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PubMed1996

Text Learning Using Scientific Diagrams: Implications for Classroom Use

In two experiments, eighth-graders viewed ecologically valid diagrams and then read a text containing multiple feature-to-fact associations or studied the same materials in reverse order. Using the Kulhavy and Stock model of text learning using organized spatial displays, it was predicted that those students viewing the diagram prior to reading the text would recall more facts and features than subjects viewing the material in the reverse order. These predictions were well supported. In addition, since the materials used in the experiments were created from actual classroom materials, these experiments examined whether the model for text learning is also valid using this type of materials. In this area the model proved to be very effective as well.

Journal Article↗

PubMed1997

Evidence-Based Pattern Classification: A Structural Approach to Human Perceptual Learning and Generalization

Models of human pattern classification have been traditionally based on implicit pattern descriptions which involve lists of continuous attribute values or discrete features. Here we propose an alternative approach which makes explicit use of pattern structure in terms of components and their unary (part-specific) and binary (part-relational) properties. Such attributes "evidence" different classes of patterns and allow one to model processes of both perceptual learning and generalization to novel instances. An object in an evidence-based system is represented by a set of rules, where each rule provides a certain amount of class-specific evidence. The accumulated class evidence over all activated rules determines the classification probability. We have examined how well this concept reflects human performance by training observers to classify compound Gabor patterns and then testing them with segmented (grey-level-transformed) versions of the patterns in the original training set. If the observers were to construct rules to define each pattern class in terms of perceived parts and their relations, then it should be expected that classification performance would generalize to these new patterns. Results confirm this hypothesis and the specific feature extraction, learning, and rule generation model used to predict performance. Copyright 1997 Academic Press

Journal Article↗

PubMed2003

Using an integrated approach to understand vaccination behavior among young men who have sex with men: stages of change, the health belief model, and self-efficacy.

Studies continue to show that the majority of men who have sex with men (MSM) in the United States remain unvaccinated against hepatitis A (HAV); such limited vaccination coverage is a missed opportunity for preventing disease. This study sought to identify beliefs and attitudes associated with motivational readiness for vaccination against HAV among MSM, using a theoretically-integrated framework. Questionnaire data were collected from 358 MSM through bar outreach. MSM with increased readiness to complete the two-dose series perceived lower practical barriers and reported greater healthcare provider communication about their sexual orientation and risk behavior. They also perceived higher benefits to vaccination and higher severity of infection, and had higher self-efficacy to complete the vaccine series. Relationships between stages of change and theory-based constructs from the health belief model and the social learning theory follow predicted patterns suggesting that these theories may provide useful frameworks for understanding vaccination readiness and intervention strategy development among MSM.

Adolescent↗

PubMed2026

Flexible use of conserved motifs constrains genome access in cell type evolution.

Cell types can be organized into related families, but the regulatory mechanisms that define and maintain these families across deep evolutionary time remain unknown. Here, combining single-nucleus multi-omic sequencing with deep learning to analyse the accessible genomes of two groups of vastly divergent animals including flatworms and vertebrates, we find that hundreds of accessibility-dictating sequence motifs partition into distinct yet conserved sets, or 'vocabularies', each associated with a specific cell type family. However, combinatorial relationships among these motifs preferred by individual cell types are largely species specific. Deep-learning models trained on one species accurately predict family-level chromatin accessibility in distantly related species, albeit frequently rely on different motifs from shared vocabularies to reach convergent predictions. By contrast, models trained on individual cell types within a family lose cross-species predictive power, indicating that the regulatory syntax governing cell type-level identity evolves rapidly. We propose a 'collective maintenance' model in which motif vocabularies defining cell type families are evolutionarily stable, while recombination of these motifs generates cell type-specific regulatory programmes. This suggests that family identity is maintained collectively by large, conserved pools of regulatory factors, analogous to the logic of developmental homology, where character identity persists through network-level conservation despite extensive rewiring.

Journal Article↗

PubMed2002

Cerebellar mechanisms in eyeblink conditioning.

