Search results for "Python"
showing 10 items of 160 documents
OPETH: Open Source Solution for Real-Time Peri-Event Time Histogram Based on Open Ephys
2019
Single cell electrophysiology remains one of the most widely used approaches of systems neuroscience. Decisions made by the experimenter during electrophysiology recording largely determine recording quality, duration of the project and value of the collected data. Therefore, online feedback aiding these decisions can lower monetary and time investment, and substantially speed up projects as well as allow novel studies otherwise not possible due to prohibitively low throughput. Real-time feedback is especially important in studies that involve optogenetic cell type identification by enabling a systematic search for neurons of interest. However, such tools are scarce and limited to costly co…
Learning Molecular Classes from Small Numbers of Positive Examples Using Graph Grammars
2021
We consider the following problem: A researcher identified a small number of molecules with a certain property of interest and now wants to find further molecules sharing this property in a database. This can be described as learning molecular classes from small numbers of positive examples. In this work, we propose a method that is based on learning a graph grammar for the molecular class. We consider the type of graph grammars proposed by Althaus et al. [2], as it can be easily interpreted and allows relatively efficient queries. We identify rules that are frequently encountered in the positive examples and use these to construct a graph grammar. We then classify a molecule as being conta…
On complexity and motion planning for co-rank one sub-Riemannian metrics
2004
In this paper, we study the motion planning problem for generic sub-Riemannian metrics of co-rank one. We give explicit expressions for the metric complexity (in the sense of Jean (10,11)), in terms of the elementary invariants of the problem. We construct asymptotic optimal syntheses. It turns out that among the results we show, the most complicated case is the 3-dimensional. Besides the generic C ∞ case, we study some non-generic generalizations in the analytic case.
Right-Justified Characterization for Generating Regular Pattern Avoiding Permutations
2017
ECO-method and its corresponding succession rules allow to recursively define and construct combinatorial objects. The induced generating trees can be coded by corresponding pattern avoiding permutations. We refine succession rules by using succession functions in case when avoided patterns are regular or c-regular. Although regular patterns are hard to be recognized in general, we give a characterization for its right-justified property which is a prerequisite in the definition of the regular pattern. Based on this characterization, we show the (c-)regularity for various classes of permutations avoiding sets of patterns with variable lengths. Last, the technique of succession functions per…
Metamodelling architectures for complex data integration in systems biology
2010
Systems biology aims at deciphering the functioning of biological systems on the basis of the knowledge of their molecular components and the relations between such components. To address the issues involved, high-throughput technologies are used. Taking advantage of the standards that are being currently developed to achieve consensual representations of technological domains, we present a metamodelling architecture based on these standards. The proposed architecture organises standard-specific metamodels and models into a single hierarchy. Each metamodel describes a consensus that is shared by several models of applications. A metamodel construct for description of faceted element is prop…
PyDSC: a simple tool to treat differential scanning calorimetry data
2020
AbstractHerein, we describe an open-source, Python-based, script to treat the output of differential scanning calorimetry (DSC) experiments, called pyDSC, available free of charge for download at https://github.com/leonardo-chiappisi/pyDSC under a GNU General Public License v3.0. The main aim of this program is to provide the community with a simple program to analyze raw DSC data. Key features include the correction from spurious signals, and, most importantly, the baseline is computed with a robust, physically consistent approach. We also show that the baseline correction routine implemented in the script is significantly more reproducible than different standard ones proposed by propriet…
On the application of the generalized means to construct multiresolution schemes satisfying certain inequalities proving stability
2021
Multiresolution representations of data are known to be powerful tools in data analysis and processing, and they are particularly interesting for data compression. In order to obtain a proper definition of the edges, a good option is to use nonlinear reconstructions. These nonlinear reconstruction are the heart of the prediction processes which appear in the definition of the nonlinear subdivision and multiresolution schemes. We define and study some nonlinear reconstructions based on the use of nonlinear means, more in concrete the so-called Generalized means. These means have two interesting properties that will allow us to get associated reconstruction operators adapted to the presence o…
tbg - a new file format for genomic data
2021
AbstractMotivationThe question of determining whether a Single-Nucleotide Polymorphism (SNP) or a variant in general leads to a change in the amino acid sequence of a protein coding gene is often a laborious and time-consuming challenge. Here, we introduce the tbg file format for storing genomic data and tbg-tools, a user-friendly toolbox for the faster analysis of SNPs. The file format stores information for each nucleotide in each gene, allowing to predict which change in the amino acid sequence will be caused by a variant in the nucleotide sequence. Our new tool therefore has the potential to make biological sense of the unprecedented amount of genome-wide genetic variation that research…
Interpretable machine learning models for single-cell ChIP-seq imputation
2019
AbstractMotivationSingle-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors.ResultsImputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene i…
Indexing a sequence for mapping reads with a single mismatch
2014
Mapping reads against a genome sequence is an interesting and useful problem in computational molecular biology and bioinformatics. In this paper, we focus on the problem of indexing a sequence for mapping reads with a single mismatch. We first focus on a simpler problem where the length of the pattern is given beforehand during the data structure construction. This version of the problem is interesting in its own right in the context of the next generation sequencing. In the sequel, we show how to solve the more general problem. In both cases, our algorithm can construct an efficient data structure in time and space and can answer subsequent queries in time. Here, n is the length of the s…