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Results for “Predictive Learning Models”

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PubMed2026

Integrative machine learning models to unravel gut microbial dysbiosis and functional disruption in polycystic ovary syndrome.

OBJECTIVE: To study gut microbial diversity and metabolic pathway disruptions in women with PolyCystic Ovary Syndrome (PCOS) compared with healthy controls, and to evaluate the diagnostic potential of microbiome-driven machine learning models. DESIGN: Case-controlled metagenomic data analysis SUBJECTS: Gut metagenomic data from women diagnosed with PCOS and age-matched healthy female controls EXPOSURE: Presence of PCOS MAIN OUTCOME MEASURES: The primary outcome measures will include gut microbial alpha and beta diversity indices, microbial taxon abundance, functional pathway profiles, predicted metabolite levels, microbe-functional pathway-metabolite interaction networks, and the diagnostic accuracy of microbiome-based machine learning models. RESULTS: Alpha and beta diversity analyses revealed marked gut microbial dysbiosis in women with PCOS, despite comparable species richness to healthy controls. Differential abundance analysis identified 41 significantly altered microbial species, including enrichment of proinflammatory taxa, such as Bacteroides vulgatus and Ruminococcus gnavus, and depletion of beneficial commensals, including Roseburia hominis and Prevotella copri. These compositional shifts indicate a proinflammatory microbial community structure in PCOS. Functional profiling demonstrated the upregulation of pathways involved in nucleotide turnover, lipid and carbohydrate metabolism, and neurotransmitter synthesis, potentially contributing to metabolic and neuroendocrine disruption. Network analysis revealed fragmented and unstable microbial-metabolite associations in PCOS compared with cohesive networks in controls. Microbiome-based machine learning models achieved a diagnostic accuracy of 84.25% (area under the curve 0.93), underscoring their predictive potential. CONCLUSION: The gut microbiome in PCOS is characterized by a proinflammatory community structure and disrupted metabolic pathways. These findings demonstrate the diagnostic potential of microbiome-based models and underscore the gut microbiome as a promising target for therapeutic interventions in the management of PCOS.

Polycystic Ovary Syndrome↗

PubMed1998

Signal detection by human observers: a cutoff reinforcement learning model of categorization decisions under uncertainty.

Previous experimental examinations of binary categorization decisions have documented robust behavioral regularities that cannot be predicted by signal detection theory (D.M. Green & J.A. Swets, 1966/1988). The present article reviews the known regularities and demonstrates that they can be accounted for by a minimal modification of signal detection theory: the replacement of the "ideal observer" cutoff placement rule with a cutoff reinforcement learning rule. This modification is derived from a cognitive game theoretic analysis (A.E. Roth & I. Erev, 1995). The modified model reproduces all 19 experimental regularities that have been considered. In all cases,it outperforms the original explanations. Some of these previous explanations are based on important concepts such as conservatism, probability matching, and "the gambler's fallacy" that receive new meanings given the current results. Implications for decision-making research and for applications of traditional signal detection theory are discussed.

Decision Making↗

PubMed2005

Hippocampal replay contributes to within session learning in a temporal difference reinforcement learning model.

Temporal difference reinforcement learning (TDRL) algorithms, hypothesized to partially explain basal ganglia functionality, learn more slowly than real animals. Modified TDRL algorithms (e.g. the Dyna-Q family) learn faster than standard TDRL by practicing experienced sequences offline. We suggest that the replay phenomenon, in which ensembles of hippocampal neurons replay previously experienced firing sequences during subsequent rest and sleep, may provide practice sequences to improve the speed of TDRL learning, even within a single session. We test the plausibility of this hypothesis in a computational model of a multiple-T choice-task. Rats show two learning rates on this task: a fast decrease in errors and a slow development of a stereotyped path. Adding developing replay to the model accelerates learning the correct path, but slows down the stereotyping of that path. These models provide testable predictions relating the effects of hippocampal inactivation as well as hippocampal replay on this task.

Algorithms↗

PubMed2026

Phage bioinformatics tools: a review of computational approaches for bacteriophage research.

