Search results for "Computer science"
showing 10 items of 22367 documents
Differential binding cell-SELEX method to identify cell-specific aptamers using high-throughput sequencing
2018
AbstractAptamers have in recent years emerged as a viable alternative to antibodies. High-throughput sequencing (HTS) has revolutionized aptamer research by increasing the number of reads from a few (using Sanger sequencing) to millions (using an HTS approach). Despite the availability and advantages of HTS compared to Sanger sequencing, there are only 50 aptamer HTS sequencing samples available on public databases. HTS data in aptamer research are primarily used to compare sequence enrichment between subsequent selection cycles. This approach does not take full advantage of HTS because the enrichment of sequences during selection can be due to inefficient negative selection when using live…
Motor-skill learning in an insect inspired neuro-computational control system
2017
In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The ad…
Electroporation by concentric-type needle electrodes and arrays.
2017
Abstract The efficacy of genomic medicine depends on gene transfer efficiency. In this area, electroporation has been found to be a highly promising method for physical gene transfer. However, electroporation raises issues related to electrical safety, tissue damage, and the number of required wounds. Concentric-type needle electrodes seek to address these issues by using a lower bias (10 V), a single wound, fewer processing steps, and a smaller working area (≈ 10 mm 3 ), thus offering greater accuracy and precision. Moreover, the needle can be arrayed to simultaneously treat several target regions. This paper proposes a novel method using concentric-type needle electrodes to improve the ef…
Deep learning architectures for prediction of nucleosome positioning from sequences data
2018
Abstract Background Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by com…
The Drosophila Larval Locomotor Circuit Provides a Model to Understand Neural Circuit Development and Function
2021
It is difficult to answer important questions in neuroscience, such as: “how do neural circuits generate behaviour?,” because research is limited by the complexity and inaccessibility of the mammalian nervous system. Invertebrate model organisms offer simpler networks that are easier to manipulate. As a result, much of what we know about the development of neural circuits is derived from work in crustaceans, nematode worms and arguably most of all, the fruit fly, Drosophila melanogaster. This review aims to demonstrate the utility of the Drosophila larval locomotor network as a model circuit, to those who do not usually use the fly in their work. This utility is explored first by discussion…
Application of Graph Clustering and Visualisation Methods to Analysis of Biomolecular Data
2018
In this paper we present an approach based on integrated use of graph clustering and visualisation methods for semi-supervised discovery of biologically significant features in biomolecular data sets. We describe several clustering algorithms that have been custom designed for analysis of biomolecular data and feature an iterated two step approach involving initial computation of thresholds and other parameters used in clustering algorithms, which is followed by identification of connected graph components, and, if needed, by adjustment of clustering parameters for processing of individual subgraphs.
Reducing sample size in experiments with animals: historical controls and related strategies
2015
Reducing the number of animal subjects used in biomedical experiments is desirable for ethical and practical reasons. Previous reviews of the benefits of reducing sample sizes have focused on improving experimental designs and methods of statistical analysis, but reducing the size of control groups has been considered rarely. We discuss how the number of current control animals can be reduced, without loss of statistical power, by incorporating information from historical controls, i.e. subjects used as controls in similar previous experiments. Using example data from published reports, we describe how to incorporate information from historical controls under a range of assumptions that mig…
Next-generation sequencing: big data meets high performance computing
2017
The progress of next-generation sequencing has a major impact on medical and genomic research. This high-throughput technology can now produce billions of short DNA or RNA fragments in excess of a few terabytes of data in a single run. This leads to massive datasets used by a wide range of applications including personalized cancer treatment and precision medicine. In addition to the hugely increased throughput, the cost of using high-throughput technologies has been dramatically decreasing. A low sequencing cost of around US$1000 per genome has now rendered large population-scale projects feasible. However, to make effective use of the produced data, the design of big data algorithms and t…
Network Analysis: Ten Years Shining Light on Host–Parasite Interactions
2020
Biological interactions are key drivers of ecological and evolutionary processes. The complexity of such interactions hinders our understanding of ecological systems and our ability to make effective predictions in changing environments. However, network analysis allows us to better tackle the complexity of ecosystems because it extracts the properties of an ecological system according to the number and distribution of links among interacting entities. The number of studies using network analysis to solve ecological and evolutionary questions in parasitology has increased over the past decade. Here, we synthesise the contribution of network analysis toward disentangling host-parasite proces…
EFMviz
2020
Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1…