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PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n = 83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗
PubMed1997

Evolutionary induction of sparse neural trees

This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest.

Journal Article↗
PubMed2006

Towards cortex sized artificial neural systems.

We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the cortical circuitry. A characterizing feature of the model is that it is based on the modularization of neocortex into hypercolumns and minicolumns. Both a floating- and fixed-point arithmetic implementation of the model are presented along with simulation results. We conclude that an implementation on a cluster computer is not communication but computation bounded. A mouse and rat cortex sized version of our model executes in 44% and 23% of real-time respectively. Further, an instance of the model with 1.6 x 10(6) units and 2 x 10(11) connections performed noise reduction and pattern completion. These implementations represent the current frontier of large-scale abstract neural network simulations in terms of network size and running speed.

Algorithms↗
PubMedDate not supplied

Modelling studies on the computational function of fast temporal structure in cortical circuit activity.

The interplay between modelling and experimental studies can support the exploration of the function of neuronal circuits in the cortex. We exemplify such an approach with a study on the role of spike timing and gamma-oscillations in associative memory in strongly connected circuits of cortical neurones. It is demonstrated how associative memory studies on different levels of abstraction can specify the functionality to be expected in real cortical neuronal circuits. In our model overlapping random configurations of sparse cell populations correspond to memory items that are stored by simple Hebbian coincidence learning. This associative memory task will be implemented with biophysically well tested compartmental neurones developed by Pinsky and Rinzel . We ran simulation experiments to study memory recall in two network architectures: one interconnected pool of cells, and two reciprocally connected pools. When recalling a memory by stimulating a spatially overlapping set of cells, the completed pattern is coded by an event of synchronized single spikes occurring after 25-60 ms. These fast associations are performed even at a memory load corresponding to the memory capacity of optimally tuned formal associative networks (>0.1 bit/synapse). With tonic stimulation or feedback loops in the network the neurones fire periodically in the gamma-frequency range (20-80 Hz). With fast changing inputs memory recall can be switched between items within a single gamma cycle. Thus, oscillation is not a primary coding feature necessary for associative memory. However, it accompanies reverberatory feedback providing an improved iterative memory recall completed after a few gamma cycles (60-260 ms). In the bidirectional architecture reverberations do not express in a rigid phase locking between the pools. For small stimulation sets bursting occurred in these cells acting as a supportive mechanism for associative memory.

Animals↗
PubMed2003

Purkinje cell synapses target physiologically unique brainstem neurons.

The cerebellum controls motor learning via Purkinje cell synapses onto discrete populations of neurons in the deep cerebellar nuclei and brainstem vestibular nuclei. In the circuitry that subserves the vestibulo-ocular reflex, the postsynaptic targets of Purkinje cells, termed flocculus target neurons (FTNs), are thought to be a critical site of learning. Little is known, however, about the intrinsic cellular properties of FTNs, which are sparsely distributed in the medial vestibular nucleus. To identify these neurons, we used the L7 promoter to express a tau-green fluorescent protein fusion protein selectively in Purkinje cells. Fluorescent Purkinje cell axons and terminal boutons surrounded the somata and proximal dendrites of a small subset of neurons, presumed FTNs, in the medial vestibular nucleus. Targeted intracellular recordings revealed that FTNs fired spontaneously at high rates in brain slices (mean, 47 spikes/sec) and exhibited dramatic postinhibitory rebound firing after the offset of membrane hyperpolarization. These intrinsic firing properties were exceptional among brainstem vestibular nucleus neurons but strikingly similar to neurons in the deep cerebellar nuclei, indicating a common role for intrinsic firing mechanisms in cerebellar control of diverse behaviors.

Action Potentials↗
PubMed1984

Use of sealants: societal and professional factors.

At present, sealant use continues at a low level in private practice by dentists and hygienists, and is moderate in community programs although there is high interest in their use in public health settings. Widespread delegation of sealant application to hygienists has not occurred even though the majority of state dental practice acts allow it, and even though practicing hygienists are highly interested in applying sealants. Use in federally sponsored programs is minimal or nonexistent. Information about sealants and adequate clinical experience in their application appear minimal in dental training institutions, and few opportunities have existed for continuing education. Generally, knowledge about sealants among dentists is low; many hold doubtful opinions. Insurance carriers, including commercial, provider, and governmental programs, appear reluctant to reimburse for sealants without purchaser demand and organized dentistry's blessings. Although society emphasizes patient rights to informed consent, few opportunities exist for the public, including group insurance purchasers and union negotiators, to learn about sealant use in conjunction with appropriate fluoride use. Public educational materials are sparse. Manufacturer marketing efforts at present, aimed at providers alone, are at best minimal, with a few exceptions; those targeting the public are nonexistent. Organized dentistry's Council statements may have contributed to a constriction, to date, in the flow of public information about sealants from industry. Lack of communication, or miscommunication, between practitioners and dental scientists has resulted in much misinformation and confusion about the value of sealants and their use. And, the contemporary status of dental manpower and the economy of dental practice in the private sector appear to have provided an infertile environment for acceptance of sealants as a primary preventive technology. In the public sector, interest in sealants is high but two primary constraints are inadequate manpower and lack of financial resources. Clearly there has been a time-lag in the adoption of new sealant products, and it is apparent that no one factor can explain this lag; rather, many complex factors must be taken into account simultaneously. Extent of need does not appear to have influenced use, to date. Often, social change is slow, and all things considered, the lag observed for sealants may not be totally unreasonable.(ABSTRACT TRUNCATED AT 250 WORDS)