Rising clinical interest in phage therapy and the exponential growth of metagenomic sequence catalogues have driven a rapid expansion of bacteriophage bioinformatics. More than 80 dedicated tools, mostly published since 2020, now span identification, assembly, annotation, taxonomy, lifestyle prediction, defence-system detection, and host prediction. Aimed at experienced practitioners and developers, this review synthesizes the field through the lens of three successive computational paradigms: sequence homology, bounded by database completeness; machine learning, constrained by labelled training data; and foundation models, which now achieve Matthews correlation coefficients above 0.95 in identification tasks and, through structure-informed prediction, raise functional annotation to over half of phage genes. Furthermore, we map the upstream components, namely, gene callers, homology engines, protein language models, and structural search tools, that underpin most downstream pipelines, exposing shared infrastructure and ecosystem-level fragility when dependencies change. To translate this into practice, we propose web-based and command-line reference workflows calibrated to user expertise and sample types. Finally, we set an agenda for the next wave of tool development. Roughly half of phage genes still resist functional annotation despite structural methods; no broadly generalizable strain-level host predictor exists for phage therapy; varying true-positive rates (0%-97%) underscore the absence of standardized community benchmarks analogous to Critical Assessment of Structure Prediction or Critical Assessment of Metagenome Interpretation. As generative genome models begin designing synthetic phages, progress will depend less on producing standalone tools than on rigorous evaluation, interoperable infrastructure, and clinically meaningful prediction targets.

Computational Biology↗

PubMed1988

Quasi-dynamic choice models: Melioration and ratio invariance.

There is continuing controversy about the behavioral process or processes that underlie the major regularities of free-operant choice such as molar matching and systematic deviations therefrom. A recent interchange between Vaughan and Silberberg and Ziriax concerned the relative merits of melioration, and a computer simulation of molecular maximizing. There are difficulties in evaluating theories expressed as computer programs because many arbitrary decisions must often be made in order to get the programs to operate. I therefore propose an alternative form of model that I term quasi-dynamic as a useful intermediate form of theory appropriate to our current state of knowledge about free-operant choice. Quasi-dynamic models resemble the game-theoretic analyses now commonplace in biology in that they can predict stable and unstable equilibria but not dynamic properties such as learning curves. It is possible to interpret melioration as a quasi-dynamic model. An alternative quasi-dynamic model for probabilistic choice, ratio invariance, has been proposed by Horner and Staddon. The present paper compares the predictions of melioration and ratio invariance for five experimental situations: concurrent variable-interval variable-interval schedules, concurrent variable-interval variable-ratio schedules, the two-armed bandit (concurrent random-ratio schedules), and two types of frequency-dependent schedule. Neither approach easily explains all the data, but ratio invariance seems to provide a better picture of pigeons' response to probabilistic choice procedures. Ratio invariance is also more adaptive (less susceptible to "traps") and closer to the original expression of the law of effect than pure hill-climbing processes such as momentary maximizing and melioration, although such processes may come in to play on more complex procedures that provide opportunities for temporal discrimination.

Journal Article↗

PubMed2025

EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.

MOTIVATION: Enhancers and promoters, as regulatory DNA elements, play pivotal roles in gene expression, homeostasis, and disease development across various biological processes. With advancing research, it has been uncovered that distal enhancers may engage with nearby promoters to modulate the expression of target genes. This discovery holds significant implications for deepening our comprehension of various biological mechanisms. In recent years, numerous high-throughput wet-lab techniques have been created to detect possible interactions between enhancers and promoters. However, these experimental methods are often time-intensive and costly. RESULTS: To tackle this issue, we have created an innovative deep learning approach, EPIPDLF, which utilizes advanced deep learning techniques to predict EPIs based solely on genomic sequences in an interpretable manner. Comparative evaluations across six benchmark datasets demonstrate that EPIPDLF consistently exhibits superior performance in EPI prediction. Additionally, by incorporating interpretable analysis mechanisms, our model enables the elucidation of learned features, aiding in the identification and biological analysis of important sequences. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at: https://github.com/xzc196/EPIPDLF.

Deep Learning↗

PubMed2025

Estimating the association of antimicrobial resistance genes with minimum inhibitory concentration in Escherichia coli: an observational study.