A recent model of cerebellar learning in eyeblink conditioning predicts two sites of plasticity, the cerebellar cortex and cerebellar nuclei, which store information relating to timing and driving the movement, respectively. Consistent with this idea, lesions of the cortex or reversible "disconnections" of Purkinje cell output to the nuclei have been shown to disrupt response timing to produce short-latency conditioned eyeblinks. To better characterize potential cortical and nuclear plasticities, we analyzed the effects upon nictitating membrane (NM) and eyeblink conditioned responses (CRs) of different drugs administered to the cortex and to the nuclei. When either excitatory or inhibitory inputs to the cerebellar cortical lobule HVI were blocked by infusions of the AMPA receptor antagonist CNQX or the GABA-A receptor antagonists picrotoxin or SR95531, CRs were abolished. Similarly GABA-A receptor antagonists in the cerebellar nuclei abolished CRs. CR latencies were never shortened. However, blockade of AMPA/kainate receptor-mediated excitatory transmission to the nuclei had no effect upon CR frequencies or latencies. These results suggest that normal cortical and nuclear function is required for performance of NM and eyeblink CRs. We saw no evidence that CRs can be driven by AMPA/kainate receptor-mediated transmission from mossy fiber afferents to the cerebellar nuclei. So, although plasticity in the cerebellar nuclei is not ruled out, it is unlikely that a long-term change in AMPA receptor-mediated transmission from mossy fiber inputs to the nuclei is an essential mechanism in eyeblink conditioning. Our findings indicate that a fully functional olivo-cortico-nuclear loop is required to express all characteristics of associatively conditioned responses.

Animals↗

PubMedDate not supplied

Integrating behavioral theory to understand hepatitis B vaccination among men who have sex with men.

OBJECTIVE: To identify beliefs and attitudes associated with motivational readiness for vaccination against hepatitis B vaccination among at-risk men who have sex with men (MSM), using a theoretically integrated framework. METHODS: Data were collected from 358 MSM. RESULTS: MSM with increased readiness to complete the 3-dose series perceived lower practical barriers and greater benefits to vaccination, perceived higher severity of infection, and had higher self-efficacy to complete the vaccine series. CONCLUSIONS: Relationships between stages of change and theory-based constructs from the health belief model and the social learning theory follow predicted patterns suggesting that these theories may provide useful frameworks for understanding vaccination readiness and intervention strategy development among MSM.

Adolescent↗

PubMedDate not supplied

Predicting mortality after coronary artery bypass surgery: what do artificial neural networks learn? The Steering Committee of the Cardiac Care Network of Ontario.

OBJECTIVE: To compare the abilities of artificial neural network and logistic regression models to predict the risk of in-hospital mortality after coronary artery bypass graft (CABG) surgery. METHODS: Neural network and logistic regression models were developed using a training set of 4,782 patients undergoing CABG surgery in Ontario, Canada, in 1991, and they were validated in two test sets of 5,309 and 5,517 patients having CABG surgery in 1992 and 1993, respectively. RESULTS: The probabilities predicted from a fully trained neural network were similar to those of a "saturated" regression model, with both models detecting all possible interactions in the training set and validating poorly in the two test sets. A second neural network was developed by cross-validating a network against a new set of data and terminating network training early to create a more generalizable model. A simple "main effects" regression model without any interaction terms was also developed. Both of these models validated well, with areas under the receiver operating characteristic curves of 0.78 and 0.77 (p > 0.10) in the 1993 test set. The predictions from the two models were very highly correlated (r=0.95). CONCLUSIONS: Artificial neural networks and logistic regression models learn similar relationships between patient characteristics and mortality after CABG surgery.

Aged↗

PubMed2003

A computational substrate for incentive salience.

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

Algorithms↗

PubMed1987

The combining of multiple hemispheric resources in learning-disabled and skilled readers' recall of words: a test of three information-processing models.

