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At least 361 records · Page 20Linked to original sources

PubMed2007

Two-sample comparison based on prediction error, with applications to candidate gene association studies.

To take advantage of the increasingly available high-density SNP maps across the genome, various tests that compare multilocus genotypes or estimated haplotypes between cases and controls have been developed for candidate gene association studies. Here we view this two-sample testing problem from the perspective of supervised machine learning and propose a new association test. The approach adopts the flexible and easy-to-understand classification tree model as the learning machine, and uses the estimated prediction error of the resulting prediction rule as the test statistic. This procedure not only provides an association test but also generates a prediction rule that can be useful in understanding the mechanisms underlying complex disease. Under the set-up of a haplotype-based transmission/disequilibrium test (TDT) type of analysis, we find through simulation studies that the proposed procedure has the correct type I error rates and is robust to population stratification. The power of the proposed procedure is sensitive to the chosen prediction error estimator. Among commonly used prediction error estimators, the .632+ estimator results in a test that has the best overall performance. We also find that the test using the .632+ estimator is more powerful than the standard single-point TDT analysis, the Pearson's goodness-of-fit test based on estimated haplotype frequencies, and two haplotype-based global tests implemented in the genetic analysis package FBAT. To illustrate the application of the proposed method in population-based association studies, we use the procedure to study the association between non-Hodgkin lymphoma and the IL10 gene.

Adult↗

PubMed2024

Human mutations in high-confidence Tourette disorder genes affect sensorimotor behavior, reward learning, and striatal dopamine in mice.

Tourette disorder (TD) is poorly understood, despite affecting 1/160 children. A lack of animal models possessing construct, face, and predictive validity hinders progress in the field. We used CRISPR/Cas9 genome editing to generate mice with mutations orthologous to human de novo variants in two high-confidence Tourette genes, CELSR3 and WWC1. Mice with human mutations in Celsr3 and Wwc1 exhibit cognitive and/or sensorimotor behavioral phenotypes consistent with TD. Sensorimotor gating deficits, as measured by acoustic prepulse inhibition, occur in both male and female Celsr3 TD models. Wwc1 mice show reduced prepulse inhibition only in females. Repetitive motor behaviors, common to Celsr3 mice and more pronounced in females, include vertical rearing and grooming. Sensorimotor gating deficits and rearing are attenuated by aripiprazole, a partial agonist at dopamine type II receptors. Unsupervised machine learning reveals numerous changes to spontaneous motor behavior and less predictable patterns of movement. Continuous fixed-ratio reinforcement shows that Celsr3 TD mice have enhanced motor responding and reward learning. Electrically evoked striatal dopamine release, tested in one model, is greater. Brain development is otherwise grossly normal without signs of striatal interneuron loss. Altogether, mice expressing human mutations in high-confidence TD genes exhibit face and predictive validity. Reduced prepulse inhibition and repetitive motor behaviors are core behavioral phenotypes and are responsive to aripiprazole. Enhanced reward learning and motor responding occur alongside greater evoked dopamine release. Phenotypes can also vary by sex and show stronger affection in females, an unexpected finding considering males are more frequently affected in TD.

Animals↗

PubMed2025

Understanding the sources of performance in deep drug response models reveals insights and improvements.

MOTIVATION: Anti-cancer drug response prediction (DRP) using cancer cell lines (CLs) is crucial in stratified medicine and drug discovery. Recently, new deep learning models for DRP have improved performance over their predecessors. However, different models use different input data types and architectures making it hard to find the source of these improvements. Here we consider published DRP models that report state-of-the-art performance predicting continuous response values. These models take chemical structures of drugs and omics profiles of CLs as input. RESULTS: By experimenting with these models and comparing with our simple baselines, we show that no performance comes from drug features, instead, performance is due to the transcriptomics CL profiles. Furthermore, we show that, depending on the testing type, much of the current reported performance is a property of the training target values. We address these limitations by creating BinaryET and BinaryCB that predict binary drug response values, guided by the hypothesis that this reduces the noise in the drug efficacy data. Thus, better aligning them with biochemistry that can be learnt from the input data. BinaryCB leverages a chemical foundation model, while BinaryET is trained from scratch using a transformer-type architecture. We show that these models learn useful chemical drug features, which is the first time this has been demonstrated for multiple testing types to our knowledge. We further show binarizing the drug response values causes the models to learn useful chemical drug features. We also show that BinaryET improves performance over BinaryCB, and the published models that report state-of-the-art performance. AVAILABILITY AND IMPLEMENTATION: Code is available from https://github.com/Nik-BB/Understanding_DRP_models.

