attributes or computing synthetic values from temporal data and exploiting them by means of a monovariable statistical method. This article proposes a method to support clinicians, especially those in orthopedics, in the contextual interpretation of biomechanical data. We propose to characterize temporal biomechanical data by means of fuzzy space-time windows and to induce fuzzy decision trees to map the biomechanical and clinical data related to patients. Then, we present a method for objectively explaining a given clinical characteristic of a particular patient; this method is derived using the fuzzy rule base generated from the trees and a satisfiability measure. We have applied our method to real data in order to provide an objective explanation of the subjective self-evaluation of the functional status of patients with a shoulder prosthesis, and evaluate it by means of the stratified tenfold cross validation method. The mean explanation rate--which corresponds to the mean proportion of the patients belonging to test sets whose functional state is explained by the proposed method--exceeds 80% for more than half of the decision trees, and exceeds 70% for 94% of the trees. By supporting clinicians during the biomechanical data interpretation process, our method helps them take the objective biomechanical measurements in the medical practice into account, particularly in orthopedics. It can also make subjective evaluations more objective by mapping subjective and objective data.

Algorithms↗

PubMed2004

Analysis and prediction of affinity of TAP binding peptides using cascade SVM.

The generation of cytotoxic T lymphocyte (CTL) epitopes from an antigenic sequence involves number of intracellular processes, including production of peptide fragments by proteasome and transport of peptides to endoplasmic reticulum through transporter associated with antigen processing (TAP). In this study, 409 peptides that bind to human TAP transporter with varying affinity were analyzed to explore the selectivity and specificity of TAP transporter. The abundance of each amino acid from P1 to P9 positions in high-, intermediate-, and low-affinity TAP binders were examined. The rules for predicting TAP binding regions in an antigenic sequence were derived from the above analysis. The quantitative matrix was generated on the basis of contribution of each position and residue in binding affinity. The correlation of r = 0.65 was obtained between experimentally determined and predicted binding affinity by using a quantitative matrix. Further a support vector machine (SVM)-based method has been developed to model the TAP binding affinity of peptides. The correlation (r = 0.80) was obtained between the predicted and experimental measured values by using sequence-based SVM. The reliability of prediction was further improved by cascade SVM that uses features of amino acids along with sequence. An extremely good correlation (r = 0.88) was obtained between measured and predicted values, when the cascade SVM-based method was evaluated through jackknife testing. A Web service, TAPPred (http://www.imtech.res.in/raghava/tappred/ or http://bioinformatics.uams.edu/mirror/tappred/), has been developed based on this approach.

ATP-Binding Cassette Transporters↗

PubMed2006

Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware.

Statistical reconstruction methods offer possibilities to improve image quality as compared with analytical methods, but current reconstruction times prohibit routine application in clinical and micro-CT. In particular, for cone-beam x-ray CT, the use of graphics hardware has been proposed to accelerate the forward and back-projection operations, in order to reduce reconstruction times. In the past, wide application of this texture hardware mapping approach was hampered owing to limited intrinsic accuracy. Recently, however, floating point precision has become available in the latest generation commodity graphics cards. In this paper, we utilize this feature to construct a graphics hardware accelerated version of the ordered subset convex reconstruction algorithm. The aims of this paper are (i) to study the impact of using graphics hardware acceleration for statistical reconstruction on the reconstructed image accuracy and (ii) to measure the speed increase one can obtain by using graphics hardware acceleration. We compare the unaccelerated algorithm with the graphics hardware accelerated version, and for the latter we consider two different interpolation techniques. A simulation study of a micro-CT scanner with a mathematical phantom shows that at almost preserved reconstructed image accuracy, speed-ups of a factor 40 to 222 can be achieved, compared with the unaccelerated algorithm, and depending on the phantom and detector sizes. Reconstruction from physical phantom data reconfirms the usability of the accelerated algorithm for practical cases.

Algorithms↗

PubMed2005

Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance.