Three theoretical models (additive, independence, maximum rule) that characterize and predict the influence of independent hemispheric resources on learning-disabled and skilled readers' simultaneous processing were tested. Predictions related to word recall performance during simultaneous encoding conditions (dichotic listening task) were made from unilateral (dichotic listening task) presentations. The maximum rule model best characterized both ability groups in that simultaneous encoding produced no better recall than unilateral presentations. While the results support the hypothesis that both ability groups use similar processes in the combining of hemispheric resources (i.e., weak/dominant processing), ability group differences do occur in the coordination of such resources.

Cerebral Cortex↗

PubMed1997

Dynamic model of visual recognition predicts neural response properties in the visual cortex.

The responses of visual cortical neurons during fixation tasks can be significantly modulated by stimuli from beyond the classical receptive field. Modulatory effects in neural responses have also been recently reported in a task where a monkey freely views a natural scene. In this article, we describe a hierarchical network model of visual recognition that explains these experimental observations by using a form of the extended Kalman filter as given by the minimum description length (MDL) principle. The model dynamically combines input-driven bottom-up signals with expectation-driven top-down signals to predict current recognition state. Synaptic weights in the model are adapted in a Hebbian manner according to a learning rule also derived from the MDL principle. The resulting prediction-learning scheme can be viewed as implementing a form of expectation-maximization (EM) algorithm. The architecture of the model posits an active computational role of the reciprocal connections between adjoining visual cortical areas in determining neural response properties. In particular, the model demonstrates the possible role of feedback from higher cortical areas in mediating neurophysiological effects due to stimuli from beyond the classical receptive field. Simulations of the model are provided that help explain the experimental observations regarding neural responses in both free viewing and fixation conditions.

Animals↗

PubMed1994

Rule-plus-exception model of classification learning.

The authors propose a rule-plus-exception model (RULEX) of classification learning. According to RULEX, people learn to classify objects by forming simple logical rules and remembering occasional exceptions to those rules. Because the learning process in RULEX is stochastic, the model predicts that individual Ss will vary greatly in the particular rules that are formed and the exceptions that are stored. Averaged classification data are presumed to represent mixtures of these highly idiosyncratic rules and exceptions. RULEX accounts for numerous fundamental classification phenomena, including prototype and specific exemplar effects, sensitivity to correlational information, difficulty of learning linearly separable versus nonlinearly separable categories, selective attention effects, and difficulty of learning concepts with rules of differing complexity. RULEX also predicts distributions of generalization patterns observed at the individual subject level.

Adult↗

PubMed2026

PathMED: an R toolkit for single-sample molecular scoring and machine learning with omics data.

MOTIVATION: Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction. RESULTS: We developed pathMED, an R/Bioconductor package that unifies various scoring methods in a simple framework. Furthermore, pathMED also contains a machine learning module to train and test models that use the calculated molecular scores to predict clinical outcomes. We demonstrate some of its potential applications in three use cases using public omics data. We showed the generalisability of machine learning models trained on transcriptomic scores in predicting clinical outcomes when deploying on proteomic scores. We also demonstrated the application of transcriptomics scores in predicting breast cancer treatment response and identifying pathways strongly associated to tumour biology and treatment response. Finally, we demonstrated the benefit of integrating a novel gene set dissection step into the analysis pipeline to resolve disease heterogeneity at the pathway level. AVAILABILITY: PathMED is freely available in the Bioconductor repository (https://bioconductor.org/packages/release/bioc/html/pathMED.html). Code to reproduce the analyses is publicly available at https://github.com/GENyO-BioInformatics/pathMED_article.

Software↗

PubMed2025

Proteomics-enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus.