Humans↗

PubMed1997

Using a financial training criterion rather than a prediction criterion.

The application of this work is to decision making with financial time series, using learning algorithms. The traditional approach is to train a model using a prediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing the likelihood of a conditional model of the dependent variable. We find here with noisy time series that better results can be obtained when the model is directly trained in order to maximize the financial criterion of interest, here gains and losses (including those due to transactions) incurred during trading. Experiments were performed on portfolio selection with 35 Canadian stocks.

Algorithms↗

PubMed2005

Critical validation studies of neurofeedback.

The field of neurofeedback training has proceeded largely without validation. In this article the authors review studies directed at validating sensory motor rhythm, beta and alpha-theta protocols for improving attention, memory, and music performance in healthy participants. Importantly, benefits were demonstrable with cognitive and neurophysiologic measures that were predicted on the basis of regression models of learning to enhance sensory motor rhythm and beta activity. The first evidence of operant control over the alpha-theta ratio is provided, together with remarkable improvements in artistic aspects of music performance equivalent to two class grades in conservatory students. These are initial steps in providing a much needed scientific basis to neurofeedback.

Adolescent↗

PubMed2006

Validating the efficacy of neurofeedback for optimising performance.

The field of neurofeedback training has largely proceeded without validation. Here we review our studies directed at validating SMR, beta and alpha-theta protocols for improving attention, memory, mood and music and dance performance in healthy participants. Important benefits were demonstrable with cognitive and neurophysiological measures which were predicted on the basis of regression models of learning. These are initial steps in providing a much needed scientific basis to neurofeedback, but much remains to be done.

Alpha Rhythm↗

PubMedDate not supplied

SMIREP: predicting chemical activity from SMILES.

Most approaches to structure-activity-relationship (SAR) prediction proceed in two steps. In the first step, a typically large set of fingerprints, or fragments of interest, is constructed (either by hand or by some recent data mining techniques). In the second step, machine learning techniques are applied to obtain a predictive model. The result is often not only a highly accurate but also hard to interpret model. In this paper, we demonstrate the capabilities of a novel SAR algorithm, SMIREP, which tightly integrates the fragment and model generation steps and which yields simple models in the form of a small set of IF-THEN rules. These rules contain SMILES fragments, which are easy to understand to the computational chemist. SMIREP combines ideas from the well-known IREP rule learner with a novel fragmentation algorithm for SMILES strings. SMIREP has been evaluated on three problems: the prediction of binding activities for the estrogen receptor (Environmental Protection Agency's (EPA's) Distributed Structure-Searchable Toxicity (DSSTox) National Center for Toxicological Research estrogen receptor (NCTRER) Database), the prediction of mutagenicity using the carcinogenic potency database (CPDB), and the prediction of biodegradability on a subset of the Environmental Fate Database (EFDB). In these applications, SMIREP has the advantage of producing easily interpretable rules while having predictive accuracies that are comparable to those of alternative state-of-the-art techniques.

Algorithms↗

PubMed1997

Direct and indirect stimulus-frequency effects in recognition.