Pharmacovigilance aims at detecting the adverse effects of marketed drugs. It is generally based on the spontaneous reporting of events thought to be the adverse effects of drugs. Spontaneous Reporting Systems (SRSs) supply huge databases that pharmacovigilance experts cannot exhaustively exploit without data mining tools. Data mining methods; i.e., statistical association measures in conjunction with signal generation criteria, have been proposed in the literature but there is no consensus regarding their applicability and efficiency, especially since such methods are difficult to evaluate on the basis of actual data. The objective of this paper is to evaluate association measures on simulated datasets obtained with SRS modeling. We compared association measures using the percentage of false positive signals among a given number of the most highly ranked drug-event combinations according to the values of the association measures. By considering 150 drugs and 100 adverse events, these percentages of false positives, among the 500 most highly ranked drug-event couples, vary from 1.1% to 53.4% (averages over 1000 simulated datasets). As the measures led to very different results, we could identify which measures appeared to be the most relevant for pharmacovigilance.

Adverse Drug Reaction Reporting Systems↗

PubMed2001

Generating protein three-dimensional fold signatures using inductive logic programming.

Inductive logic programming (ILP) has been applied to automatically discover protein fold signatures. This paper investigates the use of topological information to circumvent problems encountered during previous experiments, namely (1) matching of non-structurally related secondary structures and (2) scaling problems. Cross-validation tests were carried out for 20 folds. The overall estimated accuracy is 73.37+/-0.35%. The new representation allows us to process the complete set of examples, while previously it was necessary to sample the negative examples. Topological information is used in approximately 90% of the rules presented here. Information about the topology of a sheet is present in 63% of the rules. This set of rules presents characteristics of the overall architecture of the fold. In contrast, 26% of the rules contain topological information which is limited to the packing of a restricted number of secondary structures, as such, the later set resembles those found in our previous studies.

Algorithms↗

PubMedDate not supplied

Using ensembles to classify compounds for drug discovery.

This paper introduces Signal, a novel method for classifying activity against a small molecule drug target. Signal creates an ensemble, or collection, of meaningful descriptors chosen from a much larger property space. The method works with a variety of descriptor types, including fingerprints that represent four-point pharmacophores or shape descriptors. It also exploits information from both active and inactive compounds and generates predictive models suitable for high throughput screening data analysis. Given the fingerprints and activity data for a set of compounds, Signal is a two step process. The first step is to Evaluate the Descriptors: for each descriptor in the fingerprint, quantify and rank the correlation between the activity of the compounds and the presence of that descriptor. The second step is to Create an Ensemble Model: use the high ranking descriptors to create a model of activity against the biological target. For the first step, two possible ranking strategies were investigated: mutual information and chi-square. For the second step, two types of ensemble models were investigated: high ranking and a novel method called high ranking set cover. Of the four possible pairings, the combination of chi-square and high ranking set cover performed the best on a Thrombin data set.

Algorithms↗

PubMedDate not supplied

Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds.

This paper explores the utility of data mining and machine learning algorithms for the induction of mutagenicity structure-activity relationships (SARs) from noncongeneric data sets. We compare (i) a newly developed algorithm (MOLFEA) for the generation of descriptors (molecular fragments) for noncongeneric compounds with traditional SAR approaches (molecular properties) and (ii) different machine learning algorithms for the induction of SARs from these descriptors. In addition we investigate the optimal parameter settings for these programs and give an exemplary interpretation of the derived models. The predictive accuracies of models using MOLFEA derived descriptors is approximately 10-15%age points higher than those using molecular properties alone. Using both types of descriptors together does not improve the derived models. From the applied machine learning techniques the rule learner PART and support vector machines gave the best results, although the differences between the learning algorithms are only marginal. We were able to achieve predictive accuracies up to 78% for 10-fold cross-validation. The resulting models are relatively easy to interpret and usable for predictive as well as for explanatory purposes.

Algorithms↗

PubMed2006

[Name-based identification of cases of Turkish origin in the childhood cancer registry in Mainz].

