Optimal inference of sameness.
Ontology highlight
ABSTRACT:
SUBMITTER: van den Berg R
PROVIDER: S-EPMC3286990 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In natural data, the class and intensity of stimuli are correlated. Current machine learning algorithms ignore this ubiquitous statistical property of stimuli, usually by requiring normalized inputs. From a biological perspective, it remains unclear how neural circuits may account for these dependencies in inference and learning. Here, we use a probabilistic framework to model class-specific intensity variations, and we derive approximate inference and online learning rules which reflect common hallmarks of neural computation. Concretely, we show that a neural circuit equipped with specific forms of synaptic and intrinsic plasticity (IP) can learn the class-specific features and intensities of stimuli simultaneously. Our model provides a normative interpretation of IP as a critical part of sensory learning and predicts that neurons can represent nontrivial input statistics in their excitabilities. Computationally, our approach yields improved statistical representations for realistic datasets in the visual and auditory domains. In particular, we demonstrate the utility of the model in estimating the contrastive stress of speech.
| S-EPMC6030062 | biostudies-literature
Project description:Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior.
| S-EPMC4071291 | biostudies-literature
Project description:Time-series single-cell RNA sequencing (scRNA-seq) data in biology has opened the door to elucidate cell differentiation processes. In this context, the optimal transport theory has attracted attention to interpolate scRNA-seq data in adjacent times and infer the trajec- tories of cell differentiation. This paper presents scEGOT, a novel comprehensive single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport (EGOT). The method- ology facilitates the inference of cell differentiation pathways and dynamics from time-series scRNA-seq data. The scEGOT frame- work provides comprehensive outputs, including cell state graphs, velocity fields of cell differentiation, time interpolations of scRNA- seq data, space-time continuous videos of cell differentiation with gene expressions, gene regulatory networks, and reconstructions of Waddington’s epigenetic landscape. These outputs allow us to un- derstand the dynamics of the cell differentiation process from mul- tiple perspectives. To demonstrate that scEGOT is a powerful and versatile tool for single-cell biology, it was applied to time-series scRNA-seq data of the human primordial germ cell-like cell (human PGCLC) induction system. This application provided new insights into the mechanism of PGCLC/somatic cell segregation. In particu- lar, we identified the PGCLC progenitor population and the time point of the segregation. In addition, we found novel key genes that may be critical for human PGCLC differentiation.
2024-11-08 | GSE241287 | GEO
Project description:Single-molecule-localization-based superresolution microscopy requires accurate sample drift correction to achieve good results. Common approaches for drift compensation include using fiducial markers and direct drift estimation by image correlation. The former increases the experimental complexity and the latter estimates drift at a reduced temporal resolution. Here, we present, to our knowledge, a new approach for drift correction based on the Bayesian statistical framework. The technique has the advantage of being able to calculate the drifts for every image frame of the data set directly from the single-molecule coordinates. We present the theoretical foundation of the algorithm and an implementation that achieves significantly higher accuracy than image-correlation-based estimations.
| S-EPMC4643251 | biostudies-literature
Project description:MotivationWe addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference.ResultsWe developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4?100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates.ConclusionsREDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality.Availability and implementationMATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE CONTACT: rudi.gunawan@chem.ethz.chSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4803391 | biostudies-literature
Project description:In an isolated population, individuals are likely to share large genetic regions inherited from common ancestors. Identity by descent (IBD) can be inferred from SNP genotypes, which is useful in a number of applications, including identifying genetic variants influencing complex disease risk, and planning efficient cohort-sequencing strategies. We present ANCHAP--a method for detecting IBD in isolated populations. We compare accuracy of the method against other long-range and local phasing methods, using parent-offspring trios. In our experiments, we show that ANCHAP performs similarly as the other long-range method, but requires an order-of-magnitude less computational resources. A local phasing model is able to achieve similar sensitivity, but only at the cost of higher false discovery rates. In some regions of the genome, the studied individuals share haplotypes particularly often, which hints at the history of the populations studied. We demonstrate the method using SNP genotypes from three isolated island populations, as well as in a cohort of unrelated individuals. In samples from three isolated populations of around 1000 individual each, an average individual shares a haplotype at a genetic locus with 9-12 other individuals, compared with only 1 individual within the non-isolated population. We describe an application of ANCHAP to optimally choose samples in resequencing studies. We find that with sample sizes of 1000 individuals from an isolated population genotyped using a dense SNP array, and with 20% of these individuals sequenced, 65% of sequences of the unsequenced subjects can be partially inferred.
| S-EPMC3778345 | biostudies-literature
Project description:MotivationA plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures.ResultsWe propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and Bayesian network methods using four benchmark datasets from DREAM. In our final study, we showcased two context-specific signaling pathways activated in breast cancer.AvailabilitySource codes are available from http://dl.dropbox.com/u/16000775/sa_sc.zip.
| S-EPMC3278757 | biostudies-literature
Project description:MotivationRNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study.ResultsWe describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweigh the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally, we apply our method to the Religious Orders Study and Memory and Aging Project dataset, and we report what to our knowledge is the first replicable trans-eQTL effect in human brain.Availabilityand implementationDataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix).Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8128479 | biostudies-literature
Project description:MotivationHigh-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity are typically achieved through unsupervised clustering, which crucially relies on a similarity metric.ResultsWe here propose the use of Optimal Transport (OT) as a cell-cell similarity metric for single-cell omics data. OT defines distances to compare high-dimensional data represented as probability distributions. To speed up computations and cope with the high dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-art metrics over 13 independent datasets, including simulated, scRNA-seq, scATAC-seq and single-cell DNA methylation data. First, we test the ability of the metrics to detect the similarity between cells belonging to the same groups (e.g. cell types, cell lines of origin). Then, we apply unsupervised clustering and test the quality of the resulting clusters. OT is found to improve cell-cell similarity inference and cell clustering in all simulated and real scRNA-seq data, as well as in scATAC-seq and single-cell DNA methylation data.Availability and implementationAll our analyses are reproducible through the OT-scOmics Jupyter notebook available at https://github.com/ComputationalSystemsBiology/OT-scOmics.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9004651 | biostudies-literature
Project description:"Decisions from experience" (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the "sampling paradigm," which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the "optimal" sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE.
| S-EPMC4564691 | biostudies-literature