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PubMed2003

A domain combination based probabilistic framework for protein-protein interaction prediction.

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices are constructed. Each matrix holds information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, respectively. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting sets of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in a Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP (Database of Interacting Proteins) is used as a learning set of interacting protein pairs, very high sensitivity (86%) and moderate specificity (56%) are achieved within our framework.

Amino Acid Sequence↗
PubMed1998

Quality of student learning and preceptor productivity in urban community health centers.

BACKGROUND AND OBJECTIVES: Clinicians in community health centers find it difficult to balance the demands of increased productivity and effective teaching. We hypothesized that precepting third-year students would decrease clinical productivity and that many elements related to the quality of the learning experience (e.g., amount of patient contact, student autonomy) would be adversely affected by pressure to see increasing numbers of patients. METHODS: Students and preceptors in a 6-week family medicine clerkship completed daily surveys that measured the presence of quantifiable elements of the ambulatory teaching experience. They also rated the overall quality of learning during each session. RESULTS: For 62 sessions for which both students and preceptors completed evaluations, students rated the overall quality of learning more highly than preceptors. For students, the elements most positively associated with quality of learning were total teaching time and the frequency with which family issues were raised. For preceptors, the elements that predicted quality of learning were the number of patients that students saw independently and total teaching time. The clinical productivity of preceptors did not differ for sessions with and without a student. CONCLUSIONS: Preceptors can be effective teachers who encourage student autonomy and who model behaviors central to family practice, without decreasing productivity.

Clinical Clerkship↗
PubMed2025

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integrated gene expression, somatic mutation, and copy number aberration (CNA) data with drug response profiles using multi-omics integration (MI). We used the Genomics of Drug Sensitivity in Cancer (GDSC) data for training and incorporated drugs with same pathways into the training models. We then evaluated drug response predictions on independent in-vivo PDX Encyclopedia (PDX) and ex-vivo the Cancer Genome Atlas (TCGA) datasets. In addition, we conducted pathway enrichment analyses to elucidate the mechanisms underlying drug resistance for paclitaxel, 5-fluorouracil (5-FU), gemcitabine, and cetuximab. We also applied Fisher's exact test (FET) to assess potential associations between drug resistance and the presence of mutations or CNAs. Our pan-drug models outperformed other methods based on the area under the precision-recall curve (AUCPR). Our pathway enrichment analyses revealed LDHB-mediated pyruvate metabolism and FYN-mediated focal adhesion might have pivotal roles in paclitaxel resistance, while PINK1-mediated mitophagy might be critical in 5-FU resistance. In addition to transcriptional activation, FET suggested that CNAs in LDHB and PINK1 may also be associated with resistance to paclitaxel and 5-FU, respectively. Furthermore, enrichment results for paclitaxel and cetuximab indicated shared resistance mechanisms between the two drugs. Importantly, our findings are consistent with prior experimental studies, providing literature-based validation of our results. Overall, our DNN-based TL approach achieved strong predictive performance across PDX & TCGA datasets and enrichment analyses provided valuable biological insights into drug resistance mechanisms.

Humans↗
PubMed1999

The link between brain learning, attention, and consciousness.

The processes whereby our brains continue to learn about a changing world in a stable fashion throughout life are proposed to lead to conscious experiences. These processes include the learning of top-down expectations, the matching of these expectations against bottom-up data, the focusing of attention upon the expected clusters of information, and the development of resonant states between bottom-up and top-down processes as they reach an attentive consensus between what is expected and what is there in the outside world. It is suggested that all conscious states in the brain are resonant states and that these resonant states trigger learning of sensory and cognitive representations. The models which summarize these concepts are therefore called Adaptive Resonance Theory, or ART, models. Psychophysical and neurobiological data in support of ART are presented from early vision, visual object recognition, auditory streaming, variable-rate speech perception, somatosensory perception, and cognitive-emotional interactions, among others. It is noted that ART mechanisms seem to be operative at all levels of the visual system, and it is proposed how these mechanisms are realized by known laminar circuits of visual cortex. It is predicted that the same circuit realization of ART mechanisms will be found in the laminar circuits of all sensory and cognitive neocortex. Concepts and data are summarized concerning how some visual percepts may be visibly, or modally, perceived, whereas amodal percepts may be consciously recognized even though they are perceptually invisible. It is also suggested that sensory and cognitive processing in the What processing stream of the brain obey top-down matching and learning laws that are often complementary to those used for spatial and motor processing in the brain's Where processing stream. This enables our sensory and cognitive representations to maintain their stability as we learn more about the world, while allowing spatial and motor representations to forget learned maps and gains that are no longer appropriate as our bodies develop and grow from infanthood to adulthood. Procedural memories are proposed to be unconscious because the inhibitory matching process that supports these spatial and motor processes cannot lead to resonance.