American Dental Association↗
PubMed1997

GABAergic modulation of hippocampal population activity: sequence learning, place field development, and the phase precession effect.

A detailed biophysical model of hippocampal region CA3 was constructed to study how GABAergic modulation influences place field development and the learning and recall of sequence information. Simulations included 1,000 multicompartmental pyramidal cells, each consisting of seven intrinsic and four synaptic currents, and 200 multicompartmental interneurons, consisting of two intrinsic and four synaptic currents. Excitatory rhythmic septal input to the apical dendrites of pyramidal cells and both excitatory and inhibitory input to interneurons at theta frequencies provided a cellular basis for the development of theta and gamma frequency oscillations in population activity. The fundamental frequency of theta oscillations was dictated by the driving rhythm from the septum. Gamma oscillation frequency, however, was determined by both the decay time of the gamma-aminobutyric acid-A (GABA(A))-receptor-mediated synaptic current and the overall level of excitability in interneurons due to alpha-amino-3-hydroxy-5-methyl-4-isoxazole proprionic acid and N-methyl-D-aspartate (NMDA)-receptor-gated channel activation. During theta population activity, total GABA(B)-receptor-mediated conductance levels were found to gradually rise and fall in rhythmic fashion with the predominant population frequency (theta rhythm). This resulted in periodic GABA(B)-receptor-mediated suppression of excitatory synaptic transmission at recurrent collaterals (intrinsic fibers) of pyramidal cells and suppression of inhibitory synaptic transmission to both pyramidal cells and interneurons. To test the ability of the model to learn and recall temporal sequence information, a completion task was employed. During learning, the network was presented a sequence of nonorthogonal spatial patterns. Each input pattern represented a spatial "location" of a simulated rat running a specific navigational path. Hebbian-type learning was expressed as an increase in postsynaptic NMDA-receptor-mediated conductances. Because of several factors including the sparse, asymmetric excitatory synaptic connections among pyramidal cells in the model and a sufficient degree of random "background" firing unrelated to the input patterns, repeated simulated runs resulted in the gradual emergence of place fields where a given cell began to respond to a contiguous segment of locations on the path. During recall, the simulated rat was placed at a random location on the previously learned path and tested to see whether the sequence of locations could be completed on the basis of this initial position. Periodic GABA(B)-receptor-mediated suppression of excitatory and inhibitory transmission at intrinsic but not afferent fibers resulted in sensory information about location being dominant during early portions of each theta cycle when GABA(B)-receptor-related effects were highest. This suppression declined with levels of GABA(B) receptor activation toward the end of a theta cycle, resulting in an increase in synaptic transmission at intrinsic fibers and the subsequent recall of a segment of the entire location sequence. This scenario typically continued across theta cycles until the full sequence was recalled. When the GABA(B)-receptor-mediated suppression of excitatory and inhibitory transmission at intrinsic fibers was not included in the model, place field development was curtailed and the network consequently exhibited poor learning and recall performance. This was, in part, due to increased competition of information from intrinsic and afferent fibers during early portions of each theta cycle. Because afferent sensory information did not dominate early in each cycle, the current location of the rat was obscured by ongoing activity from intrinsic sources. (ABSTRACT TRUNCATED)

Animals↗
PubMed2025

A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.

Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches-primarily reliant on tetranucleotide frequency (TNF) and abundance profiles-often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets-including wastewater and soil microbiomes-demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction.

canonical correlation analysis↗
PubMed1999

Thermodynamics energy for both supervised and unsupervised learning neural nets at a constant temperature.