BACKGROUND: Surveillance and prediction of antibiotic resistance in Escherichia coli relies on curated databases of genes and mutations. We aimed to quantify the effect of acquiring specific genetic elements on minimum inhibitory concentrations (MICs) for particular antibiotic-species combinations, addressing the current scarcity of such data in existing databases. METHODS: For this observational study, we evaluated a collection of E coli isolates with linked whole-genome sequencing and MIC data, originating from human urinary or bloodstream infections obtained from the Oxford University Hospitals National Health Service Foundation Trust in Oxfordshire, UK. We used multivariable interval regression models to estimate the change in MIC (with 95% CIs) for specific antibiotics associated with the acquisition of antibiotic resistance genes and associated mutations in the National Center for Biotechnology Information AMRFinder database, with and without an adjustment for population structure. We then tested the ability of these models to predict MIC and binary resistance or susceptibility using leave-one-out cross-validation. FINDINGS: We evaluated 2875 E coli isolates obtained during 2013-2018 and 2020. Although most ARGs and resistance mutations (89 [80%] of 111) were associated with an increased MIC, a much smaller number (27 [24%] of 111) was found to be putatively independently resistance-conferring (ie, associated with an MIC above the European Committee on Antimicrobial Susceptibility Testing breakpoint) when acquired in isolation. We found evidence of differential effects of acquired ARGs and resistance mutations between different generations of cephalosporin antibiotics and showed that sub-breakpoint variation in MIC can be linked to genetic mechanisms of resistance. 20 697 (83·3%; range 52·9-97·7 across all antibiotics) of 24 858 MICs were correctly exactly predicted and 23 677 (95·2%; 87·3-97·7) of 24 858 MICs were predicted to within one doubling dilution. INTERPRETATION: Quantitative estimates of the independent effect of the acquisition of ARGs on MIC add to the interpretability and utility of existing databases. Compared with approaches using machine learning models, the use of these estimates yields similar or better performance in the prediction of antibiotic resistance phenotype with more readily interpretable results. The methods outlined here could be readily applied to other antibiotic-pathogen combinations. FUNDING: The National Institute for Health and Care Research (NIHR) and the Medical Research Council (MRC).

Escherichia coli↗

PubMed2003

FrameD: A flexible program for quality check and gene prediction in prokaryotic genomes and noisy matured eukaryotic sequences.

We describe FrameD, a program that predicts coding regions in prokaryotic and matured eukaryotic sequences. Initially targeted at gene prediction in bacterial GC rich genomes, the gene model used in FrameD also allows to predict genes in the presence of frameshifts and partially undetermined sequences which makes it also very suitable for gene prediction and frameshift correction in unfinished sequences such as EST and EST cluster sequences. Like recent eukaryotic gene prediction programs, FrameD also includes the ability to take into account protein similarity information both in its prediction and its graphical output. Its performances are evaluated on different bacterial genomes. The web site (http://genopole.toulouse.inra.fr/bioinfo/FrameD/FD) allows direct prediction, sequence correction and translation and the ability to learn new models for new organisms.

Computer Graphics↗

PubMed2026

DeepGeSeq: deep learning library for genomic sequence modeling and analysis.

MOTIVATION: Deep learning methods have demonstrated significant potential in genomics, enabling broad applications such as sequence activity prediction, regulatory rule identification, and variant effect quantification. However, their widespread adoption is often hindered by the steep computational learning curve required for model construction, training, and downstream biological interpretation. Here, we introduce DeepGeSeq, a user-friendly Deep-learning library tailored for Genomic Sequence modeling and analysis. RESULTS: By integrating state-of-the-art architectural modules, DeepGeSeq streamlines the entire deep learning workflow, requiring minimal user input via a simple configuration file and an intuitive agentic skill. We comprehensively validate the efficacy of DeepGeSeq through diverse case studies, encompassing pipeline verification using synthetic datasets, the reproduction and application of established models, and model fine-tuning coupled with biological interpretation on user-defined data. Furthermore, we demonstrate DeepGeSeq's versatility in domain-specific applications, including single-cell ATAC-seq modeling for cell-type clustering, and MPRA data modeling coupled with in silico saturation mutagenesis to dissect cis-regulatory elements. Ultimately, DeepGeSeq bridges the gap between computational complexity and biological discovery, providing an accessible resource that facilitates the development and broad application of deep learning methods in genomics research. AVAILABILITY AND IMPLEMENTATION: https://github.com/JiaqiLi1024/DeepGeSeq.

