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PubMed2004

Consolidation of CS and US representations in associative fear conditioning.

Much attention has been paid to the associative processes that are necessary to fuse together representations of the various components of an episodic memory. In the present study, we focus on the processes involved in the formation of lasting representations of the individual components that make up a fear-conditioning episode. In one-trial contextual fear conditioning experiments, weak conditioning to context occurs if the shock is delivered immediately following placement of the animal in a novel conditioning apparatus, a phenomenon known as the immediate shock deficit. We show that the immediate shock deficit in mice may be alleviated by pre-exposure to either the context or shock. In using this approach to temporally dissect a contextual fear-conditioning task into its constituent representational and associative processes, we are able to examine directly the processes that are important for formation of lasting representations of the context conditioned stimulus (CS) or unconditioned stimulus (US). Our data indicate that the formation of a lasting representation of the context or shock engages protein synthesis-dependent processes. Furthermore, genetic disruption of cAMP-responsive element binding protein (CREB), a transcription factor that regulates the synthesis of new proteins required for long-term memory, disrupts the formation of lasting context memories. We go on to show that the stress hormone epinephrine modulates the consolidation of a context memory, and reverses consolidation deficits in the CREB-deficient mice. Finally we show that disrupting either NMDA or calcium/calmodulin-dependent kinase II (CaMKII) function impairs consolidation of context memories. Together, these data suggest that this approach is particularly suited for the characterization of molecular and cellular processes underlying the formation of stimulus representations.

Animals↗
PubMed2004

The Protein Data Bank and lessons in data management.

The Protein Data Bank (PDB) is a widely used biological database of macromolecular structures with a long history. This history is treated as lessons learned and is used to highlight what are believed to be the best practices important to developers of biological databases today. While the focus is on data quality, data representation and the information technology to support these data, the non-data and technology issues cannot be ignored. The role of the human factor in the form of users, collaborators, scientific society and ad hoc committees is also included.

Database Management Systems↗
PubMedDate not supplied

De novo peptide sequencing via tandem mass spectrometry.

Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics for identifying proteins. Because complete genome sequences are accumulating rapidly, the recent trend in interpretation of MS/MS spectra has been database search. However, de novo MS/MS spectral interpretation remains an open problem typically involving manual interpretation by expert mass spectrometrists. We have developed a new algorithm, SHERENGA, for de novo interpretation that automatically learns fragment ion types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The test data are used to construct optimal path scoring in the graph representations of MS/MS spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins and for validating the results of database search algorithms in fully automated, high-throughput peptide sequencing.

Algorithms↗
PubMed2025

soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.

The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decoders are designed to preserve the unique information embedded in each omics type. We evaluated soFusion on multiple datasets including gene expression, protein expression, and epigenetic features. Across all benchmarks, soFusion consistently outperformed existing methods in delineating anatomical structures and identifying spatial domains with improved continuity and reduced noise. Collectively, soFusion offers an effective solution for spatial multi-omics integration, substantially enhancing the robustness of spatial domain identification.

Humans↗
PubMed2001

Episodic-like memory in animals: psychological criteria, neural mechanisms and the value of episodic-like tasks to investigate animal models of neurodegenerative disease.