Unified Lyaponov function is given for the first time to prove the learning methodologies convergence of artificial neural network (ANN), both supervised and unsupervised, from the viewpoint of the minimization of the Helmholtz free energy at the constant temperature. Early in 1982, Hopfield has proven the supervised learning by the energy minimization principle. Recently in 1996, Bell & Sejnowski has algorithmically demonstrated Independent Component Analyses (ICA) generalizing the Principal Component Analyses (PCA) that the continuing reduction of early vision redundancy happens towards the "sparse edge maps" by maximization of the ANN output entropy. We explore the combination of both as Lyaponov function of which the proven convergence gives both learning methodologies. The unification is possible because of the thermodynamics Helmholtz free energy at a constant temperature. The blind de-mixing condition for more than two objects using two sensor measurement. We design two smart cameras with short term working memory to do better image de-mixing of more than two objects. We consider channel communication application that we can efficiently mix four images using matrices [AO] and [Al] to send through two channels.

Algorithms↗
PubMed2002

Human gene discovery through experimental definition of transcribed regions of the human genome.

The sequencing of the human genome has failed to realize its primary goal: the identification of all human genes. We have learned that genes can only be identified with certainty within this vast and information-sparse structure by comparison with transcript sequences. Significantly more sequence data of this kind is required before we can claim to have deciphered our genetic blueprint.

Algorithms↗
PubMed2005

Relevance vector machine for automatic detection of clustered microcalcifications.

Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms.

Algorithms↗
PubMed2006

Neurodevelopmental outcome of bronchopulmonary dysplasia.

Children born very preterm are vulnerable for long-term cognitive, educational, and behavioral impairments; bronchopulmonary dysplasia (BPD) is an additional risk factor which exacerbates these problems. As a population, children with BPD exhibit low average IQ, academic difficulties, delayed speech and language development, visual-motor integration impairments, and behavior problems. Neuropsychological studies are sparse, but there is some evidence that children with BPD also display attention problems, memory and learning deficits, and executive dysfunction. BPD does not appear to be associated with a specific neuropsychological impairment but rather a global impairment.

Bronchopulmonary Dysplasia↗
PubMed2004

Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning.

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

Cognition↗
PubMed2002

Effects of conditioned food aversions on nutritional behavior in humans.

Conditioned food aversion (CFA) and taste aversion (CTA) are widely occurring phenomena mediating rejection of solids or liquids, the ingestion of which has induced the onset of post-ingestional malaise. It is a powerful and durable imprint learning that may influence food choice and intake in all animals, including humans. For ethical reasons, CTA has been extensively investigated in a wide variety of laboratory animal's species but only incidentally in humans. Nevertheless, convincing evidence has been provided that CFA and CTA learning are possible in a wide range of human subjects. The results in humans may have some limitations in accuracy since data are sparse, sometimes indirect, and poorly controlled. There is only limited information on the extent of CFA in the elderly since most studies have employed questionnaire and/or interview methods on young people (i.e. college students). The present review evaluates the literature derived both from laboratory animals and humans. In the first instance, the salient features of food and taste aversion learning and the neural mechanisms involved in this learning behavior will be examined. Then, the problems encountered when trying to assess the role of learned food and taste aversions in the nutritional status of healthy as well as sick young or elderly people will be considered. In particular, the importance of CFA on the nutritional status of cancer patients and treatment of alcoholism will be examined. It is concluded that the data are compelling enough to warrant further research and, some indications and recommendations are suggested.

Alcoholism↗
PubMed2001

Effects of neocortical ectopias and environmental enrichment on Hebb-Williams maze learning in BXSB mice.

Approximately 40-60% of BXSB mice have neocortical ectopias, a developmental anomaly characterized by migration of neurons into the neuron-sparse layer I of cortex. Previous studies have shown that ectopic BXSB mice have superior reference, but inferior working, memory on spatial tasks. Female BXSB mice were housed either in an enriched environment or in standard cages at weaning. Subsequently, these animals were tested on four of the Hebb-Williams mazes in a water-based version of this maze. Theoretically, two of the maze configurations placed greater emphasis on reference memory to find the goal, whereas the other two favored working memory. Ectopics reared in standard housing conditions were better than nonectopics on mazes that favored the use of reference memory, but poorer on mazes that favored working memory. In contrast, subjects raised in the enriched environment showed no ectopia differences. A comparison of enriched and standard housing conditions found that the enriched animals had better reference memory but poorer working memory. The latter effect may be because the enriched environment, although more stimulating, did not change in time or space; and other researchers have shown that daily replacement of stimuli in complex environments is correlated with better working memory.

Animals↗
PubMed2001

Concentration-controlled or effect-controlled trials: useful alternatives to conventional dose-controlled trials?