Subsequent recognition of stimuli perceived in a given situation was studied in relation to stimulus familiarity as determined by frequency in observers' prior experience. To distinguish direct and indirect frequency effects, on the basis of selective memory retrieval and selective attention during learning, respectively, rigorous controls were imposed on stimulus rehearsability and learning conditions. As predicted by a global memory model, both hits and false alarms on recognition tests increased as a function of prior frequency in a concordant pattern that indicates a direct effect, in contrast with the usual indirect effect of varying normative word frequency. Understanding the role of experiential stimulus frequency in recognition may further the interpretation of research in such paradigms as eyewitness testimony.

Adult↗

PubMed2006

Interdose interval effects on the development of contextual tolerance to nicotine's analgesic effects in rats (Rattus norvegicus).

Learning models of associative and nonassociative drug tolerance predict that the development of contextual tolerance to drug effects is disrupted when the drug is delivered at short interdose intervals (IDIs). The authors examined the impact of 1 long IDI and 2 short IDIs in the development of contextual nicotine tolerance. Associative tolerance was investigated by giving rats (Rattus norvegicus) 10 subcutaneous injections of nicotine at either long (72-hr) IDIs or short (6-hr and 4.5-hr) IDIs. The delivery of nicotine was either explicitly paired or explicitly unpaired with a distinctive context. A 3rd group of rats was exposed to the experimental procedures but received only saline. Associative tolerance to nicotine's analgesic effects was defined as a shift to the right of the dose-response curve (DRC) of rats in the explicitly paired condition with respect to the DRC of rats in the explicitly unpaired condition. Analgesia was assessed with the tail-flick and hot-plate devices. In the tail-flick assessment, associative tolerance was evident in the 72-hr and the 6-hr IDI conditions only. In the hot-plate assessment, associative tolerance was present in the 72-hr IDI condition only. The findings suggest that contextual tolerance to nicotine's analgesic effects are positively related to IDI length and are more readily demonstrated with the tail-flick method than with the hot-plate method. Overall, the results supported the thesis that nicotine tolerances that develop to different IDIs are qualitatively different and may be mediated by different psychological and physiological mechanisms.

Analgesics↗

PubMed2004

Random presentation enables subjects to adapt to two opposing forces on the hand.

Studies have shown that humans cannot simultaneously learn opposing force fields or opposing visuomotor rotations, even when provided with arbitrary contextual information, probably because of interference in their working memory. In contrast, we found that subjects can adapt to two opposing force fields when provided with contextual cues and can consolidate motor memories if random and frequent switching occurs. Because significant aftereffects were seen, this study suggests that multiple internal models can be acquired simultaneously during learning and predictively switched, depending only on contextual information.

Cues↗

PubMed2003

Evaluating the C-section rate of different physician practices: using machine learning to model standard practice.

The C-section rate of a population of 22,175 expectant mothers is 16.8%; yet the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 13% to 23%. Our goal is to determine retrospectively if the variations in the observed rates can be attributed to variations in the intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or differences in physician practice (i.e. some groups do more C-sections). We apply machine learning to this problem by training models to predict standard practice from retrospective data. We then use the models of standard practice to evaluate the C-section rate of each physician practice. Our results indicate that although there is variation in intrinsic risk among the groups, there also is much variation in physician practice.

Artificial Intelligence↗

PubMedDate not supplied

Prediction of P-glycoprotein substrates by a support vector machine approach.

P-glycoproteins (P-gp) actively transport a wide variety of chemicals out of cells and function as drug efflux pumps that mediate multidrug resistance and limit the efficacy of many drugs. Methods for facilitating early elimination of potential P-gp substrates are useful for facilitating new drug discovery. A computational ensemble pharmacophore model has recently been used for the prediction of P-gp substrates with a promising accuracy of 63%. It is desirable to extend the prediction range beyond compounds covered by the known pharmacophore models. For such a purpose, a machine learning method, support vector machine (SVM), was explored for the prediction of P-gp substrates. A set of 201 chemical compounds, including 116 substrates and 85 nonsubstrates of P-gp, was used to train and test a SVM classification system. This SVM system gave a prediction accuracy of at least 81.2% for P-gp substrates based on two different evaluation methods, which is substantially improved against that obtained from the multiple-pharmacophore model. The prediction accuracy for nonsubstrates of P-gp is 79.2% using 5-fold cross-validation. These accuracies are slightly better than those obtained from other statistical classification methods, including k-nearest neighbor (k-NN), probabilistic neural networks (PNN), and C4.5 decision tree, that use the same sets of data and molecular descriptors. Our study indicates the potential of SVM in facilitating the prediction of P-gp substrates.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2003

