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Results for “prior domain knowledge”

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At least 19 recordsLinked to original sources
PubMed2003

Prior knowledge for time domain quantification of in vivo brain or liver 31P MR spectra.

Prior knowledge is required when quantifying in vivo (31)P magnetic resonance spectra from the brain or liver. The prior knowledge system we have used models both the phosphomonoester and phosphodiester resonances as two peaks of equal linewidth and fixed relative chemical shift. The analysis of the data is carried out in the time domain, which allows the broad component of the spectra to be modelled. This prior knowledge method has been tested for analysis of in vivo (31)P MR spectra from the liver and brain and gives results consistent with other methods that are also used to analyse the spectra, but with reduced variability. This technique may be utilized for studies requiring serial MR spectroscopy examinations, before and after patient treatment.

Algorithms↗
PubMed1998

Versatile frequency domain fitting using time domain models and prior knowledge.

An iterative nonlinear least-squares fitting algorithm in the frequency domain using time domain models for quantification of complex frequency domain MR spectra is presented. The algorithm allows incorporation of prior knowledge and has both the advantage of time-domain fitting with respect to handling the problem of missing data points and truncated data sets and of frequency-domain fitting with respect to multiple frequency-selective fitting. The described algorithm can handle, in addition to Lorentzian and Gaussian lineshapes, Voigt and nonanalytic lineshapes. The program allows the user the design of his own fitting strategy to optimize the probability of reaching the global least-squares minimum. The application of the fitting program is illustrated with examples from in vivo 1H-, 31P-, and 13C-MR spectroscopy.

Algorithms↗
PubMed2004

Frequent word section extraction in a presentation speech by an effective dynamic programming algorithm.

Word frequency in a document has often been utilized in text searching and summarization. Similarly, identifying frequent words or phrases in a speech data set for searching and summarization would also be meaningful. However, obtaining word frequency in a speech data set is difficult, because frequent words are often special terms in the speech and cannot be recognized by a general speech recognizer. This paper proposes another approach that is effective for automatic extraction of such frequent word sections in a speech data set. The proposed method is applicable to any domain of monologue speech, because no language models or specific terms are required in advance. The extracted sections can be regarded as speech labels of some kind or a digest of the speech presentation. The frequent word sections are determined by detecting similar sections, which are sections of audio data that represent the same word or phrase. The similar sections are detected by an efficient algorithm, called Shift Continuous Dynamic Programming (Shift CDP), which realizes fast matching between arbitrary sections in the reference speech pattern and those in the input speech, and enables frame-synchronous extraction of similar sections. In experiments, the algorithm is applied to extract the repeated sections in oral presentation speeches recorded in academic conferences in Japan. The results show that Shift CDP successfully detects similar sections and identifies the frequent word sections in individual presentation speeches, without prior domain knowledge, such as language models and terms.

Algorithms↗
PubMed2005

Age, ability, and the role of prior knowledge on the acquisition of new domain knowledge: promising results in a real-world learning environment.

Prior knowledge, fluid intelligence (Gf), and crystallized intelligence (Gc) were investigated as predictors of learning new information about cardiovascular disease and xerography with a sample of 199 adults (19 to 68 years). The learning environment included a laboratory multimedia presentation (high-constraint-maximal effort), and a self-directed at-home study component (low-constraint-typical performance). Results indicated that prior knowledge and ability were important predictors of knowledge acquisition for learning. Gc was directly related to learning from the video for both domains. Because the trajectory of Gc stays relatively stable throughout the life span, these findings provide a more optimistic perspective on the relationship between aging and learning than that offered by theories that focus on the role of fluid abilities in learning.

Adult↗
PubMed1992

Determination of concentrations by time domain fitting of proton NMR echo signals using prior knowledge.

A fast and flexible time domain iterative fitting procedure that can be used to fit free induction decays as well as echo-like signals is described. Damping constants of the first and second part of the echo do not have to be identical. Prior knowledge can be used to diminish the number of parameters to be fitted, which results in an improved accuracy. It is shown how prior knowledge is mathematically incorporated in the Gauss-Newton method. From proton NMR measurements of model solutions actual prior knowledge is extracted. With this knowledge relative concentrations are determined from a mixture of metabolites. The fitted results agree with the true values within the margins of the noise. After some minor changes the same prior knowledge was successfully used to analyze a series of in vivo rat brain measurements.

Magnetic Resonance Spectroscopy↗
PubMed1996

31P-magnetic resonance spectroscopy studies of nucleated and non-nucleated erythrocytes; time domain data analysis (VARPRO) incorporating prior knowledge can give information on the binding of ADP.