Attention↗
PubMed1990

Connectionism and the learning of probabilistic concepts.

Three experiments examine the claim of Gluck and Bower (1986, 1988a, 1988b) that the learning of medical concepts can be simulated by a connectionist network in which the symptoms are the input and the diagnosis is the output. The first experiment replicates the main finding of Gluck and Bower. In this experiment, subjects were required to estimate the probability of each of two diseases, given a particular target symptom. In fact these two probabilities were identical, but because one illness was more common than the other, the target symptom was a better predictor of the rare disease than of the common disease. Contrary to a normative probability judgement account, subjects were biased in that they judged the probability of the rare disease given the target symptom to be greater than the probability of the common disease given the target symptom. Gluck and Bower argued that such a result was predicted by a connectionist network using the Rescorla-Wagner learning rule, but it is argued that Gluck and Bower's network simulation was not appropriate for the experiment they had performed. In fact, it appeared that the connectionist network failed to predict the bias in the subjects' probability estimates. However, this conclusion rests on an assumption that Gluck and Bower implicitly made. They arranged for P(rare disease/target symptom) and P(common disease/target symptom) to be identical across all trials on which the target symptom occurred, both on its own and with other symptoms present. Gluck and Bower assumed that the subjects were estimating these probabilities. But the results of the second experiment showed instead that the subjects were estimating the probability of each disease given only the target symptom. In the final experiment the design was changed so that this problem might be circumvented. In this experiment, again, the subjects were biased in their probability judgements exactly as the connectionist network predicted. Thus, finally, evidence was found which was compatible with the network model but not with a normative account, but this was true only if the network did not include a layer of hidden units.

Adult↗
PubMed2025

Inflammatory pathways and immune dysregulation in pediatric postoperative septic shock: A study integrating transcriptomics, machine learning and molecular docking.

This study elucidates the molecular and immune regulatory mechanisms of pediatric postoperative septic shock. Transcriptomic data were obtained from the Gene Expression Omnibus database. Differentially expressed genes were identified using the limma package, and gene co-expression modules were constructed using Weighted Gene Co-expression Network Analysis. Functional enrichment was performed via gene set enrichment analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. Immune cell infiltration was assessed using ESTIMATE and CIBERSORT. Mendelian randomization was applied to explore causal relationships between gene expression and septic shock. Feature genes were selected using machine learning algorithms, and a diagnostic nomogram model was constructed. Finally, molecular docking analysis was performed to screen and evaluate the binding affinity of traditional Chinese medicine monomers to core target proteins. A total of 1331 differentially expressed genes were identified, and the turquoise module was strongly correlated with septic shock. Enrichment analysis revealed significant activation of IL-6/JAK/STAT3, TNF-α/NF-κB, and PI3K/Akt/mTOR pathways. Immune infiltration analysis indicated suppressed immune scores and imbalances in neutrophils, macrophages, T cells, and B cells. Mendelian randomization confirmed causal associations for 6 genes, including PIM3. The predictive model based on feature genes demonstrated high diagnostic performance. Molecular docking suggested that quercetin and astramembrannin I could stably bind PIM3. This study systematically identified core genes, dysregulated immune pathways, and candidate small-molecule interventions in pediatric septic shock, providing novel insights for early diagnosis and targeted therapy.

Humans↗
PubMed2005

The dynamics of perceptual learning: an incremental reweighting model.

The mechanisms of perceptual learning are analyzed theoretically, probed in an orientation-discrimination experiment involving a novel nonstationary context manipulation, and instantiated in a detailed computational model. Two hypotheses are examined: modification of early cortical representations versus task-specific selective reweighting. Representation modification seems neither functionally necessary nor implied by the available psychophysical and physiological evidence. Computer simulations and mathematical analyses demonstrate the functional and empirical adequacy of selective reweighting as a perceptual learning mechanism. The stimulus images are processed by standard orientation- and frequency-tuned representational units, divisively normalized. Learning occurs only in the "read-out" connections to a decision unit; the stimulus representations never change. An incremental Hebbian rule tracks the task-dependent predictive value of each unit, thereby improving the signal-to-noise ratio of their weighted combination. Each abrupt change in the environmental statistics induces a switch cost in the learning curves as the system temporarily works with suboptimal weights.