The question of whether any non-human species displays episodic memory is controversial. Associative accounts of animal learning recognize that behaviour can change in response to single events but this does not imply that animals need or are later able to recall representations of unique events at a different time and place. The lack of language is also relevant, being the usual medium for communicating about the world, but whether it is critical for the capacity to represent and recall events is a separate matter. One reason for suspecting that certain animals possess an episodic-like memory system is that a variety of learning and memory tasks have been developed that, even though they do not meet the strict criteria required for episodic memory, have an 'episodic-like' character. These include certain one-trial learning tasks, scene-specific discrimination learning, multiple reversal learning, delayed matching and non-matching tasks and, most recently, tasks demanding recollection of 'what, where and when' an event happened. Another reason is that the neuronal architecture of brain areas thought to be involved in episodic memory (including the hippocampal formation) are substantially similar in mammals and, arguably, all vertebrates. Third, our developing understanding of activity-dependent synaptic plasticity (which is a candidate neuronal mechanism for encoding memory traces) suggests that its expression reflects certain physiological characteristics that are ideal components of a neuronal episodic memory system. These include the apparently digital character of synaptic change at individual terminals and the variable persistence of potentiation accounted for by the synaptic tag hypothesis. A further value of studying episodic-like memory in animals is the opportunity it affords to model certain kinds of neurodegenerative disease that, in humans, affect episodic memory. An example is recent work on a transgenic mouse that over-expresses a mutation of human amyloid precursor protein (APP) that occurs in familial Alzheimer's disease, under the control of platelet derived (PD) growth factor promoter (the PDAPP mouse). A striking age- and amyloid plaque-related deficit is seen using a task in which the mice have to keep changing their memory representation of the world rather than learn a single fact.

Alzheimer Disease↗
PubMed2006

Predicting the effect of missense mutations on protein function: analysis with Bayesian networks.

BACKGROUND: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction. RESULTS: Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables. CONCLUSION: The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.

Algorithms↗
PubMed2001

Are grammatical representations useful for learning from biological sequence data?--a case study.

This paper investigates whether Chomsky-like grammar representations are useful for learning cost-effective, comprehensible predictors of members of biological sequence families. The Inductive Logic Programming (ILP) Bayesian approach to learning from positive examples is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). Collectively, five of the co-authors of this paper, have extensive expertise on NPPs and general bioinformatics methods. Their motivation for generating a NPP grammar was that none of the existing bioinformatics methods could provide sufficient cost-savings during the search for new NPPs. Prior to this project experienced specialists at SmithKline Beecham had tried for many months to hand-code such a grammar but without success. Our best predictor makes the search for novel NPPs more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the ILP Bayesian approach to learning from positive examples. A group of features is derived from this grammar. Other groups of features of NPPs are derived using other learning strategies. Amalgams of these groups are formed. A recognition model is generated for each amalgam using C4.5 and C4.5rules and its performance is measured using both predictive accuracy and a new cost function, Relative Advantage (RA). The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives.

Bayes Theorem↗
PubMed1994

Stochastic motif extraction using hidden Markov model.

In this paper, we study the application of an HMM (hidden Markov model) to the problem of representing protein sequences by a stochastic motif. A stochastic protein motif represents the small segments of protein sequences that have a certain function or structure. The stochastic motif, represented by an HMM, has conditional probabilities to deal with the stochastic nature of the motif. This HMM directly reflects the characteristics of the motif, such as a protein periodical structure or grouping. In order to obtain the optimal HMM, we developed the "ilerative duplication method" for HMM topology learning. It starts from a small fully-connected network and iterates the network generation and parameter optimization until it achieves sufficient discrimination accuracy. Using this method, we obtained an HMM for a leucine zipper motif. Compared to the accuracy of a symbolic pattern representation with accuracy of 14.8 percent, an HMM achieved 79.3 percent in prediction. Additionally, the method can obtain an HMM for various types of zinc finger motifs, and it might separate the mixed data. We demonstrated that this approach is applicable to the validation of the protein database; a constructed HMM has indicated that one protein sequence annotated as "leucine-zipper like sequence" in the database is quite different from other leucine-zipper sequences in terms of likelihood, and we found this discrimination is plausible.

Algorithms↗
PubMed2004

Intramaze and extramaze cue processing in adult APPSWE Tg2576 transgenic mice.

The present study examined spatial and nonspatial learning in adult Tg2576 mice. Transgenic mice were impaired in acquisition of a T-maze forced-choice alternation task. However, mutant mice were as sensitive as control mice to the introduction of retention intervals and proactive interference, and this suggested that short-term memory processes were intact in Tg2576 mice. Probe trials revealed that the Tg2576 mice did not use an allocentric strategy to navigate to the goal arm. However, mutant mice acquired an intramaze brightness discrimination, a simple room discrimination, and a contextual biconditional left-right discrimination in a T maze. Results suggest that Tg2576 mice are able to process both intramaze and extramaze stimuli but are impaired in forming an allocentric representation of their environment.