Cerebellar learning of bio-mechanical functions of extra-ocular muscles: modeling by artificial neural networks.

A control circuit is proposed to model the command of saccadic eye movements. Its wiring is deduced from a mathematical constraint, i.e. the necessity, for motor orders processing, to compute an approximate inverse function of the bio-mechanical function of the moving plant, here the bio-mechanics of the eye. This wiring is comparable to the anatomy of the cerebellar pathways. A predicting element, necessary for inversion and thus for movement accuracy, is modeled by an artificial neural network whose structure, deduced from physical constraints expressing the mechanics of the eye, is similar to the cell connectivity of the cerebellar cortex. Its functioning is set by supervised reinforcement learning, according to learning rules aimed at reducing the errors of pointing, and deduced from a differential calculation. After each movement, a teaching signal encoding the pointing error is distributed to various learning sites, as is, in the cerebellum, the signal issued from the inferior olive and conveyed to various cell types by the climbing fibers. Results of simulations lead to predict the existence of a learning site in the glomeruli. After learning, the model is able to accurately simulate saccadic eye movements. It accounts for the function of the cerebellar pathways and for the final integrator of the oculomotor system. The novelty of this model of movement control is that its structure is entirely deduced from mathematical and physical constraints, and is consistent with general anatomy, cell connectivity and functioning of the cerebellar pathways. Even the learning rules can be deduced from calculation, and they reproduce long term depression, the learning process which takes place in the dendritic arborization of the Purkinje cells. This approach, based on the laws of mathematics and physics, appears thus as an efficient way of understanding signal processing in the motor system.

Biomechanical Phenomena↗

PubMed2025

Modeling Alternative Conformational States in CASP16.

The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain-linker-domain (D-L-D) targets assessed against statistical models from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein-DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score >0.75). However, with the exception of one protein-ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality based selection. While the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein-DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score <0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains a critical frontier for structural biology.

AlphaFold2↗

PubMed2026

Metagenomic polymorphic toxin effector and immunity profiling predicts microbiome development and disease-related dysbiosis.

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease states, outperforming models utilizing taxonomy alone. During microbiome development in the first year of life, PolyProf alpha diversity increases, and beta diversity becomes increasingly like the maternal microbiome, influenced by vertical transfer, delivery mode, and breastfeeding. PolyProf is related to strain sharing among adults through social interactions. In summary, TSS genes strongly correlate with microbiome development and interpersonal strain sharing, suggesting roles for interbacterial antagonism. Since PolyProf distinguishes diverse adult disease statuses, these dynamics may contribute to non-genetic inheritance.IMPORTANCEPrevious research has demonstrated that bacteria compete within the gut microbiome using toxin secretion systems (TSS). How TSS contribute to human microbiome development and the microbiome alterations observed in human diseases is not known. This study develops a new bioinformatic tool for profiling TSS-related genes in metagenomic data. Application of this approach to large-scale human fecal metagenomic data demonstrates the dynamic association of TSS during microbiome development, including the exchange of strains among social contacts. TSS gene abundance patterns are highly predictive of 12 disease states. This study advances the field by enabling TSS profiling in metagenomes and by identifying disease and microbiome development biomarkers that provide hypotheses for future mechanistic studies and may be useful for disease diagnosis.

Dysbiosis↗

PubMed2006

A cerebellar model for predictive motor control tested in a brain-based device.