Until now few analyses of routine data relating to the health of migrants have been conducted in Germany. A major obstacle is that most data sources do not provide reliable information on the origin of migrants. While some sources contain the nationality of persons registered, this information does not allow one to identify migrants who have taken up German citizenship, i.e., a substantial part of second-generation migrants. In this paper we demonstrate how a computer-aided, name-based algorithm can be used to identify persons of Turkish origin in the German Childhood Cancer Registry in Mainz, Germany. The performance of the algorithm, as assessed against the gold standard of assessing names manually, was very good (sensitivity and specificity > or = 0.975). In total, we identified 1774 of the 37,259 cases in the registry as being of Turkish origin. The name algorithm proved to be a useful tool to identify Turkish migrants in routine data sources, thus avoiding potential bias due to changes in citizenship. This approach aims at improving migrant-sensitive health reporting and research in Germany. In future, additional information on migrant status should be obtained already during primary data collection so that health data for all migrant groups can be provided.

Algorithms↗

PubMed2004

Supervised machine learning techniques for the classification of metabolic disorders in newborns.

MOTIVATION: During the Bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. Owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in high-dimensional metabolic data which form the source for constructing classification rules with high discriminatory power. RESULTS: Six machine learning techniques have been investigated for their classification accuracy focusing on two metabolic disorders, phenylketo nuria (PKU) and medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Logistic regression analysis led to superior classification rules (sensitivity >96.8%, specificity >99.98%) compared to all investigated algorithms. Including novel constellations of metabolites into the models, the positive predictive value could be strongly increased (PKU 71.9% versus 16.2%, MCADD 88.4% versus 54.6% compared to the established diagnostic markers). Our results clearly prove that the mined data confirm the known and indicate some novel metabolic patterns which may contribute to a better understanding of newborn metabolism.

Algorithms↗

PubMed2005

A probabilistic model for mining implicit 'chemical compound-gene' relations from literature.

MOTIVATION: The importance of chemical compounds has been emphasized more in molecular biology, and 'chemical genomics' has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit 'chemical compound and gene' relations from the co-occurrence in the literature. RESULTS: We propose a probabilistic model, called the mixture aspect model (MAM), and an algorithm for estimating its parameters to efficiently handle different types of co-occurrence datasets at once. We examined the performance of our approach not only by a cross-validation using the data generated from the MEDLINE records but also by a test using an independent human-curated dataset of the relationships between chemical compounds and genes in the ChEBI database. We performed experimentation on three different types of co-occurrence datasets (i.e. compound-gene, gene-gene and compound-compound co-occurrences) in both cases. Experimental results have shown that MAM trained by all datasets outperformed any simple model trained by other combinations of datasets with the difference being statistically significant in all cases. In particular, we found that incorporating compound-compound co-occurrences is the most effective in improving the predictive performance. We finally computed the likelihoods of all unknown compound-gene (more specifically, drug-gene) pairs using our approach and selected the top 20 pairs according to the likelihoods. We validated them from biological, medical and pharmaceutical viewpoints.

Artificial Intelligence↗

PubMed2004

Biological nomenclatures: a source of lexical knowledge and ambiguity.

There has been increased work in developing automated systems that involve natural language processing (NLP) to recognize and extract genomic information from the literature. Recognition and identification of biological entities is a critical step in this process. NLP systems generally rely on nomenclatures and ontological specifications as resources for determining the names of the entities, assigning semantic categories that are consistent with the corresponding ontology, and assignment of identifiers that map to well-defined entities within a particular nomenclature. Although nomenclatures and ontologies are valuable for text processing systems, they were developed to aid researchers and are heterogeneous in structure and semantics. A uniform resource that is automatically generated from diverse resources, and that is designed for NLP purposes would be a useful tool for the field, and would further database interoperability. This paper presents work towards this goal. We have automatically created lexical resources from four model organism nomenclature systems (mouse, fly, worm, and yeast), and have studied performance of the resources within an existing NLP system, GENIES. Using nomenclatures is not straightforward because issues concerning ambiguity, synonymy, and name variations are quite challenging. In this paper we focus mainly on ambiguity. We determined that the number of ambiguous gene names within the individual nomenclatures, across the four nomenclatures, and with general English ranged from 0%-10.18%, 1.187%-20.30%, and 0%-2.49% respectively. When actually processing text, we found the rate of ambiguous occurrences (not counting ambiguities stemming from English words) to range from 2.4%-32.9% depending on the organisms considered.