Deep Learning↗

PubMed2002

Under what conditions is recognition spared relative to recall after selective hippocampal damage in humans?

The claim that recognition memory is spared relative to recall after focal hippocampal damage has been disputed in the literature. We examined this claim by investigating object and object-location recall and recognition memory in a patient, YR, who has adult-onset selective hippocampal damage. Our aim was to identify the conditions under which recognition was spared relative to recall in this patient. She showed unimpaired forced-choice object recognition but clearly impaired recall, even when her control subjects found the object recognition task to be numerically harder than the object recall task. However, on two other recognition tests, YR's performance was not relatively spared. First, she was clearly impaired at an equivalently difficult yes/no object recognition task, but only when targets and foils were very similar. Second, YR was clearly impaired at forced-choice recognition of object-location associations. This impairment was also unrelated to difficulty because this task was no more difficult than the forced-choice object recognition task for control subjects. The clear impairment of yes/no, but not of forced-choice, object recognition after focal hippocampal damage, when targets and foils are very similar, is predicted by the neural network-based Complementary Learning Systems model of recognition. This model postulates that recognition is mediated by hippocampally dependent recollection and cortically dependent familiarity; thus hippocampal damage should not impair item familiarity. The model postulates that familiarity is ineffective when very similar targets and foils are shown one at a time and subjects have to identify which items are old (yes/no recognition). In contrast, familiarity is effective in discriminating which of similar targets and foils, seen together, is old (forced-choice recognition). Independent evidence from the remember/know procedure also indicates that YR's familiarity is normal. The Complementary Learning Systems model can also accommodate the clear impairment of forced-choice object-location recognition memory if it incorporates the view that the most complete convergence of spatial and object information, represented in different cortical regions, occurs in the hippocampus.

Brain Diseases↗

PubMed1994

A possible neural mechanism underlying consciousness based on the pattern processing capabilities of pyramidal neurons in the cerebral cortex.

This paper examines a possible neural mechanism underlying the phenomenon of consciousness by exploring the ability of cortical pyramidal cells to process patterns of information. A numerical model of a section of a neuron is described that enables the transient membrane potential distribution to be predicted following inputs to the cell. The neuron model incorporates learning by modelling the ability of dendritic spine receptors to undergo changes in their sensitivity if they receive inputs when their local membrane is depolarized. Simulations show the cell to be capable of recognizing patterns amongst its inputs, and to be able to extend its repertoire of learnt patterns by associating one pattern of inputs with another. These pattern processing capabilities can take place within the apical dendritic tree, whilst the soma sees a much attenuated and slower response that reflects the general level of pattern recognition taking place in the dendrites. It is argued here that the distribution of patterns stored in the apical dendrites of all the pyramidal cells represents the cortical knowledge base. Incoming patterns of information are compared with those stored in the cortical knowledge base to pick out components that have been experienced before. The resulting distribution of soma responses represents the way the incoming patterns of information are perceived. Most of the patterns learnt arise from other pyramidals and represent information about cortical behaviour itself. The distribution of soma responses therefore represents a perception of the self as well as a perception of the environment. It is argued here that this intimate perception of the self could underlie the phenomenon of consciousness.

Cerebral Cortex↗

PubMed2025

Oncogenic EME1 promotes tumor progression and immune modulation in human cancers with therapeutic targeting potential.

