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PubMed2004

What can we learn from noncoding regions of similarity between genomes?

BACKGROUND: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to assume that these conserved regions are more likely to contain functional elements than less-conserved portions of the genome. METHODS: Here we used a motif-oriented machine learning method based on the Relevance Vector Machine algorithm to extract the strongest signal from a set of non-coding conserved sequences. RESULTS: We successfully fitted models to reflect the non-coding sequences, and showed that the results were quite consistent for repeated training runs. Using the learned models to scan genomic sequence, we found that they often made predictions close to the start of annotated genes. We compared this method with other published promoter-prediction systems, and showed that the set of promoters which are detected by this method is substantially similar to that detected by existing methods. CONCLUSIONS: The results presented here indicate that the promoter signal is the strongest single motif-based signal in the non-coding functional fraction of the genome. They also lend support to the belief that there exists a substantial subset of promoter regions which share several common features including, but not restricted to, a relative abundance of CpG dinucleotides. This subset is detectable by a variety of distinct computational methods.

Animals↗
PubMed2002

A connectionist model of septohippocampal dynamics during conditioning: closing the loop.

Septohippocampal interactions determine how stimuli are encoded during conditioning. This study extends a previous neurocomputational model of corticohippocampal processing to incorporate hippocamposeptal feedback and examines how the presence or absence of such feedback affects learning in the model. The effects of septal modulation in conditioning were simulated by dynamically adjusting the hippocampal learning rate on the basis of how well the hippocampal system encoded stimuli. The model successfully accounts for changes in behavior and septohippocampal activity observed in studies of the acquisition, retention, and generalization of conditioned responses and accounts for the effects of septal disruption on conditioning. The model provides a computational, neurally based synthesis of prior learning theories that predicts changes in medial septal activity based on the novelty of stimulus events.

Animals↗
PubMed2026

How negative sampling shapes the performance of transcription factor binding site prediction models.

MOTIVATION: Transcription factors (TFs) are key players in gene regulation and development, where they activate and repress gene expression through DNA binding. Predicting transcription factor binding sites (TFBSs) has long been an active area of research, with many deep learning methods developed to tackle this problem. These models are often trained on TF ChIP-seq data, which is generally seen as only providing positive samples. The choice of datasets and negative sampling techniques is a critical yet often overlooked aspect of this work. RESULTS: In this study, we investigate the impact of different negative sampling techniques on TFBS prediction performance. We create high-quality test datasets based on ChIP-seq and ATAC-seq data, where true negatives can be identified as positions that are accessible but not bound by the TF in question. We then train models using various negative sampling techniques, including genomic sampling, shuffling, dinucleotide shuffling, neighborhood sampling, and cell line specific sampling, simulating cases where matching ATAC-seq data is not available. Our results show that, generally, metrics calculated on training datasets give inflated performance scores. Of the tested techniques, genomic sampling of negatives based on similarity to the positives performed by far the best, although still not reaching the performance of baseline models trained on high-quality datasets. Models trained on dinucleotide shuffled negatives performed poorly, despite being a common practice in the field. Our findings highlight the importance of carefully selecting negative sampling techniques for TFBS prediction, as they can significantly impact model performance and the interpretation of results. AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/NatanTourne/TFBS-negatives (DOI: 10.5281/zenodo.18007567).

Binding Sites↗
PubMed1984

Recognition memory in conversion hysteria: effect of sexual stimuli during learning.

Hysteria has two main explanatory models: a neurobiological and a psychodynamic. Both models can predict a memory deficit, as a consequence either of a neurophysiological inhibition or of a repression produced by a conflict. The existence of a conflict can also be proved by showing the stimulating, and not the inhibitory effect that conflictual material can have when appropriately shown. Ten female hysterical patients and 10 nonpsychiatric female patients were submitted to a short-term memory test consisting of pictures. In one session all pictures were neutral, while in a second session the neutral were mixed with mild sexually charged pictures, excluded from subsequent recall. The overall performances of the two groups were not significantly different, but the hysterical patients remembered better the pictures linked to the sexual ones, while the control group did the opposite. The results are consistent with the psychodynamic model, while they cannot be explained by the neurobiological.

