Bayesian inference for small-sample capture-recapture data

We consider data on the survival of a population of Cephalorhynchus hectori, Hector's dolphins, in a marine area of New Zealand. To estimate survival probabilities of animal populations, a multiple capture-recapture sampling scheme can be used. In this paper, we propose a practical methodology to derive approximations to posterior distributions based on Laplace methods. We show how to calculate Bayes estimates and credible intervals in this setting.     

A comparison of probe-level and probeset models for small-sample gene expression data

Background  

Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.     

Artificial neural networks for solution scattering data analysis

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Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

Background  

The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set.     

Adipose tissue-derived stem cells show considerable promise for regenerative medicine applications

The stromal-vascular cell fraction (SVF) of adipose tissue can be an abundant source of both multipotent and pluripotent stem cells, known as adipose-derived stem cells or adipose tissue-derived stromal cells (ADSCs). The SVF also contains vascular cells, targeted progenitor cells, and preadipocytes. Stromal cells isolated from adipose tissue express common surface antigens, show the ability to adhere to plastic, and produce forms that resemble fibroblasts. They are characterized by a high proliferation potential and the ability to differentiate into cells of meso-, ecto- and endodermal origin. Although stem cells obtained from an adult organism have smaller capabilities for differentiation in comparison to embryonic and induced pluripotent stem cells (iPSs), the cost of obtaining them is significantly lower. The 40 years of research that mainly focused on the potential of bone marrow stem cells (BMSCs) revealed a number of negative factors: the painful sampling procedure, frequent complications, and small cell yield. The number of stem cells in adipose tissue is relatively large, and obtaining them is less invasive. Sampling through simple procedures such as liposuction performed under local anesthesia is less painful, ensuring patient comfort. The isolated cells are easily grown in culture, and they retain their properties over many passages. That is why adipose tissue has recently been treated as an attractive alternative source of stem cells. Essential aspects of ADSC biology and their use in regenerative medicine will be analyzed in this article.     

Low affinity use-dependent NMDA receptor antagonists show promise for clinical development

Summary. The success of the low affinity use-dependent NMDA receptor antagonists to reach clinical trials can be readily attributed to their wider margins of safety and lack of neurotoxicity at higher doses. Several mechanistic differences distinguish the low affinity from the high affinity use-dependent antagonists: 1) Differential regional affinities for the various NMDA receptor subtypes; 2) The static receptor blockade due to the faster on/off rate receptor kinetics which limit, but do not totally prevent the amount of Ca⁺² entry into the cell during glutamate-induced depolarization; and 3) Rapid egress of the compounds from the ion channel during recovery resulting in less membrane trapping between transmission pulses. Advanced clinical trials are in progress for the following indications: epilepsy, stroke, head trauma, tardive dyskinesia, pain plus Parkinson's, Huntington's and Alzheimer's diseases. Received August 31, 1999 Accepted September 20, 1999     

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.     

Novel method for the display of multivariate data using neural networks

A neural network has been used to reduce the dimensionality of multivariate data sets to produce two-dimensional (2D) displays of these sets. The data consisted of physicochemical properties for sets of biologically active molecules calculated by computational chemistry methods. Previous work has demonstrated that these data contain sufficient relevant information to classify the compounds according to their biological activity. The plots produced by the neural network are compared with results from two other techniques for linear and nonlinear dimension reduction, and are shown to give comparable and, in one case, superior results. Advantages of this technique are discussed.     

Artificial neural networks and their use in quantitative pathology

A brief general introduction to artificial neural networks is presented, examining in detail the structure and operation of a prototype net developed for the solution of a simple pattern recognition problem in quantitative pathology. The process by which a neural network learns through example and gradually embodies its knowledge as a distributed representation is discussed, using this example. The application of neurocomputer technology to problems in quantitative pathology is explored, using real-world and illustrative examples. Included are examples of the use of artificial neural networks for pattern recognition, database analysis and machine vision. In the context of these examples, characteristics of neural nets, such as their ability to tolerate ambiguous, noisy and spurious data and spontaneously generalize from known examples to handle unfamiliar cases, are examined. Finally, the strengths and deficiencies of a connectionist approach are compared to those of traditional symbolic expert system methodology. It is concluded that artificial neural networks, used in conjunction with other nonalgorithmic artificial intelligence techniques and traditional algorithmic processing, may provide useful software engineering tools for the development of systems in quantitative pathology.     

A note on the low-dimensional display of multivariate data using neural networks

A novel neural network technique has been proposed (Livingstone et al. J. Mol. Graphics 1991, 9, 115-118) which is useful for a low-dimensional display of multivariate data sets. The method makes use of the activity values of the hidden neurons in a trained three-layer feed-forward network to produce the low-dimensional display. It was claimed that in contrast to conventional techniques, such as principal components analysis or nonlinear mapping, this technique could be used also to reconstruct, from a given point in the low-dimensional display, the corresponding multivariate input vector via the completely known weight matrices of a suitably trained network. We show here that this claim is unjustified in this general form. When previously unknown, grossly different input vectors are presented to the trained network, they can occupy, for example, exactly the same point in the low-dimensional display which is occupied also by a given training vector, if certain linear relationships between the vector components are fulfilled. Thus, an infinite set of different linearly dependent input vectors is projected onto one single point in the low-dimensional display. Reconstruction of a multivariate vector, starting from this point in the low-dimensional display, is able to lead back to only one multivariate vector (in the example given, to the original training vector).     

Bacterial classification with convolutional neural networks based on different data reduction layers

Abstract

For high accuracy classification of DNA sequences through Convolutional Neural Networks (CNNs), it is essential to use an efficient sequence representation that can accelerate similarity comparison between DNA sequences. In addition, CNN networks can be improved by avoiding the dimensionality problem associated with multi-layer CNN features. This paper presents a new approach for classification of bacterial DNA sequences based on a custom layer. A CNN is used with Frequency Chaos Game Representation (FCGR) of DNA. The FCGR is adopted as a sequence representation method with a suitable choice of the frequency k-lengthen words occurrence in DNA sequences. The DNA sequence is mapped using FCGR that produces an image of a gene sequence. This sequence displays both local and global patterns. A pre-trained CNN is built for image classification. First, the image is converted to feature maps through convolutional layers. This is sometimes followed by a down-sampling operation that reduces the spatial size of the feature map and removes redundant spatial information using the pooling layers. The Random Projection (RP) with an activation function, which carries data with a decent variety with some randomness, is suggested instead of the pooling layers. The feature reduction is achieved while keeping the high accuracy for classifying bacteria into taxonomic levels. The simulation results show that the proposed CNN based on RP has a trade-off between accuracy score and processing time.     

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). By allowing the diffusion and reaction terms to be multilayer perceptrons (MLPs), the nonlinear forms of these terms can be learned while simultaneously converging to the solution of the governing PDE. Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the biological and physical mechanisms that govern the dynamics of the observed system. The method is evaluated on sparse real-world data from wound healing assays with varying initial cell densities [2].     

The use of artificial neural networks for Aegilops species identification based on analysis of D genomes

The possibilities of the use of artificial neural networks (ANNs) for identification of some polyploid species of genus Aegilops based on the idiograms of their D genomes were demonstrated.