BACKGROUND AND AIMS: Estimating the risk of cardiovascular disease (CVD) complications in type 2 diabetes mellitus (T2DM) patients is critical in the medical decision-making process. This study aimed to use a machine learning technique combined with proteomics to develop personalized models for predicting CVD in patients with T2DM. METHODS AND RESULTS: In total, 874 patients with T2DM and 2,920 Olink proteins obtained from the UK Biobank were used in this study. Proteins were screened using Cox regression and LASSO regression. A basic model containing clinical features and a full model combining proteome and clinical features were constructed using the random survival forest algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive performance of the models and compare them with other CVD predictive models. Compared with the basic model, the full model performed better in predicting CVD, with time-dependent AUCs of 0.81 (3 years), 0.74 (5 years) and 0.74 (10 years) (0.77, 0.69 and 0.67). We calculated the risk scores of the Framingham, ASCVD and Score2-Diabetes models. The results revealed that the prediction performance of the full model was also better than that of the abovementioned models. In terms of differentiation accuracy, the results of the net reclassification improvement index and integrated discrimination improvement index showed that the full model can identify high-risk individuals more accurately (accuracy rate: 79% vs. 69%). CONCLUSIONS: Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.

Humans↗

PubMed2001

The quality of adolescents' friendships: associations with mothers' interpersonal relationships, attachments to parents and friends, and prosocial behaviors.

Adolescents' friendship quality and observed emotional expression with their best friends were predicted from reports of their mother's interpersonal relationships-specifically the quality of her marriage and social network. Two models explaining these relationships received support. Consistent with an Attachment Theory model, adolescents' perceptions of marital quality predicted attachment security with mother, father and friends. Security of attachment to friends in turn predicted best friendship quality, but not affective behavior with the friend. A Social Learning Theory model was also supported, in which perceptions of both marital quality and mother's social network quality predicted adolescents' prosocial behavior. Prosocial behavior in turn predicted both best friendship quality and affective behavior with the friend.

Adolescent↗

PubMed2000

An associational model of birdsong sensorimotor learning II. Temporal hierarchies and the learning of song sequence.

Understanding the neural mechanisms underlying serially ordered behavior is a fundamental problem in motor learning. We present a computational model of sensorimotor learning in songbirds that is constrained by the known functional anatomy of the song circuit. The model subsumes our companion model for learning individual song "syllables" and relies on the same underlying assumptions. The extended model addresses the problem of learning to produce syllables in the correct sequence. Central to our approach is the hypothesis that the Anterior Forebrain Pathway (AFP) produces signals related to the comparison of the bird's own vocalizations and a previously memorized "template." This "AFP comparison hypothesis" is challenged by the lack of a direct projection from the AFP to the song nucleus HVc, a candidate site for the generator of song sequence. We propose that sequence generation in HVc results from an associative chain of motor and sensory representations (motor --> sensory --> next motor. ) encoded within the two known populations of HVc projection neurons. The sensory link in the chain is provided, not by auditory feedback, but by a centrally generated efference copy that serves as an internal prediction of this feedback. The use of efference copy as a substitute for the sensory signal explains the ability of adult birds to produce normal song immediately after deafening. We also predict that the AFP guides sequence learning by biasing motor activity in nucleus RA, the premotor nucleus downstream of HVc. Associative learning then remaps the output of the HVc sequence generator. By altering the motor pathway in RA, the AFP alters the correspondence between HVc motor commands and the resulting sensory feedback and triggers renewed efference copy learning in HVc. Thus, auditory feedback-mediated efference copy learning provides an indirect pathway by which the AFP can influence sequence generation in HVc. The model makes predictions concerning the role played by specific neural populations during the sensorimotor phase of song learning and demonstrates how simple rules of associational plasticity can contribute to the learning of a complex behavior on multiple time scales.

Animals↗

PubMed2002

The effects of galanin on long-term synaptic plasticity in the CA1 area of rodent hippocampus.

Learning and memory involve complex changes in neuronal excitability including long-lasting synaptic plasticity of glutamatergic synapses. The observation that the neuropeptide galanin affects performance in a number of behavioural models predicts that galanin should affect synaptic processes underlying learning. The present study in rat and mouse hippocampal slices now demonstrates that galanin inhibits long-term potentiation induced by both tetanic and theta-burst stimulation in both apical and basal dendrites of CA1 pyramidal neurones but does not affect long-term depression. This selective effect on long-term potentiation does not appear to be mediated through inhibition of N-methyl-D-aspartate or metabotropic glutamate receptor function, but likely resides downstream of receptor activation, possibly at the level of the kinase cascade that converts short-term into long-term potentiation. Our results indicate possible mechanisms by which the neuropeptide galanin may act at the molecular level to influence learning and memory in vertebrates.

