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At least 397 records · Page 22Linked to original sources

PubMed2026

BaGGLS: a Bayesian shrinkage framework for interpretable modeling of interactions in high-dimensional biological data.

MOTIVATION: Biological data is often high dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable feature selection. Tasks such as identifying motif interactions in genomics exemplify these difficulties, as only a small subset of biologically relevant features (e.g. motifs) are typically active, and their effects are often non-linear and context-dependent. While statistical approaches often result in more interpretable models, deep learning models have proven effective in modeling complex interactions and prediction accuracy, yet their black-box nature limits interpretability. RESULTS: We introduce BaGGLS, a flexible and interpretable probabilistic binary regression model designed for high-dimensional biological inference involving feature interactions. BaGGLS incorporates a Bayesian group global-local shrinkage prior, aligned with the group structure introduced by interaction terms. This prior encourages sparsity while retaining interpretability, helping to isolate meaningful signals and suppress noise. To enable scalable inference, we employ a partially factorized variational approximation that captures posterior skewness and supports efficient learning even in large feature spaces. In extensive simulations, we compare BaGGLS to frequentist probit regressions (unconstrained and with L1-penalty) as well as a probit model with Markov Chain Monte Carlo (MCMC) sampling under a horseshoe prior. We can show that BaGGLS outperforms the other methods with regard to interaction detection and is many times faster than MCMC sampling under the horseshoe prior. We also demonstrate the usefulness of BaGGLS in the context of interaction discovery from motif scanner outputs (e.g. Find Individual Motif Occurrences (FIMO)) and noisy attribution scores from deep learning models. This shows that BaGGLS is a promising approach for uncovering biologically relevant interaction patterns, with potential applicability across a range of high-dimensional tasks in computational biology. AVAILABILITY: Code is available at gitlab.com/dacs-hpi/baggls.

Bayes Theorem↗

PubMed2003

Chaotic itinerancy in coupled dynamical recognizers.

We argue that chaotic itinerancy in interaction between humans originates in the fluctuation of predictions provided by the nonconvergent nature of learning dynamics. A simple simulation model called the coupled dynamical recognizer is proposed to study this phenomenon. Daily cognitive phenomena provide many examples of chaotic itinerancy, such as turn taking in conversation. It is therefore an interesting problem to bridge two chaotic itinerant phenomena. A clue to solving this is the fluctuation of prediction, which can be translated as "hot prediction" in the context of cognitive theory. Hot prediction is simply defined as a prediction based on an unstable model. If this approach is correct, the present simulation will reveal some dynamic characteristics of cognitive interactions.

Cognition↗

PubMed2025

A comparative study highlights superiority of LSTM in crop genomic prediction.

We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic prediction (GP) has been developed as an important method supporting crop breeding. By utilizing the phenotype values result from GP, breeders could make decisions in the seedling stage that consequently benefit for cost saving. In recent years, machine learning emerged as an efficient technology to solve modeling problems in many fields, including crop breeding. However, numerous modeling approaches have hindered the application of GP since breeders struggle to choose. Therefore, a comprehensively methodological research with guiding significance is extremely necessary. In the present study, we systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods. As for genomic feature processing, we found feature selection (SNP filtering approach) performed better than feature extraction (PCA method). Specifically, the feature relationship dependent methods (GBLUP, RNN, and LSTM) as well as DNN architecture showed superior performance with feature selection. Marker density analysis showed positive correlation with prediction accuracy in a limited threshold. Comparison on effect of population size demonstrated a positive correlation between trait genetic complexity and the optimal population size required. By testing fifteen modeling methods, we found LSTM network displayed superior performance, achieving the highest average STScore (0.967) across six datasets. Further research using all cell states or the latest cell states of LSTM inputs demonstrated its architecture particularly adept with capturing additive and epistatic QTL effects among SNPs. In conclusion, our findings provide basic principles for implementing GP in breeding project to maximize prediction accuracy while maintaining cost-effectiveness.

Plant Breeding↗

PubMed1997

Protein kinase C, learning and memory: a circular determinism between physiology and behaviour.