Amyloid beta-Protein Precursor↗
PubMed2005

Protein structure and fold prediction using Tree-Augmented naïve Bayesian classifier.

Due to the large volume of protein sequence data, computational methods to determine the structure class and the fold class of a protein sequence have become essential. Several techniques based on sequence similarity, Neural Networks, Support Vector Machines (SVMs), etc. have been applied. Since most of these classifiers use binary classifiers for multi-classification, there may be (N) c2 classifiers required. This paper presents a framework using the Tree-Augmented Bayesian Networks (TAN) which performs multi-classification based on the theory of learning Bayesian Networks and using improved feature vector representation of (Ding et al., 2001). In order to enhance TAN's performance, pre-processing of data is done by feature discretization and post-processing is done by using Mean Probability Voting (MPV) scheme. The advantage of using Bayesian approach over other learning methods is that the network structure is intuitive. In addition, one can read off the TAN structure probabilities to determine the significance of each feature (say, hydrophobicity) for each class, which helps to further understand the complexity in protein structure. The experiments on the datasets used in three prominent recent works show that our approach is more accurate than other discriminative methods. The framework is implemented on the BAYESPROT web server and it is available at http://www-appn.comp.nus.edu.sg/~bioinfo/bayesprot/Default.htm. More detailed results are also available on the above website.

Algorithms↗
PubMed2002

Conserved codon composition of ribosomal protein coding genes in Escherichia coli, Mycobacterium tuberculosis and Saccharomyces cerevisiae: lessons from supervised machine learning in functional genomics.

Genomics projects have resulted in a flood of sequence data. Functional annotation currently relies almost exclusively on inter-species sequence comparison and is restricted in cases of limited data from related species and widely divergent sequences with no known homologs. Here, we demonstrate that codon composition, a fusion of codon usage bias and amino acid composition signals, can accurately discriminate, in the absence of sequence homology information, cytoplasmic ribosomal protein genes from all other genes of known function in Saccharomyces cerevisiae, Escherichia coli and Mycobacterium tuberculosis using an implementation of support vector machines, SVM(light). Analysis of these codon composition signals is instructive in determining features that confer individuality to ribosomal protein genes. Each of the sets of positively charged, negatively charged and small hydrophobic residues, as well as codon bias, contribute to their distinctive codon composition profile. The representation of all these signals is sensitively detected, combined and augmented by the SVMs to perform an accurate classification. Of special mention is an obvious outlier, yeast gene RPL22B, highly homologous to RPL22A but employing very different codon usage, perhaps indicating a non-ribosomal function. Finally, we propose that codon composition be used in combination with other attributes in gene/protein classification by supervised machine learning algorithms.

Algorithms↗
PubMed2005

Song tutoring triggers CaMKII phosphorylation within a specialized portion of the avian basal ganglia.

In several songbird species, a specialized anterior forebrain pathway (AFP) that includes part of the avian basal ganglia has been implicated specifically in song learning. To further elucidate cellular mechanisms and circuitry involved in vocal learning, we used quantitative immunoblot analysis to determine if early song tutoring promotes within the AFP phosphorylation of calcium/calmodulin-dependent kinase II (CaMKII), a multifunctional kinase whose phosphorylation at threonine 286 is critical for many forms of neural plasticity and behavioral learning. We report that in young male zebra finches likely to have begun the process of song acquisition, brief tutoring by a familiar conspecific adult promotes a dramatic increase in levels of phosphorylated CaMKII (pCaMKII) in Area X, the striatal/pallidal component of the AFP. In contrast, pCaMKII levels in this region were not elevated if 1) the tutor did not sing, 2) the tutor sang but was visually isolated from the pupil, or 3) the tutor was an unfamiliar adult. In young males that had not previously heard any conspecific song, first exposure to a song tutor produced a more modest, but significant rise in pCaMKII levels. Young females (who do not develop song behavior) did not exhibit any effect of tutoring on pCaMKII levels in that portion of the basal ganglia that corresponds to Area X in males. These data are consistent with the hypothesis that Area X participates in encoding and/or attaching reward value to a representation of tutor song that is accessed later to guide motor learning.