Historically, dose-finding trials have been confirmatory in nature despite the fact that these trials represent an important and essential 'learning' phase in the drug development process. About 10 years ago 2 alternatives to the randomised dose-controlled trial (RDCT) were proposed as being more informative trial types. Controlling systemic drug exposure in order to improve efficiency of a trial forms the basis for the suggestion of a randomised concentration-controlled trial (RCCT). For the common instance where pharmacodynamic variability is larger than pharmacokinetic variability, the randomised effect-controlled trial (RECT), where patients are randomised to the effect of interest was suggested as even more informative. A survey of the literature shows that the RCCT has been sparsely applied and RECT not at all. For RCCT, the practical complications of carrying out the study seldom makes it the study type of choice. For RECT, the limited number of suitable situations for its application and the fact that the same effect is used for randomisation and analysis may explain the lack of applications. As a somewhat more favourable trial type, we suggest the randomised biomarker-controlled trial (RBCT), where patients are randomised to a certain value or range of a biomarker whereas the analysis is performed on another, clinically more relevant, effect. Although the RBCT has some attractive features, for example contributing to validation of a biomarker as a surrogate for clinical outcome, it is unlikely to be extensively used. Instead, the main shift from confirming to learning in dose-finding trials is coming from the incorporation of well-known learning components into the RDCT (e.g. sparse concentration measurements combined with population pharmacokinetic-pharmacodynamic, biomarker measurements and analysis of effect measures throughout the entire trial period).

Dose-Response Relationship, Drug↗
PubMed2026

A leakage-aware genomic prediction pipeline for meropenem resistance in Klebsiella pneumoniae using transformer-based resistome representation learning.

MOTIVATION: Antimicrobial resistance (AMR) in Klebsiella pneumoniae, particularly to carbapenems such as meropenem, is a major global health problem. Machine learning is increasingly used to predict resistance from genomic markers; however, many models fail to capture high-level gene-gene interactions and may exhibit inflated performance due to lineage-biased prediction. Existing genomic prediction models largely rely on flat feature representations that fail to capture epistatic gene interactions, and commonly suffer from inflated performance estimates due to phylogenetic data leakage. To address these limitations simultaneously, a leakage-aware hybrid TabTransformer-CatBoost pipeline was developed, combining self-attention-based resistome representation learning with gradient boosting classification under clade-aware data partitioning. A self-attention encoder converts sparse gene presence-absence profiles into contextualized latent embeddings, which are subsequently classified using gradient boosting to capture lineage-aware AMR patterns. RESULTS: The proposed architecture outperformed classical baselines including Logistic Regression, Random Forest, XGBoost, and optimized CatBoost models. Internal accuracy reached 92.59% for the Chained Hybrid configuration (area under the receiver operating characteristic curve, AUROC = 0.8670, F1 = 0.8537). Performance gains primarily originated from the embedding stage, as confirmed by ablation analysis. External validation across independent multinational cohorts (n = 305) demonstrated generalizability (AUROC = 0.8105; F1 = 0.7552). Permutation testing produced near-zero Matthews Correlation Coefficient (MCC) = 0.0091, indicating predictions reflect genuine biological signal rather than noise. These results establish attention-based genomic embedding with gradient boosting as a scalable, interpretable, and leakage-aware framework for clinical AMR prediction. AVAILABILITY AND IMPLEMENTATION: The source code for the TabTransformer-CatBoost framework, including preprocessing pipelines and pre-trained embeddings, is available at https://github.com/SibelKervanci/kp-meropenem-tabtransformer.

Journal Article↗
PubMed2006

Cognitive functioning in patients with familial bipolar I disorder and their unaffected relatives.

BACKGROUND: Impairments in verbal learning and memory, executive functions and attention are manifest in some euthymic patients with bipolar disorder (BPD). However, evidence is sparse on their putative role as aetiologically important genetic vulnerability markers for the disorder. This population-based study examined the cognitive functions of affected and unaffected individuals in families with BPD. The aim was to discover whether any cognitive function would indicate genetic liability to the disorder and could thus be regarded as endophenotypes of BPD. METHOD: A diagnostic interview and a neuropsychological test battery were administered to 32 familial bipolar I disorder patients, 40 of their unaffected first-degree relatives and 55 controls, all representing population-based samples. RESULTS: Unaffected first-degree relatives showed impairment in psychomotor performance speed and slight impairment in executive function. Bipolar patients were impaired in verbal learning and memory compared with unaffected relatives and controls. They also differed from controls in tasks of executive functions. There were no difference between the groups in simple attention and working memory tasks. CONCLUSIONS: Impaired psychomotor performance speed and executive function may represent endophenotypes of BPD, reflecting possible underlying vulnerability to the disorder. Verbal memory impairments appear to be more related to the fully developed disorder.

Bipolar Disorder↗