1. In vertebrates as in invertebrates, protein kinase C appears to have a key role in learning and memory, probably given its involvement in synaptic plasticity. 2. Hippocampal PKC in mammalians is activated by learning in a large variety of memory tasks. However, the kind of information processed, the type of task, and the dynamics of learning processes all induce differential changes in the mode of PKC activation and in its anatomy. 3. The behaviourally induced changes in PKC activity are often varying in their magnitude. Inter-individual differences in PKC basal activity are generally correlated to the ability to learn. 4. Pharmacologic activation and inhibition of brain PKC shows that PKC activation plays an important role in cognitive function. 5. Basal PKC stores characterising each individual could be determined by genetic factors and modulated through life by individual experience. 6. The issue of PKC and memory relationships is reformulated through a comprehensive interactionist model which leads to formulating some new testable predictions.

Animals↗

PubMed2003

What can animal models tell us about human sexual response?

In all species, sexual behavior is directed by a complex interplay between steroid hormone actions in the brain that give rise to sexual arousability and experience with sexual reward that gives rise to expectations of competent sexual activity, including sexual arousal, desire, and performance. Sexual experience allows animals to form instrumental and Pavlovian associations that predict sexual outcome and thereby directs the strength of sexual responding. Although the study of animal sexual behavior by neuroendocrinologists has traditionally been concerned with mechanisms of copulatory responding, more recent use of conditioning and preference paradigms, and a focus on environmental circumstances and experience, has revealed behaviors and processes that resemble human sexual responses. In this paper, we review behavioral paradigms used with rodents and other species that are analogous or homologous to human sexual arousal, desire, reward, and inhibition. The extent to which these behavioral paradigms offer predictive validity and practicality as preclinical tools and models is discussed. Identification of common neurochemical and neuroanatomical substrates of sexual responding between animals and humans suggests that the evolution of sexual behavior has been highly conserved and indicates that animal models of human sexual response can be used successfully as preclinical tools.

Animals↗

PubMed2006

Linking animal models of psychosis to computational models of dopamine function.

Psychosis is linked to dysregulation of the neuromodulator dopamine and antipsychotic drugs (APDs) work by blocking dopamine receptors. Dopamine-modulated disruption of latent inhibition (LI) and conditioned avoidance response (CAR) have served as standard animal models of psychosis and antipsychotic action, respectively. Meanwhile, the 'temporal difference' algorithm (TD) has emerged as the leading computational model of dopamine neuron firing. In this report TD is extended to include action at the level of dopamine receptors in order to explain a number of behavioral phenomena including the dose-dependent disruption of CAR by APDs, the temporal dissociation of the effects of APDs on receptors vs behavior, the facilitation of LI by APDs, and the disruption of LI by amphetamine. The model also predicts an APD-induced change to the latency profile of CAR--a novel prediction that is verified experimentally. The model's primary contribution is to link dopamine neuron firing, receptor manipulation, and behavior within a common formal framework that may offer insights into clinical observations.

Action Potentials↗

PubMed2026

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 688 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

DNA methylation↗

PubMed1998

Similarity-based models of human visual recognition.

Seven models of human visual recognition from cognitive psychology, visual psychophysics and connectionism were compared. They were used to predict psychophysical classification data obtained via supervised learning with parametrised grey-level patterns (compound Gabor signals). Four sets of learning patterns, as well as foveal and extrafoveal viewing conditions, were applied. Model performance was determined by comparing observed and predicted data with respect to root mean square deviation and to signal reconstruction via multidimensional scaling. Results show that a psychophysical theory of classification requires a similarity concept that is based both on physical signal description and on cognitive bias. The latter is less pronounced in foveal recognition, where all seven models performed almost equally well, but matters in extrafoveal recognition. Virtual prototype models (Rentschler et al. (1994), Vision Research 34, 669-687), which best accommodate stimulus- and observer-dependencies, are then of advantage. Concerning computational efficiency, a hyperBF model (Poggio and Girosi (1990), Science 247, 978) was much faster, and generalized signal detection models were much slower than the average.

Cognition↗

PubMed2006

Machine learning techniques in disease forecasting: a case study on rice blast prediction.