Acoustic Stimulation↗
PubMed2005

Learning-induced plasticity of cortical representations does not affect GAD65 mRNA expression and immunolabeling of cortical neuropil.

Two forms of glutamic acid decarboxylase (GAD) are present in inhibitory neurons of the mammalian brain, a 65-kDa isoform (GAD65) and a 67-kDa isoform (GAD67). We have previously found that GAD67 is upregulated during learning-dependent plasticity of cortical vibrissal representations of adult mice. After sensory conditioning involving pairing stimulation of vibrissae with a tail shock, the increase in mRNA expression and density of GAD67-immunoreactive neurons was observed in barrels representing vibrissae activated during the training. In the present study, using the same experimental model, we examined GAD65 mRNA and protein levels in the barrel cortex. For this purpose, we used in situ hybridization and immunohistochemistry. No changes in the level of GAD65 mRNA expression were detected after the training. The pattern of GAD65 mRNA expression was complementary to that observed for GAD67. Immunocytochemical analysis found no changes in immunolabeling of neuropil of the barrels representing the vibrissae activated during the training. The results show that, in contrast to GAD67, cortical plasticity induced by sensory learning does not affect the expression of GAD65.

Animals↗
PubMed2000

Adaptive information processing in microtubule networks.

Microtubule networks provide a wide range of microskeletal and micromuscular functionalities. Evidence from a number of directions suggests that they can also serve as a medium for intracellular signaling processing. The model presented here comprises an empirically motivated representation of microtubule growth dynamics, an abstract representation of signal processing, and a feedback learning mechanism that we refer to as adaptive self-stabilization. The growth model mimics the dynamic instability picture of microtubule formation and decomposition, but as modulated by the binding activity of microtubule associated proteins (or MAPs). The signal processing submodel treats each microtubule as a string of linked discrete oscillators capable of propagating signals that are introduced, manipulated, and extracted by bound MAP activity. Adaptive self-stabilization is essentially feedback acting on signal processing capabilities via the growth dynamics. The network is presented with a training set of patterns. If the input-output behavior is satisfactory MAP binding affinity increases, thereby stabilizing the network structure; otherwise the binding affinity decreases, allowing for more structural variation. The results obtained suggest that adaptive capabilities are practically inevitable in microtubule networks, a conclusion strengthened by the fact that the signal processing and growth dynamics mechanisms available in nature are undoubtedly much richer than those represented in the model.

Adaptation, Physiological↗
PubMed2001

Pharmacokinetic software for the health sciences: choosing the right package for teaching purposes.

Computer assisted learning has an important role in the teaching of pharmacokinetics to health sciences students because it transfers the emphasis from the purely mathematical domain to an 'experiential' domain in which graphical and symbolic representations of actions and their consequences form the major focus for learning. Basic pharmacokinetic concepts can be taught by experimenting with the interplay between dose and dosage interval with drug absorption (e.g. absorption rate, bioavailability), drug distribution (e.g. volume of distribution, protein binding) and drug elimination (e.g. clearance) on drug concentrations using library ('canned') pharmacokinetic models. Such 'what if' approaches are found in calculator-simulators such as PharmaCalc, Practical Pharmacokinetics and PK Solutions. Others such as SAAM II, ModelMaker, and Stella represent the 'systems dynamics' genre, which requires the user to conceptualise a problem and formulate the model on-screen using symbols, icons, and directional arrows. The choice of software should be determined by the aims of the subject/course, the experience and background of the students in pharmacokinetics, and institutional factors including price and networking capabilities of the package(s). Enhanced learning may result if the computer teaching of pharmacokinetics is supported by tutorials, especially where the techniques are applied to solving problems in which the link with healthcare practices is clearly established.