Brain↗
PubMed2004

The cognitive activation theory of stress.

This paper presents a cognitive activation theory of stress (CATS), with a formal system of systematic definitions. The term "stress" is used for four aspects of "stress", stress stimuli, stress experience, the non-specific, general stress response, and experience of the stress response. These four meanings may be measured separately. The stress response is a general alarm in a homeostatic system, producing general and unspecific neurophysiological activation from one level of arousal to more arousal. The stress response occurs whenever there is something missing, for instance a homeostatic imbalance, or a threat to homeostasis and life of the organism. Formally, the alarm occurs when there is a discrepancy between what should be and what is-between the value a variable should have (set value (SV)), and the real value (actual value (AV)) of the same variable. The stress response, therefore, is an essential and necessary physiological response. The unpleasantness of the alarm is no health threat. However, if sustained, the response may lead to illness and disease through established pathophysiological processes ("allostatic load"). The alarm elicits specific behaviors to cope with the situation. The level of alarm depends on expectancy of the outcome of stimuli and the specific responses available for coping. Psychological defense is defined as a distortion of stimulus expectancies. Response outcome expectancies are defined as positive, negative, or none, to the available responses. This offers formal definitions of coping, hopelessness, and helplessness that are easy to operationalize in man and in animals. It is an essential element of CATS that only when coping is defined as positive outcome expectancy does the concept predict relations to health and disease.

Adaptation, Psychological↗
PubMed2003

Modular organization of internal models of tools in the human cerebellum.

Human capabilities in manipulating many different tools with dexterity suggest modular neural organization at functional levels, but anatomical modularity underlying the capabilities has yet to be demonstrated. Although modularity in phylogenetically older parts of the cerebellum is well known, comparable modularity in the lateral cerebellum for cognitive functions remains unknown. We investigated these issues by functional MRI (fMRI) based on our previous findings of a cerebellar internal model of a tool. After subjects intensively learned to manipulate two novel tools (the rotated mouse whose cursor appeared at a rotated position, and the velocity mouse whose cursor velocity was proportional to the mouse position), they could easily switch between the two. The lateral and posterior cerebellar activities for the two different tools were spatially segregated, and their overlaps were <10%, even at low statistical thresholds. Activities of the rotated mouse were more anterior and lateral than the velocity mouse activities. These results were consistent with predictions by the MOdular Selection And Identification Controller (MOSAIC) model that multiple internal models compete to partition sensory-motor experiences and their outputs are linearly combined for a particular context.

Adult↗
PubMed2003

Motor learning and prediction in a variable environment.

Traditional studies of motor learning and prediction have focused on how subjects perform a single task. Recent advances have been made in our understanding of motor learning and prediction by investigating the way we learn variable tasks, which change either predictably or unpredictably over time. Similarly, studies have examined how variability in our own movements affects motor learning.

Biomechanical Phenomena↗
PubMed1998

Maladaptive achievement patterns: a test of a diathesis-stress analysis of helplessness.

The present study tested the assumptions, derived from a diathesis-stress model, that students' perceptions of autonomy-support in their classroom produce a relatively intrinsic as opposed to extrinsic motivational approach to academic tasks and that this approach, in turn, predicts qualitatively different responses to uncontrollable events. It was further assumed that students' motivational orientation would be more reliable than attributional style or perceptions of competence in predicting achievement patterns, including performance level after failure, use of adaptive attributions, and overall achievement scores. Results supported these predictions and further demonstrated, in longitudinal analyses, that motivational orientation may contribute to the formation of perceptions of competence and attributional style in students.

Achievement↗
PubMed2004

Interaction profile-based protein classification of death domain.

BACKGROUND: The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. 1 and more recently by Valdar et al. 2 and Mintseris et al. 3, from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation. RESULTS: We have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy. CONCLUSION: We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.

Humans↗
PubMed2005

Surgical skill is predicted by the ability to detect errors.