Artificial Intelligence↗

PubMed2006

Novel knowledge-based mean force potential at the profile level.

BACKGROUND: The development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials. RESULTS: The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Phi/Psi dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5-6%). The contact and Phi/Psi dihedral angle potential only get a slight improvement (1-2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading. CONCLUSION: The knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement.

Algorithms↗

PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗

PubMed2005

An adaptive incremental LBG for vector quantization.

This study presents a new vector quantization method that generates codewords incrementally. New codewords are inserted in regions of the input vector space where the distortion error is highest until the desired number of codewords (or a distortion error threshold) is achieved. Adoption of the adaptive distance function greatly increases the proposed method's performance. During the incremental process, a removal-insertion technique is used to fine-tune the codebook to make the proposed method independent of initial conditions. The proposed method works better than some recently published efficient algorithms such as Enhanced LBG (Patane, & Russo, 2001) for traditional tasks: with fixed number of codewords, to find a suitable codebook to minimize distortion error. The proposed method can also be used for new tasks that are insoluble using traditional methods: with fixed distortion error, to minimize the number of codewords and find a suitable codebook. Experiments for some image compression problems indicate that the proposed method works well.

Algorithms↗

PubMed2005

Pulsed Doppler signal processing for use in mice: applications.

We have developed a high-frequency, high-resolution Doppler spectrum analyzer (DSPW) and compared its performance against an adapted clinical Medasonics spectrum analyzer (MSA) and a zero-crossing interval histogram (ZCIH) used previously by us to evaluate cardiovascular physiology in mice. The aortic velocity (means +/- SE: 92.7 +/- 2.5 versus 82.2 +/- 1.8 cm/s) and aortic acceleration (8194 +/- 319 versus 5178 +/- 191 cm/s2) determined by the DSPW were significantly higher compared to those by the MSA. Aortic ejection time was shorter (48.3 +/- 0.9 versus 64.6 +/- 1.8 ms) and the isovolumic relaxation was longer (17.6 +/- 0.6 versus 13.5 +/- 0.6 ms) when determined by the DSPW because it generates shorter temporal widths in the velocity spectra when compared to the MSA. These data indicate that the performance of the DSPW in evaluating cardiovascular physiology was better than that of the MSA. There were no significant differences between the aortic pulse wave velocity determined by using the ZCIH (391 +/- 16 cm/s) and the DSPW (394 +/- 20 cm/s). Besides monitoring cardiac function, we have used the DSPW for studying peripheral vascular physiology in normal, transgenic, and surgical models of mice. Several applications such as the detection of high stenotic jet velocities (> 4 m/s), vortex shedding frequencies (250 Hz), and subtle changes in wave shapes in peripheral vessels which could not obtained with clinical Doppler systems are now made possible with the DSPW.

Algorithms↗

PubMed2000

Ontology acquisition from on-line knowledge sources.

Electronic knowledge representation is becoming more and more pervasive both in the form of formal ontologies and less formal reference vocabularies, such as UMLS. The developers of clinical knowledge bases need to reuse these resources. Such reuse requires a new generation of tools for ontology development and management. Medical experts with little or no computer science experience need tools that will enable them to develop knowledge bases and provide capabilities for directly importing knowledge not only from formal knowledge bases but also from reference terminologies. The portions of knowledge bases that are imported from disparate resources then need to be merged or aligned to one another in order to link corresponding terms, to remove redundancies, to resolve logical conflicts. We discuss the requirements for ontology-management tools that will enable interoperability of disparate knowledge sources. Our group is developing a suite of tools for knowledge-base management based on the Protégé-2000 environment for ontology development and knowledge acquisition. We describe one such tool in detail here: an application for incorporating information from remote knowledge sources such as UMLS into a Protégé knowledge base.

Artificial Intelligence↗