Adult↗
PubMed2025

An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.

MOTIVATION: Accurate prediction of protein-ligand binding (PLB) relationships plays a crucial role in drug discovery, which helps identify drugs that modulate the activity of specific targets. Traditional biological assays for measuring PLB relationships are time consuming and costly. In addition, models for predicting PLB relationships have been developed and widely used in drug discovery tasks. However, learning more accurate PLB representations is essential to meet the stringent standards required for drug discovery. RESULTS: We propose an image-based PLB representation learning framework, called ImagePLB, which equips ligand representation learner (LRL) and protein representation learner (PRL) to accept 3D multi-view ligand images and protein graphs as input, respectively, and learns rich interaction information between ligand and protein through a binding representation learner (BRL). Considering the scarcity of protein-ligand pairs, we further propose a multi-level next trajectory prediction (MLNTP) task to pre-train ImagePLB on the 4D flexible dynamics trajectory of 16 972 complexes, including ligand level, protein level, and complex level, to learn information related to trajectories. Besides, by introducing trajectory regularization (TR), we effectively alleviate the problem of high (even almost identical) feature similarity caused by adjacent trajectories. Compared with the current state-of-the-art methods, ImagePLB has achieved competitive improvements on PLB-related prediction tasks, including protein-ligand affinity and efficacy prediction tasks. This study opens the door to the image-based PLB learning paradigm. AVAILABILITY AND IMPLEMENTATION: All data and implementation details of code can be obtained from https://github.com/HongxinXiang/ImagePLB.

Ligands↗
PubMed1993

How to make the right choice: a new model for the selection interview.

The problems encountered when selecting new members of staff in the higher education setting are those universal in any type of organization. Nursing faculties, however, have an even greater difficulty in selecting appropriate staff owing to the diversity of both clinical and academic backgrounds of applicants. The organization is therefore stimulated and challenged in its ability to select the right employee for the job. The literature tends to support the use of group decision making in selection and the function of the group in the interview setting is to evaluate the applicant's prior learning against desirable characteristics. From an analysis of the literature, criteria relating to desirable employee characteristics for nurse academics have been developed. The author has identified a new model to use in assisting the selection panel in predicting the right person for the job.

Decision Making↗
PubMedDate not supplied

Mitral cell beta1 and 5-HT2A receptor colocalization and cAMP coregulation: a new model of norepinephrine-induced learning in the olfactory bulb.

In the present study we assess a new model for classical conditioning of odor preference learning in rat pups. In preference learning beta(1)-adrenoceptors activated by the locus coeruleus mediate the unconditioned stimulus, whereas olfactory nerve input mediates the conditioned stimulus, odor. Serotonin (5-HT) depletion prevents odor learning, with 5-HT(2A/2C) agonists correcting the deficit. Our new model proposes that the interaction of noradrenergic and serotonergic input with odor occurs in the mitral cells of the olfactory bulb through activation of cyclic adenosine monophosphate (cAMP). Here, using selective antibodies and immunofluorescence examined with confocal microscopy, we demonstrate that beta(1)-adrenoceptors and 5-HT(2A) receptors colocalize primarily on mitral cells. Using a cAMP assay and cAMP immunocytochemistry, we find that beta-adrenoceptor activation by isoproterenol, at learning-effective and higher doses, significantly increases bulbar cAMP, as does stroking. As predicted by our model, the cAMP increases are localized to mitral cells. 5-HT depletion of the olfactory bulb does not affect basal levels of cAMP but prevents isoproterenol-induced cAMP elevation. These results support the model. We suggest the mitral-cell cAMP cascade converges with a Ca(2+) pathway activated by odor to recruit CREB phosphorylation and memory-associated changes in the olfactory bulb. The dose-related increase in cAMP with isoproterenol implies a critical cAMP window because the highest dose of isoproterenol does not produce learning.