Animals↗

PubMed2025

Plasma Proteomic Profiles Predict Individual Future Osteoarthritis Risk.

OBJECTIVE: Osteoarthritis (OA) is a widespread degenerative joint disease that causes a considerable socioeconomic burden. Despite progress in genetic and environmental insights, early diagnosis is still limited by the lack of evident symptoms during the initial phases and accurate biomarkers. This study aims to identify plasma proteins associated with future risk of OA and develop a predictive model. METHODS: We conducted a large-scale proteomic analysis of 45,307 participants from the UK Biobank, excluding those with baseline OA. Plasma samples were assayed using the Olink Explore Proximity Extension Assay targeting 1,463 unique proteins. Clinical variables and OA outcomes were extracted and linked to electronic health records. A predictive model was constructed using the LightGBM machine learning method, and SHapley Additive exPlanations (SHAP) were applied to evaluate the importance of variables. RESULTS: We identified a panel of proteins significantly associated with the risk of developing OA. Notably, after adjusting for multiple confounders, collagen type IX alpha 1 chain (COL9A1) and cartilage acidic protein 1 (CRTAC1) were the most significant predictors of incident OA, with hazard ratios of 1.54 (95% confidence interval [CI] 1.48-1.61) and 1.65 (95% CI 1.54-1.78), respectively. SHAP analysis allowed a profound interpretation of the contribution of each protein and clinical variable to the model, revealing the multifactorial nature of OA risk prediction. The temporal trajectories of plasma proteins indicated that the levels of COL9A1 and CRTAC1 began to deviate from normal for more than a decade before OA onset, suggesting their potential use in early detection strategies. The predictive model, developed using the LightGBM algorithm, integrated proteins with clinical covariates and demonstrated an area under the curve (AUC) of 0.729 for 5-year OA prediction, 0.721 for 10-year prediction, and 0.723 for all incident OA. The predictive accuracy of the model was further enhanced for hip and knee OA, achieving AUCs of 0.820 and 0.803 for 5-year predictions. CONCLUSION: Our study identified the role of plasma proteomics in predicting future OA risk, which could contribute to preemptive measures. The innovative model, which integrates proteomic biomarkers with clinical data, offers a potential tool for risk assessment, potentially optimizing OA management strategies and enhancing prevention efforts.

Humans↗

PubMed2026

ExoShorkie: predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning.

MOTIVATION: Predicting the RNA-seq coverage of native and exogenous sequences is central to many molecular- and synthetic-biology applications. Substantial progress has been made in developing methods to predict the RNA-seq coverage of native genomic sequences, with the recently developed Shorkie achieving state-of-the-art performance in yeast. However, prediction performance of these methods over exogenous DNA is still unknown. Recent studies measured RNA-seq coverage of large exogenous genomes in yeast, providing a unique opportunity to train machine-learning models on a large exogenous sequence space and to improve both prediction performance and our understanding of regulatory mechanisms. RESULTS: We introduce ExoShorkie, a method we developed by extending Shorkie through transfer learning across multiple exogenous RNA-seq datasets. We demonstrate that ExoShorkie significantly improves prediction performance on held-out exogenous genomes and outperforms both a native-genome-trained Shorkie baseline and Yorzoi, the only competing method for predicting exogenous RNA-seq coverage in yeast, in cross-validation and in leave-one-genome-out evaluations. Furthermore, through interpretability analyses we reveal biologically meaningful regulatory motifs and distinct regulatory rules in exogenous genomes in yeast, providing new insights into transcriptional regulation. AVAILABILITY AND IMPLEMENTATION: ExoShorkie is available at https://github.com/OrensteinLab/ExoShorkie.

Genome, Fungal↗