Human erythrocytes have no nucleus, mitochondria or endoplasmic reticulum, whereas chicken erythrocytes have a nucleus and mitochondria and are closer in internal morphology, to cells such as the hepatocyte. Erythrocytes were used to test the hypothesis that 31P-MRS invisibility of ADP is associated with the presence of intracellular organelles. Simple frequency domain spectral analysis methods showed that all the acid extractable ADP (and ATP) was MR-visible in human erythrocytes. However, such methods gave variable estimates for 31P-NMR spectra of fresh chicken erythrocytes from which no conclusions could be drawn about the MR-visibility of ADP. Only when the data were fitted by a method incorporating prior knowledge of the ATP and ADP peak structure, using the time domain VARPRO method, was it possible to conclude that in fresh chicken erythrocytes, similar to other nucleated cells (liver, muscle), all the acid extractable ADP appeared to be MRS invisible, indicating binding or sequestration by intracellular organelles.

Adenosine Diphosphate↗
PubMed2025

Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer.

To unravel the mechanism of immune activation and suppression within tumors, a critical step is to identify transcriptional signals governing cell-cell communication between tumor and immune/stromal cells in the tumor microenvironment. Central to this communication are interactions between secreted ligands and cell-surface receptors, creating a highly connected signaling network among cells. Recent advancements in in situ-omics profiling, particularly spatial transcriptomic (ST) technology, provide unique opportunities to directly characterize ligand-receptor signaling networks that power cell-cell communication. In this paper, we propose a novel statistical method, LRnetST, to characterize the ligand-receptor interaction networks between adjacent tumor and immune/stroma cells based on ST data. LRnetST utilizes a directed acyclic graph model with a novel approach to handle the zero-inflated distributions of ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation. Application of LRnetST to ST data of high-grade serous ovarian tumor samples revealed both common and distinct ligand-receptor regulations across different tumors. Some of these interactions were validated through both a MERFISH dataset and a CosMx SMI dataset of independent ovarian tumor samples. These results cast light on biological processes relating to the communication between tumor and immune/stromal cells in ovarian tumors. An open-source R package of LRnetST is available on GitHub at https://github.com/jie108/LRnetST.

Humans↗
PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗
PubMed1995

Continuous and discontinuous domains: an algorithm for the automatic generation of reliable protein domain definitions.

An algorithm is presented for the fast and accurate definition of protein structural domains from coordinate data without prior knowledge of the number or type of domains. The algorithm explicitly locates domains that comprise one or two continuous segments of protein chain. Domains that include more than two segments are also located. The algorithm was applied to a nonredundant database of 230 protein structures and the results compared to domain definitions obtained from the literature, or by inspection of the coordinates on molecular graphics. For 70% of the proteins, the derived domains agree with the reference definitions, 18% show minor differences and only 12% (28 proteins) show very different definitions. Three screens were applied to identify the derived domains least likely to agree with the subjective definition set. These screens revealed a set of 173 proteins, 97% of which agree well with the subjective definitions. The algorithm represents a practical domain identification tool that can be run routinely on the entire structural database. Adjustment of parameters also allows smaller compact units to be identified in proteins.

Actins↗
PubMed1991

A simple method to generate non-trivial alternate alignments of protein sequences.

A major problem in sequence alignments based on the standard dynamic programming method is that the optimal path does not necessarily yield the best equivalencing of residues assessed by structural or functional criteria. An algorithm is presented that finds suboptimal alignments of protein sequences by a simple modification to the standard dynamic programming method. The standard pairwise weight matrix elements are modified in order to penalize, but not eliminate, the equivalencing of residues obtained from previous alignments. The algorithm thereby yields a limited set of alternate alignments that can differ considerably from the optimal. The approach is benchmarked on the alignments of immunoglobulin domains. Without a prior knowledge of the optimal choice of gap penalty, one of the suboptimal alignments is shown to be more accurate than the optimal.

Algorithms↗
PubMed2004

Glutamate concentrations in human brain using single voxel proton magnetic resonance spectroscopy at 3 Tesla.