Adrenergic beta-Agonists↗
PubMed1998

The role of learning in remembered duration.

In two experiments, the effects of learning on both the accuracy and bias of duration judgments were examined. In Experiment 1, subjects learned one of two tasks (i.e., using a computer software package, building a model car), containing a varying number of action steps, over a one-, three-, or five-trial period. Retrospective judgments of a task's total duration revealed that accuracy was high at intermediate stages of learning but was low at early stages due to an overestimation bias and low at later stages due to an underestimation bias. The number of action steps within a task influenced behavior only at early learning stages where more action steps led to significantly longer duration estimates. Experiment 2 acted as a converging operation in which novice and experienced pianists were asked to estimate, in advance, how long they thought it would take them to play melodies that varied in their degree of familiarity (i.e. recently learned, well learned, extremely well learned). When these estimates were compared with the melodies' actual playing times, results revealed a similar pattern of accuracy and bias as found in Experiment 1. These findings are discussed in terms of a "structural remembering model" that emphasizes the role of event predictability in time estimation behavior.

Analysis of Variance↗
PubMed2004

Mental models and meaningful learning.

If you understand something, you can use the information you have acquired to solve problems to which that knowledge is relevant. Meaningful learning is learning with understanding. Achieving meaningful learning begins with the building of correct, appropriate mental models, or representations, of the knowledge being acquired. The next step is learning to use the available mental models to solve problems. In many of the biomedical sciences, this means being able to either calculate something, predict the responses of the system, or explain the responses of the system. Since only the learner can do the learning, the only possible role for the teacher is to help the learner to learn. This means creating an active learning environment in which the learner can acquire the needed information, continually test the mental models being built, and correct or refine those models as needed. In an active learning environment, students are given ample opportunities to learn to solve problems. If the goal of the course is the achievement of meaningful learning, it is essential that the students then be assessed to determined whether they have reached that goal.

Animals↗
PubMed2003

Predicting gene ontology biological process from temporal gene expression patterns.

The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end, supervised learning was used to analyze microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition, new roles in biological process were hypothesized for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition, many biological process roles hypothesized for uncharacterized genes were found to agree with assumptions based on homology information. To our knowledge, a gene classifier of similar scope and functionality has not been reported earlier.

Animals↗
PubMed2002

Acquisition and extinction in autoshaping.

C. R. Gallistel and J. Gibbon (2000) presented quantitative data on the speed with which animals acquire behavioral responses during autoshaping, together with a statistical model of learning intended to account for them. Although this model captures the form of the dependencies among critical variables, its detailed predictions are substantially at variance with the data. In the present article, further key data on the speed of acquisition are used to motivate an alternative model of learning, in which animals can be interpreted as paying different amounts of attention to stimuli according to estimates of their differential reliabilities as predictors.

Animals↗
PubMed1999

A model of probabilistic category learning.

A new connectionist model (named RASHNL) accounts for many "irrational" phenomena found in nonmetric multiple-cue probability learning, wherein people learn to utilize a number of discrete-valued cues that are partially valid indicators of categorical outcomes. Phenomena accounted for include cue competition, effects of cue salience, utilization of configural information, decreased learning when information is introduced after a delay, and effects of base rates. Experiments 1 and 2 replicate previous experiments on cue competition and cue salience, and fits of the model provide parameter values for making qualitatively correct predictions for many other situations. The model also makes 2 new predictions, confirmed in Experiments 3 and 4. The model formalizes 3 explanatory principles: rapidly shifting attention with learned shifts, decreasing learning rates, and graded similarity in exemplar representation.

Cues↗
PubMed1996

Concept formation vs. logistic regression: predicting death in trauma patients.

