Search results for "artificial"
showing 10 items of 7394 documents
Deep learning in next-generation sequencing
2020
Highlights • Machine learning increasingly important for NGS. • Deep learning can improve many NGS applications.
Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics
2020
One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC&ndash
Morphogenetically-Active Barrier Membrane for Guided Bone Regeneration, Based on Amorphous Polyphosphate
2017
We describe a novel regeneratively-active barrier membrane which consists of a durable electrospun poly(ε-caprolactone) (PCL) net covered with a morphogenetically-active biohybrid material composed of collagen and inorganic polyphosphate (polyP). The patch-like fibrous collagen structures are decorated with small amorphous polyP nanoparticles (50 nm) formed by precipitation of this energy-rich and enzyme-degradable (alkaline phosphatase) polymer in the presence of calcium ions. The fabricated PCL-polyP/collagen hybrid mats are characterized by advantageous biomechanical properties, such as enhanced flexibility and stretchability with almost unaltered tensile strength of the PCL net. The pol…
Low-Cost Optical Mapping Systems for Panoramic Imaging of Complex Arrhythmias and Drug-Action in Translational Heart Models.
2017
[EN] Panoramic optical mapping is the primary method for imaging electrophysiological activity from the entire outer surface of Langendorff-perfused hearts. To date, it is the only method of simultaneously measuring multiple key electrophysiological parameters, such as transmembrane voltage and intracellular free calcium, at high spatial and temporal resolution. Despite the impact it has already had on the fields of cardiac arrhythmias and whole-heart computational modeling, present-day system designs precludes its adoption by the broader cardiovascular research community because of their high costs. Taking advantage of recent technological advances, we developed and validated low-cost opti…
A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning
2019
Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we …
Establishing gene models from the Pinus pinaster genome using gene capture and BAC sequencing
2016
Background In the era of DNA throughput sequencing, assembling and understanding gymnosperm mega-genomes remains a challenge. Although drafts of three conifer genomes have recently been published, this number is too low to understand the full complexity of conifer genomes. Using techniques focused on specific genes, gene models can be established that can aid in the assembly of gene-rich regions, and this information can be used to compare genomes and understand functional evolution. Results In this study, gene capture technology combined with BAC isolation and sequencing was used as an experimental approach to establish de novo gene structures without a reference genome. Probes were design…
Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters
2017
Quantum Clustering (QC) provides an alternative approach to clustering algorithms, several of which are based on geometric relationships between data points. Instead, QC makes use of quantum mechanics concepts to find structures (clusters) in data sets by finding the minima of a quantum potential. The starting point of QC is a Parzen estimator with a fixed length scale, which significantly affects the final cluster allocation. This dependence on an adjustable parameter is common to other methods. We propose a framework to find suitable values of the length parameter σ by optimising twin measures of cluster separation and consistency for a given cluster number. This is an extension of the Se…
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…
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…
Improving Docking Performance Using Negative Image-Based Rescoring
2017
Despite the large computational costs of molecular docking, the default scoring functions are often unable to recognize the active hits from the inactive molecules in large-scale virtual screening experiments. Thus, even though a correct binding pose might be sampled during the docking, the active compound or its biologically relevant pose is not necessarily given high enough score to arouse the attention. Various rescoring and post-processing approaches have emerged for improving the docking performance. Here, it is shown that the very early enrichment (number of actives scored higher than 1% of the highest ranked decoys) can be improved on average 2.5-fold or even 8.7-fold by comparing th…