A method for quantitative determination of the glutamate (Glu) concentration in human brain using PRESS-based single voxel MR spectroscopy (MRS) at 3 T has been developed and validated by repeatedly analyzing voxels comprising the anterior cingulate cortex (acc) and the left hippocampus (hc) in 40 healthy volunteer brains. At an optimum echo time of 80 ms, the C4 resonance of Glu appears well resolved and separated from major interferents, that is, glutamine and N-acetylaspartate. As a complementary method, a multiple quantum coherence filter sequence for Glu was employed. For quantification of Glu and the principal MRS-visible metabolites as well as for an estimate of the glutamine level, analysis of both types of in vivo spectra was carried out by a time domain-frequency domain method involving prior knowledge obtained from phantom spectra. Using PRESS, coefficients of variation (CV) for Glu concentration were of the order of 10%. When the concentrations were corrected by individual cerebrospinal fluid fractions obtained by segmentation using spm, CVs tended to increase and the correlation coefficients for the two MRS sessions tended to decrease, indicating that this type of correction adds uncertainty to the data. The concentrations of Glu in the two voxels studied were found to be significantly different (11.6 mmol/l in acc, 10.9 mmol/l in hc, P = 0.023) and decrease with age (P < 0.04). These concentrations agreed well with those determined using the quantum coherence filter method although the uncertainty of the latter limits reliable analysis.

Adult↗
PubMed2005

Salivary gland tumors at in vivo proton MR spectroscopy.

PURPOSE: To prospectively evaluate whether proton magnetic resonance (MR) spectroscopy can be used to characterize salivary gland tumors (SGTs). MATERIALS AND METHODS: Ethics committee approval and informed consent were obtained. Hydrogen 1 ((1)H) MR spectroscopy was performed with echo times of 136 and 272 msec at 1.5 T in both SGTs and normal parotid glands. Spectra were analyzed in the time domain by using prior knowledge in the fitting procedure to obtain peak amplitudes of choline (Cho), creatine (Cr), and unsuppressed water. Mean Cho/Cr and Cho/water ratios for each subgroup of SGTs were obtained, and results were compared by using a nonparametric t test. RESULTS: Successful spectra were acquired in 56 patients (35 men, 21 women; mean age, 56 years) with a total of nine malignant tumors and 47 benign SGTs (24 Warthin tumors, 22 pleomorphic adenomas, one oncocytoma). At an echo time of 136 msec, Cho/Cr ratios were obtained in 26 (47%) of 55 spectra, with a mean value (+/- standard deviation) of 1.73 +/- 0.47, 5.49 +/- 1.86, 3.46 +/- 0.84, and 2.45 for malignant tumors, Warthin tumors, pleomorphic adenomas, and oncocytoma, respectively. Differences were significant between Warthin tumors and pleomorphic adenomas (P = .028) and between benign SGTs and malignant tumors (P < .001). At an echo time of 272 msec, Cho/Cr ratios were obtained in 16 (30%) of 53 spectra, with a mean value of 2.27 +/- 0.69, 6.92 +/- 1.47, and 3.67 +/- 1.23 for malignant tumors, Warthin tumors, and pleomorphic adenomas, respectively. Differences were also significant between Warthin tumors and pleomorphic adenomas (P = .041) and benign SGTs and malignant tumors (P = .004). There was a significant difference in mean Cho/water ratio for Warthin tumors versus pleomorphic adenomas at echo times of 136 msec (P = .003) and 272 msec (P = .002) but not for benign SGTs versus malignant tumors. CONCLUSION: (1)H MR spectroscopy may be used to characterize SGTs, but a larger study is required to validate these initial results.

Adult↗
PubMed2003

Why are some people more knowledgeable than others? A longitudinal study of knowledge acquisition.

The purpose of this study was to investigate correlates of knowledge acquisition within a real-world domain-basketball. The study employed a longitudinal design and involved two sessions, with approximately 2.5 months between sessions. Session 1 occurred near the beginning of the men's college basketball season, and Session 2 occurred after the season. There was an indirect effect of interest in basketball on new basketball knowledge, via exposure to domain-relevant information. In addition, prior basketball knowledge had a strong effect on new basketball knowledge distinct from an influence through basketball exposure. This finding suggests that prior knowledge may provide a framework into which new knowledge can be integrated. By contrast, there was no effect of fluid intelligence on new basketball knowledge. Although somewhat surprising given evidence that this aspect of intelligence may play a role in the acquisition of certain types of knowledge, this finding accords with the view that knowledge is a major determinant of various aspects of cognitive functioning, including learning.

Aptitude↗
PubMed2006

Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge.