BACKGROUND: EME1, a critical DNA repair endonuclease, has emerged as a potential oncogene implicated in genome instability and cancer progression. However, its pan-cancer roles, prognostic significance, immune interactions, and therapeutic targeting remain underexplored. METHODS: We conducted a comprehensive pan-cancer analysis integrating multi-omics data from public databases, including TIMER2.0, GEPIA2, TISIDB, and cBioPortal, to evaluate EME1 expression, genetic alterations, and their association with clinical outcomes, immune infiltration, and molecular pathways. Virtual screening of 3180 FDA-approved drugs and molecular dynamics (MD) simulations were employed to identify and validate potential EME1 inhibitors. RESULTS: EME1 was significantly overexpressed in various human cancers and positively associated with advanced tumor grade and stage. High EME1 expression and mutations were linked to poor overall and disease-free survival. Immunogenomic profiling revealed strong positive correlations between EME1 and myeloid-derived suppressor cells (MDSCs), alongside a negative association with endothelial cell function, suggesting immunosuppressive roles. Machine learning models based on EME1-associated genes demonstrated high predictive accuracy for liver hepatocellular carcinoma (AUC > 0.90). Virtual screening identified eight promising drug candidates, including Everolimus and Dioscin, with strong binding affinities. MD simulations confirmed the stability of these interactions, particularly for Dioscin. CONCLUSION: This study reveals the multifaceted oncogenic roles of EME1 in tumor progression, immune evasion, and prognosis. It proposes EME1 as a promising biomarker and therapeutic target across multiple cancer types. The identified drug candidates warrant further in vitro and in vivo validation for potential repurposing in EME1-targeted cancer therapy.

EME1↗

PubMed2005

Interdisciplinary research and education at the biology-engineering-computer science interface: a perspective.

Progress in the life sciences, including genome sequencing and high-throughput experimentation, offers an opportunity for understanding biology and medicine from a systems perspective. This 'new view', which complements the more traditional component-based approach, involves the integration of biological research with approaches from engineering disciplines and computer science. The result is more than a new set of technologies. Rather, it promises a fundamental reconceptualization of the life sciences based on the development of quantitative and predictive models to describe crucial processes. To achieve this change, learning communities are being formed at the interface of the life sciences, engineering and computer science. Through these communities, research and education will be integrated across disciplines and the challenges associated with multidisciplinary team-based science will be addressed.

Biological Science Disciplines↗

PubMed2000

Computational principles of movement neuroscience.

Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.

Animals↗

PubMed2025

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 684 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.

Journal Article↗

PubMedDate not supplied

A compromise for closed system anesthesia.

Closed system anesthesia is economical, minimally, polluting, and conserves a patient's airway heat and moisture. Yet this method of anesthesia is not widely used because it is considered dangerous by many clinicians. We review the origins of that belief and then test the application of 2 schemes for administering potent agents in a closed system with CO2 absorption. We 1st employed Lowe's square-root-of-time uptake model in 30 patients, using halothane or enflurane. We found that the model provided a good starting point for learning to use the closed system. However, anesthetic concentrations were not accurately predicted. Based on our experience with that model, we examined a simpler approach. We began each of 10 anesthetics using a semiclosed system, then closed the system. Only sufficient O2 for metabolic demand and halothane were added to the closed system. The rate of halothane administration was the same for each patient. This approach proved clinically satisfactory, and the measured halothane concentration remained relatively constant during 45 minutes using the closed system. Changing from a semiclosed to a closed system affords the advantages of the closed system 75 percent of the time, yet requires no extra tasks or equipment.

Anesthesia↗

PubMed2005

Rapid reshaping of human motor generalization.

People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.

Adaptation, Physiological↗

PubMed1990

Nefazodone: preclinical pharmacology of a new antidepressant.

Recent pharmacologic studies suggest that nefazodone may possess antidepressant activity. Nefazodone is active in behavioral models predictive of antidepressant potential. It is active in reversing learned helplessness, prevents reserpine-induced ptosis, and enhances response efficiency in the differential reinforcement for low rates of response paradigm. In in vitro studies, nefazodone inhibits the binding of [3H]ketanserin to cortical serotonin2 (5-HT2) binding sites, whereas in vivo, it antagonizes the 5-HT2-mediated quipazine-induced head shake in rats. In ex vivo studies, acute oral administration of nefazodone inhibits cortical serotonin uptake and occupies frontal cortical 5-HT2 receptor binding sites. Chronic administration of nefazodone produces a reduction in 5-HT2-mediated behavior and decreases cortical 5-HT2 receptor binding site density. Further, a chronic high-dose nefazodone regimen significantly potentiates 5-HT1A-mediated behavioral responses in rats. Nefazodone exhibits decreased anticholinergic, alpha-adrenolytic, and sedative activity relative to other antidepressants.

Animals↗