Computer Simulation↗
PubMed1994

Neural network model of the genetic code is strongly correlated to the GES scale of amino acid transfer free energies.

A neural network trained to classify the 61 nucleotide triplets of the genetic code into 20 amino acid categories develops in its internal representation a pattern matching the relative cost of transferring amino acids with satisfied backbone hydrogen bonds from water to an environment of dielectric constant of roughly 2.0. Such environments are typically found in lipid membranes or in the interior of proteins. In learning the mapping between the codons and the categories, the network groups the amino acids according to the scale of transfer free energies developed by Engelman, Goldman and Steitz. Several other scales based on internal preference statistics also agree reasonably well with the network grouping. The network is able to relate the structure of the genetic code to quantifications of amino acid hydrophobicity-hydrophilicity more systematically than the numerous attempts made earlier. Due to its inherent non-linearity, the code is also shown to impose decisive constraints on algorithmic analysis of the protein coding potential of DNA.

Amino Acid Sequence↗
PubMed2003

Differential pattern of cAMP response element-binding protein activation in the rat brain after conditioned aversion as a function of the associative process engaged: taste versus context association.

Ample data indicate that cAMP-response element-binding protein (CREB) is essential for the formation of long-term memory in various species and learning systems. This implies that activated CREB could delineate neuronal circuits that subserve items in memory, while leaving open the possibility that the specifics of CREB activation itself contribute to the specificity of the internal representation encoded by the relevant circuit. We describe here the differential activation of CREB in the rat brain as a function of two related yet distinct forms of aversive conditioning: conditioned taste aversion (CTA) and conditioned context aversion (CCA). We found that CTA induces strong CREB activation in the insular cortex (IC) and the lateral septum (LS), but not in the parietal cortex (PC) and the medial septum (MS). In contrast, CCA results in strong activation in the PC and MS, but not in the IC and LS. These findings are congruent with a model that links differential pattern of activity within the LS and the MS with the acquisition of elemental versus contextual conditioning and, more generally, with the notion that CREB activation delineates learning-dependent circuits as a function of the type of cognitive process engaged.

Animals↗
PubMed2025

Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.

MOTIVATION: Pinpointing the subcellular location of proteins is essential for studying protein function and related diseases. Advances in spatial proteomics have shown that automatic recognition of protein subcellular localization from images could highly facilitate protein translocation analysis and biomarker discovery, but existing machine-learning works have been mostly limited to processing 2D images. By contrast, 3D images have higher spatial resolution and allow researchers to observe cellular structures in their natural context, but currently, there are only a few studies of 3D image processing for protein distribution analysis due to the lack of data and complexity of modeling. RESULTS: We developed a knowledge-enhanced protein subcellular localization model, KE3DLoc, which could recognize distribution patterns in 3D fluorescence microscope images using deep learning methods. The model designs an image feature extraction module that incorporates information from 3D and 2D projected cells and implements asymmetric loss and confidence weights to address data imbalance and weak cell annotation issues. Besides, considering that the biological knowledge in the Gene Ontology (GO) database can provide valuable support for protein location understanding, the KE3DLoc model incorporates a novel knowledge enhancement module that optimizes the protein representation by related knowledge graphs derived from the GO. Since the image module and the knowledge module calculate features from different levels, KE3DLoc designs protein ID aggregation to enhance the consistency of protein features across different cells. Experimental results on three public datasets have demonstrated that the KE3DLoc significantly outperforms existing methods and provides valuable insights for spatial proteomics research. AVAILABILITY AND IMPLEMENTATION: All datasets and codes used in this study are available at GitHub: https://github.com/PRBioimages/KE3DLoc.

Microscopy, Fluorescence↗