The cerebellum is known to be critical for accurate adaptive control and motor learning. We propose here a mechanism by which the cerebellum may replace reflex control with predictive control. This mechanism is embedded in a learning rule (the delayed eligibility trace rule) in which synapses onto a Purkinje cell or onto a cell in the deep cerebellar nuclei become eligible for plasticity only after a fixed delay from the onset of suprathreshold presynaptic activity. To investigate the proposal that the cerebellum is a general-purpose predictive controller guided by a delayed eligibility trace rule, a computer model based on the anatomy and dynamics of the cerebellum was constructed. It contained components simulating cerebellar cortex and deep cerebellar nuclei, and it received input from a middle temporal visual area and the inferior olive. The model was incorporated in a real-world brain-based device (BBD) built on a Segway robotic platform that learned to traverse curved paths. The BBD learned which visual motion cues predicted impending collisions and used this experience to avoid path boundaries. During learning, the BBD adapted its velocity and turning rate to successfully traverse various curved paths. By examining neuronal activity and synaptic changes during this behavior, we found that the cerebellar circuit selectively responded to motion cues in specific receptive fields of simulated middle temporal visual areas. The system described here prompts several hypotheses about the relationship between perception and motor control and may be useful in the development of general-purpose motor learning systems for machines.

Cerebellum↗

PubMed2003

The trichotomous model and investment in learning to prepare for a sport test: a mediational analysis.

BACKGROUND: Contemporary views on motivation suggest that the approach-avoidance achievement goals conceptualization--namely the trichotomous model--can shed light on the important issue of student motivation. AIMS: To test the predictive value of the trichotomous model on the investment in learning a sport task for test preparation, and to validate a model which included a set of psychological processes (i.e., state anxiety and competence valuation) which mediate the relationship between the three goals (i.e., performance-approach, performance-avoidance, and mastery goals) and test preparation. The study was designed to investigate the direct and mediational effects of three experimental goal conditions on the time in which pupils prepared for a sport test. SAMPLE: French male school pupils (N = 75). Pupils were aged 13-15 years and attended schools in southern France. METHOD: Pupils prepared themselves for a sport task with a 5-minute period of training, and performed in one of three experimental conditions to which they were randomly assigned: a performance goal with a positive outcome focus (performance-approach), a performance goal with a negative outcome focus (performance-avoidance), or a mastery goal. RESULTS: Pupils in the performance-avoidance group reported higher state anxiety and lower competence valuation than those in the performance-approach and mastery groups, and this psychological state was associated with less time taken to prepare for the test. CONCLUSION: School pupils placed in an examination preparation context that elicits a performance goal with a negative outcome focus (performance-avoidance) show motivational deficits which manifest themselves in less time spent practising. The trichotomous model appears to be valid for the study of motivational processes in school physical education.

Adolescent↗

PubMed2026

dsRNAscan maps human dsRNAome, revealing conservation, intermolecular dsRNA, and correlates of ADAR dependency.

The human transcriptome contains millions of A-to-I editing sites arising from an unclear number of poorly characterized dsRNAs. Editing sites reveal the presence of dsRNA, but this method is limited by transcription levels, read depth, and ADAR expression and cannot identify unedited dsRNA. To address these limitations, we developed dsRNAscan. Applying dsRNAscan to the human genome predicted 5 million dsRNAs, mostly in repetitive and intergenic regions. Machine learning models trained on A-to-I editing and RNA structure-probing data identified ∼2.4 million high-confidence predictions, which were enriched at dsRNA-binding protein binding sites. Additionally, we predicted hundreds of dsRNAs conserved across vertebrates and observed thousands of editing-enriched regions suspected to arise from intermolecular dsRNAs formed with sense-antisense transcripts. Quantifying expression of intramolecular and intermolecular dsRNAs accessible to cytoplasmic immune sensors revealed that their ratio correlated with ADAR dependency across cancer cell lines. The human dsRNAome is available as a resource at https://dsrna.chpc.utah.edu/.

A-to-I RNA editing↗