BACKGROUND: Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. RESULTS: Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year) were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG) approach achieved an average correlation coefficient (r) of 0.50, which increased to 0.60 and percent mean absolute error (%MAE) decreased from 65.42 to 52.24 when back-propagation neural network (BPNN) was used. With generalized regression neural network (GRNN), the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM) based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. CONCLUSION: Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also developed a SVM-based web server for rice blast prediction, a first of its kind worldwide, which can help the plant science community and farmers in their decision making process. The server is freely available at http://www.imtech.res.in/raghava/rbpred/.

Agriculture↗

PubMed2006

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation.

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.

China↗

PubMedDate not supplied

Neuromodulation and plasticity in an autonomous robot.

In this paper we implement a computational model of a neuromodulatory system in an autonomous robot. The output of the neuromodulatory system acts as a value signal, modulating widely distributed synaptic changes. The model is based on anatomical and physiological properties of midbrain diffuse ascending systems, in particular parts of the dopamine and noradrenaline systems. During reward conditioning, the model learns to generate tonic and phasic signals that represent predictions and prediction errors, including precisely timed negative signals if expected rewards are omitted or delayed. We test the robot's learning and behavior in different environmental contexts and observe changes in the development of the neuromodulatory system that depend upon environmental factors. Simulation of a computational model incorporating both reward-related and aversive stimuli leads to the emergence of conditioned reward and aversive behaviors. These studies represent a step towards investigating computational aspects of neuromodulatory systems in autonomous robots.

Models, Neurological↗

PubMed2006

The misbehavior of value and the discipline of the will.

Most reinforcement learning models of animal conditioning operate under the convenient, though fictive, assumption that Pavlovian conditioning concerns prediction learning whereas instrumental conditioning concerns action learning. However, it is only through Pavlovian responses that Pavlovian prediction learning is evident, and these responses can act against the instrumental interests of the subjects. This can be seen in both experimental and natural circumstances. In this paper we study the consequences of importing this competition into a reinforcement learning context, and demonstrate the resulting effects in an omission schedule and a maze navigation task. The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined.

Animals↗

PubMed2005

Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: a case study on renal function evaluation.

OBJECTIVE: In many medical areas, there exist different regression formulas to predict/evaluate a medical outcome on the same problem, each of them being efficient only in a particular sub-space of the problem space. The paper aims at the development of a generic, incremental learning model that includes all available regression formulas for a particular prediction problem to define local areas of the problem space with their best performing formula along with useful explanation rules. Another objective of the paper is to develop a specific model for renal function evaluation using nine existing formulas. METHODS AND MATERIALS: We have used a connectionist neuro-fuzzy approach and have developed a knowledge-based neural network model (KBNN) which incorporates and adapts incrementally several existing regression formulas and kernel functions. The model incorporates different non-linear regression functions as neurons in its hidden layer and adapts these functions through incremental learning from data in particular local areas of the space. More specifically, each hidden neural node has a pair of functions associated with it--one regression formula, that represents existing knowledge and one Gaussian kernel function, that defines the sub-space of the whole problem space, in which the formula is locally adapted to new data. All these functions are aggregated and changed through incremental learning. The proposed KBNN model is illustrated using a medical dataset of observed patient glomerular filtration rate (GFR) measurements for renal function evaluation. In this case study, the regression function for each cluster is selected by the model from nine formulas commonly used by medical practitioners to predict GFR. 441 GFR data vectors from 141 patients taken from 12 sites in Australia and New Zealand have been used as a case study experimental data set. RESULTS: The proposed GFR prediction model, based on the proposed generic KBNN model, outperforms at least by 10% accuracy any of the individual regression formulas or a standard neural network model. Furthermore, we have derived locally adapted regression formulas to perform best on local clusters of data along with useful explanatory rules. CONCLUSION: The proposed KBNN model manifests better accuracy then existing regression formulas or neural network models for renal function evaluation and extracts modified formulas that perform well in local areas of the problem space.

Algorithms↗

PubMed2005

Stability criteria for unsupervised temporal association networks.

A biologically realizable, unsupervised learning rule is described for the online extraction of object features, suitable for solving a range of object recognition tasks. Alterations to the basic learning rule are proposed which allow the rule to better suit the parameters of a given input space. One negative consequence of such modifications is the potential for learning instability. The criteria for such instability are modeled using digital filtering techniques and predicted regions of stability and instability tested. The result is a family of learning rules which can be tailored to the specific environment, improving both convergence times and accuracy over the standard learning rule, while simultaneously insuring learning stability.