Quantification of MRS spectra is a challenging problem when a large baseline is present along with a low signal to noise ratio. This work investigates a robust fitting technique that yields accurate peak areas under these conditions. Using simulated long echo time (1)H MRS spectra with low signal to noise ratio and a large baseline component, both the accuracy and reliability of the fit in the frequency domain were greatly improved by reducing the number of fitted parameters and making full use of all the known information concerning the Voigt lineshape. Using an appropriate first order approximation to a popular approximation of the Voigt lineshape, a significant improvement in the estimate of the area of a known spectral peak was obtained with a corresponding reduction in the residual. Furthermore, this improved parameter choice resulted in a large reduction in the number of iterations of the least-squares fitting routine. On the other hand, making use of the known centre frequency differences of the component resonances gave negligible improvement. A wavelet filter was used to remove the baseline component. In addition to performing a Monte Carlo study, these fitting techniques were also applied to a set of 10 spectra acquired from healthy human volunteers. Again, the same reduced parameter model gave the lowest value for chi(2) in each case.

Computer Simulation↗
PubMed2007

Effect of signal-to-noise ratio and spectral linewidth on metabolite quantification at 4 T.

The accuracy and precision of measurements of metabolite concentrations from short echo-time spectra has previously been characterized at l.5 T as a function of signal-to-noise ratio (SNR) and peak linewidth. The purpose of this study was to characterize the systematic error in quantification of metabolite concentrations associated with linewidth and SNR for the major metabolites of interest in the short echo-time 1H-MR spectrum at 4 T. Simulated 4 T LASER localized spectra (TE = 46 ms) were generated with full width at half maximum (FWHM) over the range 4-14 Hz, and SNR over the range 5-500 by adding 100 Gaussian-distributed noise realizations at each combination of SNR and linewidth. Linewidth and SNR were treated as independent parameters, and therefore an increase in linewidth at a constant SNR resulted in increased metabolite areas. All spectra were fitted in the time domain using identical prior-knowledge and relative parameter starting values. Six metabolites (N-acetylaspartate, glutamate, creatine, myo-inositol, glycerophosphocholine, phosphocholine) were quantified with >90% accuracy and <10% standard deviation at SNR = 10 for linewidths ranging from 8 to 14 Hz FWHM. These simulations did not consider additional sources of variation, including eddy current artifacts, incomplete macromolecule baseline removal, and incomplete water suppression. Regardless, the results show that metabolite quantification from 4 T short echo-time 1H-MRS is sensitive to SNR and linewidth.

Aspartic Acid↗
PubMed2002

Predicting the sub-cellular location of proteins from text using support vector machines.

We present an automatic method to classify the sub-cellular location of proteins based on the text of relevant medline abstracts. For each protein, a vector of terms is generated from medline abstracts in which the protein/gene's name or synonym occurs. A Support Vector Machine (SVM) is used to automatically partition the term space and to thus discriminate the textual features that define sub-cellular location. The method is benchmarked on a set of proteins of known sub-cellular location from S. cerevisiae. No prior knowledge of the problem domain nor any natural language processing is used at any stage. The method out-performs support vector machines trained on amino acid composition and has comparable performance to rule-based text classifiers. Combining text with protein amino-acid composition improves recall for some sub-cellular locations. We discuss the generality of the method and its potential application to a variety of biological classification problems.

Automation↗
PubMedDate not supplied

Prior knowledge and exemplar encoding in children's concept acquisition.

Three experiments examined how children's domain knowledge and observation of exemplars interact during concept acquisition and how exposure to novel exemplars causes revision of such knowledge. In Experiments 1 (N = 126) and 2 (N = 64), children aged 4 to 10 years were shown exemplars of fictitious animal categories that were either unrelated to, or consistent with, their prior knowledge in 25% or 75% of presented exemplars. In Experiment 3, children (N = 290) saw fictitious animal, artifact, or unfamiliar social categories that were either consistent or inconsistent with their prior knowledge in 20%, 40%, 60%, or 80% of exemplars. In the test, children made judgments about the likely co-occurence of features. In all experiments, prior knowledge and exemplar observation independently influenced children's categorization judgments. Utilization of prior knowledge was consistent across age and domain, but 10-year-olds were more sensitive to observed feature covariation. Training with larger categories increased the impact of observed feature covariation and decreased reliance on prior knowledge.

Association Learning↗
PubMed2004

Modeling the effects of prior knowledge on learning incongruent features of category members.

The authors conducted 3 experiments addressing the issue of how observations and multiple sources of prior knowledge are put together in category learning. In Experiments 1 and 2, learning was faster for critical features, which were predictable on the basis of prior knowledge, than for filler features, and this advantage increased as more observations were made. In addition, learning was fastest for incongruent features that could only be predicted using knowledge from other domains. In Experiment 3, presenting contradictory features that violated prior knowledge led to rote learning rather than use of prior knowledge. The results were simulated with the Baywatch model, which addresses how observations of category members lead to recruitment and selection of sources of prior knowledge.

Cognition↗