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PubMed2025

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data, which is pivotal to understanding the nature of complex molecular systems. Here, we show how to construct a simple model using automated machine learning (AutoML) to predict protein abundance in Schizosaccharomyces pombe, using data obtained from codon usage bias and quantitative proteomics.

Machine Learning↗

PubMed2025

The signed two-space proximity model for learning representations in protein-protein interaction networks.

MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. RESULTS: S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns. AVAILABILITY: S2-SPM is implemented and freely available under the MIT license at https://github.com/Nicknakis/S2SPM.

Protein Interaction Mapping↗

PubMed2001

Spike-timing-dependent Hebbian plasticity as temporal difference learning.

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physio-logically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

Action Potentials↗

PubMed1989

Causal explanations and emotional health of women during divorce.

This study investigated (a) relationships between women's causal explanations for divorce and their present emotional health, and (b) the clinical usefulness of the Personal Stress Inventory (Ireton, 1980) as a stress assessment tool. The convenience sample consisted of 36 women ending a first marriage. Subjects were interviewed using the Tennessee Self-Concept Scale (Fitts, 1965), the Personal Stress Inventory, and a causal explanations questionnaire. Results indicated that causal explanations did predict self-esteem and emotional distress under conditions suggested by the reformulated learned helplessness model. The Personal Stress Inventory includes an emotional distress subscale that predicted self-esteem, self-perceptions of coping ability, and overall concerns about wellness/energy.

Adaptation, Psychological↗

PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗

PubMed2002

The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves.

Learning curves are presented as an unbiased means for evaluating the performance of models for neuroimaging data analysis. The learning curve measures the predictive performance in terms of the generalization or prediction error as a function of the number of independent examples (e.g., subjects) used to determine the parameters in the model. Cross-validation resampling is used to obtain unbiased estimates of a generic multivariate Gaussian classifier, for training set sizes from 2 to 16 subjects. We apply the framework to four different activation experiments, in this case [(15)O]water data sets, although the framework is equally valid for multisubject fMRI studies. We demonstrate how the prediction error can be expressed as the mutual information between the scan and the scan label, measured in units of bits. The mutual information learning curve can be used to evaluate the impact of different methodological choices, e.g., classification label schemes, preprocessing choices. Another application for the learning curve is to examine the model performance using bias/variance considerations enabling the researcher to determine if the model performance is limited by statistical bias or variance. We furthermore present the sensitivity map as a general method for extracting activation maps from statistical models within the probabilistic framework and illustrate relationships between mutual information and pattern reproducibility as derived in the NPAIRS framework described in a companion paper.

Adult↗

PubMed2000

Distance-based reconstruction of tree models for oncogenesis.

Comparative genomic hybridization (CGH) is a laboratory method to measure gains and losses in the copy number of chromosomal regions in tumor cells. It is hypothesized that certain DNA gains and losses are related to cancer progression and that the patterns of these changes are relevant to the clinical consequences of the cancer. It is therefore of interest to develop models which predict the occurrence of these events, as well as techniques for learning such models from CGH data. We continue our study of the mathematical foundations for inferring a model of tumor progression from a CGH data set that we started in Desper et al. (1999). In that paper, we proposed a class of probabilistic tree models and showed that an algorithm based on maximum-weight branching in a graph correctly infers the topology of the tree, under plausible assumptions. In this paper, we extend that work in the direction of the so-called distance-based trees, in which events are leaves of the tree, in the style of models common in phylogenetics. Then we show how to reconstruct the distance-based trees using tree-fitting algorithms developed by researchers in phylogenetics. The main advantages of the distance-based models are that 1) they represent information about co-occurrences of all pairs of events, instead of just some pairs, 2) they allow quantitative predictions about which events occur early in tumor progression, and 3) they bring into play the extensive methodology and software developed in the context of phylogenetics. We illustrate the distance-based tree method and how it complements the branching tree method, with a CGH data set for renal cancer.

Algorithms↗

PubMed2001

Seeing mum drinking a 'light' product: is social learning a stronger determinant of taste preference acquisition than caloric conditioning?

OBJECTIVE: It was examined whether caloric conditioning or social learning strategies dominate in taste preference acquisition in children. The caloric learning paradigm predicts that eating or drinking artificially sweetened products, which deliver virtually no energy, will not lead to a taste preference whereas the social learning paradigm predicts that seeing important others modelling the eating and drinking of these 'light' products will induce a preference for the taste of light products in the child. DESIGN: In a 2 x 2 between subjects factorial design, the amount of energy and social modelling was varied. SETTING: The study was undertaken at primary schools in Maastricht, The Netherlands. SUBJECTS: Forty-five children participated and six children dropped out. The 39 children who completed the study (14 boys and 25 girls) had a mean age of 67 months (range 51--81, s.d. 5.6). INTERVENTIONS: Each subject took part in nine conditioning trials with an individually selected tasting yoghurt which was not preferred very much at the pre-test. RESULTS: The children in the combined caloric and social condition showed an increase in their preference for the conditioned taste which was larger than a regression-to-the-mean effect (P=0.007), whereas children in the other groups did not. CONCLUSION: Caloric and social learning combined, ie modelling the consumption of energy-rich foods or drinks, is the best way to establish taste preferences. Children more easily learn a preference for energy-rich food that is eaten by significant others than for food that is low in energy and eaten by significant others.

Child↗

PubMed2000

Volume of focal brain lesions and hippocampal formation in relation to memory function after closed head injury in children.

OBJECTIVES: (1) A study of verbal learning and memory in children who had sustained a closed head injury (CHI) at least 3 months earlier. (2) To relate memory function to focal brain lesion and hippocampal formation volumes using morphometric analysis of MRI. METHODS: A group of 245 children who had been admitted to hospital for CHI graded by the Glasgow coma scale (GCS), including 161 patients with severe and 84 with mild CHI completed the California verbal learning test (CVLT) and underwent MRI which was analysed for focal brain lesion volume independently of memory test data. Brain MRI with 1.5 mm coronal slices obtained in subsets of 25 patients with severe and 25 patients with mild CHI were analysed for hippocampal formation volume. Interoperator reliability in morphometry was satisfactory. RESULTS: Severity of CHI and age at study significantly affected memory performance. Regression analysis showed that bifrontal, left frontal, and right frontal lesion volumes incremented prediction of various learning and memory indices after entering the GCS score and age into the model. Extrafrontal lesion volume did not contribute to predicting memory performance. CONCLUSIONS: Prefrontal lesions contribute to residual impairment of learning and memory after severe CHI in children. Although effects of CHI on hippocampal formation volume might be difficult to demonstrate in non-fatal paediatric CHI, further investigation using functional brain imaging could potentially demonstrate hippocampal dysfunction.

Adolescent↗

PubMed1996

A sequence predicting CA3 is a flexible associator that learns and uses context to solve hippocampal-like tasks.

The model discussed in this paper is, by hypothesis, a minimal, biologically plausible model of hippocampal region CA3. Because cognitive mapping can be viewed as a sequence prediction problem, we qualify this model as a successful sequence predictor. Since the model solves problems which require the use of context, the model is also able to learn and use context. The model also solves configural learning problems of which, at least one, requires a hippocampus. Thus, by solving sequence problems, by solving configural learning problems, and by creating codes for context, this model provides a computational unification of hippocampal functions which are often viewed as disparate.

Cognition↗

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↗

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↗

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↗