BACKGROUND: Objective analysis methods of surgical performance are now available so comparison between surgeons is available. One such method is by direct observation using the Objective Structured Assessment of Technical Skills (OSATS), but this is a time-consuming process; therefore, a simple screening tool for the ability to detect errors (previously validated) was analyzed and considered as a predictor of qualitative performance. METHODS: Thirty-eight volunteer surgeons were recruited to the skills laboratory to undertake 3 exercises. Two were bench-top surgical tasks that were scored using the global rating of the OSATS technique. The third task was the ability to detect simple errors in 22 synthetic models of common surgical procedures, some of which contained purposefully made errors. P<.05 was deemed to be statistically significant. RESULTS: The scores (interquartile ranges in parentheses) for the 3 sections were excision of sebaceous cyst=21 (19,24), closure of small bowel enterotomy=23 (21,27), and identification of errors=31 (27,34). Three scorers blinded to the operative models exhibited an interobserver reliability of .9 and .91 for the video tasks, respectively. Spearman's rank correlations between the error examination and performance on the 2 tasks were both statistically significant at .69 (cystectomy) and .54 (enterotomy). CONCLUSIONS: The ability to detect simple surgical errors is a predictor of technical skill and performance of bench tasks. What must be answered is whether the use of such models and principles can shorten the qualitative surgical learning curve.

Clinical Competence↗
PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n&#xa0;=&#xa0;549) and a validation set (n&#xa0;=&#xa0;236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60&#xa0;mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60&#xa0;mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗
PubMed2003

Predicting the practice effects on the blood oxygenation level-dependent (BOLD) function of fMRI in a symbolic manipulation task.

Based on adaptive control of thought-rational (ACT-R), a cognitive architecture for cognitive modeling, researchers have developed an information-processing model to predict the blood oxygenation level-dependent (BOLD) response of functional MRI in symbol manipulation tasks. As an extension of this research, the current event-related functional MRI study investigates the effect of relatively extensive practice on the activation patterns of related brain regions. The task involved performing transformations on equations in an artificial algebra system. This paper shows that the base-level activation learning in the ACT-R theory can predict the change of the BOLD response in practice in a left prefrontal region reflecting retrieval of information. In contrast, practice has relatively little effect on the form of BOLD response in the parietal region reflecting imagined transformations to the equation or the motor region reflecting manual programming.

Adult↗
PubMed2001

Lack of consistent behavioural effects of Maudsley reactive and non-reactive rats in a number of animal tests of anxiety and activity.

RATIONALE: A number of previous studies have reported that the Maudsley reactive (MR/Har) and non-reactive (MNRA/Har) strains of rats show behavioural and physiological differences consistent with the hypothesis that these strains differ in emotionality and could therefore be considered a model of trait anxiety in humans. OBJECTIVES: We sought to confirm this observation by determining their behaviour in various animal models of conditioned and unconditioned fear. METHODS: Both strains were evaluated in the open field (OF), conditioned avoidance (CA), elevated plus maze (EPM) and fear-potentiated startle (FPS) tests. In the OF the behaviour of both strains was consistent with previous results showing that reactive rats had significantly higher levels of defecation and lower levels of activity than the non-reactive rats. However, there were no significant strain differences in CA responses or in the time spent on the open arms of the EPM. In addition, the full benzodiazepine receptor agonist, chlordiazepoxide, induced quantitatively similar effects in both strains of rats. In the FPS test, MNRA/Hars had a higher baseline level of startle and fear potentiation than the MR/Har rats. CONCLUSIONS: These data show that the behaviour of MR/Har and MNRA/Har rats in some models of conditioned and unconditioned fear is inconsistent with that predicted by their behaviour in the OF test, suggesting that they are not a model of trait fear.

Animals↗
PubMedDate not supplied

Phonological processing and oral language abilities in fourth-grade poor readers.

UNLABELLED: This study investigated the contribution of phonological processing and oral language skills to reading abilities (word attack, word recognition, and comprehension) in 17 fourth graders identified as poor readers. Participants were assessed in the areas of phonological awareness, verbal working memory, and rapid naming, in addition to oral language abilities as predictors of poor reading skills. Results indicated that the strongest predictors of word attack skills were phonological awareness and grammaticality judgment. The combination of phonological awareness, grammaticality judgment, phoneme manipulation, and rapid naming of digits accounted for more than half of the variance in word recognition. Furthermore, the amount of discrepancy between IQ scores and reading achievement did not distinguish groups of poor readers. The results support the importance of metalinguistic skills to support reading ability. Phonological processing and oral language abilities may vary in children with poor reading skills as processing demands change with age and task. LEARNING OUTCOMES: As a result of this activity, the reader will be able to: (1) identify phonological processing and oral language skills that are most predictive of specific reading skills in poor readers; (2) describe further evidence of the lack of utility of the discrepancy model to define reading disorders in children.

Awareness↗