Learning↗

PubMed1997

A phenomenological variant of ecological systems theory (PVEST): a self-organization perspective in context.

A framework that emphasizes and integrates individuals' intersubjective experiences with Bronfenbrenner's ecological systems theory (PVEST) is introduced and compared with self-organizational perspectives. Similarities, differences and advantages of each framework are described. In a demonstration of PVEST's utility, a subset of data from the 3rd year of a longitudinal study (14- to 16-year-old middle adolescent African-Americans) is used for examining an achievement variable: negative learning attitude. Explored separately by gender, a regression model that contained risk, stress, and a reactive coping variable for the prediction of negative learning attitudes was investigated. For boys, stress was an independent stressor across steps independent of the other variables entered; social support variable, perceived unpopularity with peers, that was a significant predictor of girls' negative learning attitude. Particularly for boys, the findings suggest critically important roles for teachers and peers in the negative learning attitude of midadolescent economically disadvantaged African-American students.

Adolescent↗

PubMed2005

Predicting drug efficacy for cognitive deficits in schizophrenia.

The purpose of this article is to discuss the prediction of cognitive enhancement in schizophrenia from preclinical data. Despite increasing focus on the significance of cognitive impairment in schizophrenia, the progress of novel treatments has been slow. Hyman and Fenton's identification of a "translational gap" between preclinical and clinical science underscores the need to revise preclinical, clinical, and regulatory practice. A review of the clinical literature identifies evidence for some cognitive benefits with current antipsychotics. The magnitude of these effects may, in some cases, be too small to be functionally relevant, and many studies are methodologically flawed, but the data might nevertheless allow translational links to be identified between clinical and preclinical studies. The literature is reviewed to determine if the cognitive signal reported in clinical studies is detectable in preclinical studies. The effects of antipsychotics on prepulse-inhibition deficits in animals is robust and demonstrates a reversal of drug-induced and developmentally induced deficits, although predictive links to the clinic are not well established. The preclinical literature on antipsychotic effects on attention, learning and memory, and recognition and executive function shows, with rare exceptions, impaired learning or task performance, rather than improvement. In general, therefore, preclinical studies have not detected the small pro-cognitive signal evident in the clinical literature. A number of factors may account for this. Effective closure of the translation gap for cognitive deficits in schizophrenia will require the design of a coherent preclinical strategy, and some of the potential elements of such a strategy are outlined and discussed.

Animals↗

PubMed1998

Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form.

Artificial Neural Networks (ANN) methodology was used to assess experimental data from a tablet compression study showing highly non-linear relationships (i.e. measurements of ejection forces) and compared to classical modelling technique (i.e. Response Surface Methodology, RSM). These kinds of relationships are known to be difficult to model using classical methods. The aim of this investigation was to quantitatively describe the achieved degree of data fitting and predicting abilities of the developed models. The comparison between the ANN and RSM was carried out both graphically and numerically. For comparing the goodness of fit, all data were used, whereas for the goodness of prediction the data were split into a learning and a validation data set. Better results were achieved for the model using ANN methodology with regard to data fitting and predicting ability. All determined ejection properties were mainly influenced by the concentration of magnesium stearate and silica aerogel, whereas the other factors showed very much lower effects. Important relationships could be recognised from the ANN model only, whereas the RSM model ignored them. The ANN methodology represents a useful alternative to classical modelling techniques when applied to variable data sets presenting non-linear relationships.

Chemistry, Pharmaceutical↗

PubMed1994

Do ability-performance relationships differentiate age and practice effects in visual search?

Relationships between abilities and performance in visual search were investigated for young and old adults. Ss received extensive practice on category search task. A consistent version allowed development of an automatic attention response; a varied version allowed general performance improvements. Transfer conditions assessed learning. General ability, induction, semantic knowledge, working memory, perceptual speed, semantic memory access, and psychomotor speed were assessed. LISREL models revealed that general ability and semantic memory access predicted initial performance for both ages. Improvements on both the consistent and varied tasks were predicted by perceptual speed. Ability-performance relationships indexed performance changes but were not predictive of learning (i.e., automatic process vs. general efficiency). Qualitative differences in the ability-transfer models suggest age differences in learning.

Adolescent↗