This study compares two classification models used to predict survival of injured patients entering the emergency department. Concept formation is a machine learning technique that summarizes known examples cases in the form of a tree. After the tree is constructed, it can then be used to predict the classification of new cases. Logistic regression, on the other hand, is a statistical model that allows for a quantitative relationship for a dichotomous event with several independent variables. The outcome (dependent) variable must have only two choices, e.g. does or does not occur, alive or dead, etc. The result of this model is an equation which is then used to predict the probability of class membership of a new case. The two models were evaluated on a trauma registry database composed of information on all trauma patients admitted in 1992 to a Level I trauma center. A total of 2155 records. representing all trauma patients admitted for more than 24 h or who died in the Emergency Department, were grouped into two databases as follows: (1) discharge status of 'died' (containing 151 records), and (2) any discharge status other than 'died' (containing 2004 records). Both databases contained the same variables.

Artificial Intelligence↗
PubMed1997

Relationships, individual differences, and children's use of literate language.

BACKGROUND: Research in children's oral language and early literacy learning currently stresses the facilitative role of social context. Social context in this literature is typically treated on a macro-level, e.g., mother-child interaction or peer interaction. We present a more differentiated model of peer influences on children's learning one oral language register, 'literate language'. Literate language, which predicts school-based literacy, is defined as talk about language and literacy. AIMS: We suggest that children's temperament and their close relationships, in the form of friendships, play important roles in literate language learning. We present separate models for friends and nonfriends and posit that literate language is learned more effectively between friends because of the emotional tenor of this relationship. When they are with friends children, even those that might be considered 'difficult', disagree, resolve disagreements, then express emotions indicative of social understanding. Reflection upon emotion states, in turn, leads to literate language. SAMPLE: The sample comprised 33 males and 23 females attending American kindergarten classes, with a mean age of 65 months. METHODS: Dyads of same gender and race were observed 12 times across the school year during which time samples of oral language were taken. Measures of children reading and writing were also collected. RESULTS: The data support our model, and the friendship model accounting for more of the variance in literate language (R2 = .69) than did the nonfriend model (R2 = .43). CONCLUSIONS: Children with friends engage in the sort of conceptual conflict and resolutions which maximise use of literate language. This context seems particularly important for 'difficult' children. Future research should continue to examine the interface between individual and group levels variables.

Child, Preschool↗
PubMed2006

Sequence-based protein structure prediction using a reduced state-space hidden Markov model.

This work describes the use of a hidden Markov model (HMM), with a reduced number of states, which simultaneously learns amino acid sequence and secondary structure for proteins of known three-dimensional structure and it is used for two tasks: protein class prediction and fold recognition. The Protein Data Bank and the annotation of the SCOP database are used for training and evaluation of the proposed HMM for a number of protein classes and folds. Results demonstrate that the reduced state-space HMM performs equivalently, or even better in some cases, on classifying proteins than a HMM trained with the amino acid sequence. The major advantage of the proposed approach is that a small number of states is employed and the training algorithm is of low complexity and thus relatively fast.

Algorithms↗
PubMed2025

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods.

DNA-based biodiversity surveys result in massive-scale data, including up to millions of species-of which, most are rare. Making the most of such data for inference and prediction requires modeling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a 'common to rare transfer learning' (CORAL) approach, based on borrowing information from the common species to enable statistically and computationally efficient modeling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2,874 samples.

Animals↗
PubMed1975

Aggression and heat: mediating effects of prior provocation and exposure to an aggressive model.

Sixty-four undergraduate males participated in an experiment designed to examine the effects of level of prior anger arousal, exposure to an aggressive model, and ambient temperature on physical aggression. On the basis of Bandura's social learning theory of aggression, it was predicted that uncomfortably hot environmental conditions would be most effective in facilitating later aggression when subjects had both witnessed the actions of the model and been exposed to strong provocation from the victim, but least effective in this regard when they had neither witnessed the actions of the model nor been exposed to prior instigation. In contrast to these predictions, results indicated that high ambient temperatures facilitated aggression by nonangered subjects but actually inhibited such behavior by those who had previously been provoked.

Aggression↗