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检索条件"主题词=Programming language"

共 1133 条记录，以下是241-250 订阅

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Promoting Students' programming Skills in Constructions for Teaching Materials and Curriculums:Experiments for Comprehensive Application of programming Methods

Promoting Students' Programming Skills in Constructions for ...

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作者： Yonghui Wu Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University

The goal for experiments for programming languages is to polish students' programming skills solving problems by programming *** contests are contests solving problems by programming.A programming language experiment based on "solving problems by programming" consists of the related knowledge background for programming languages and methods,programming contest problems and their analysis and *** paper specifies experiments for programming languages based on "solving problems by programming":the comprehensive application of programming *** experiments enlighten students to solve a problem by programming through comprehensively applying programming methods:offline method,binary search,and real precision processing.

关键词： programming language programming Contest Problem Offline Binary Search Real Precision Processing

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A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

A Practical Guide to Integrating Multimodal Machine Learning...

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作者： Supreeta Vijayakumar Giuseppe Magazzù Pradip Moon Annalisa Occhipinti Claudio Angione

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://***/Angione-Lab/Tutorials_Combining_ML_and_GSMM . less

关键词： Machine Learning Data Mining MATLAB programming language Python Data analysis Genomics Transcriptomics Deep learning Proteomics Lipidomics Metabolomics DNA Sequencing Microarray RNA-seq Mass Spectrometry Fluxomics Statistical Calculation Neural Network Model Saccharomyces cerevisiae Homo sapiens Data integration Flux balance analysis Multimodal Metabolic modeling Multi-omics Cancer survival prediction

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Simulating Crop Root Systems Using

Simulating Crop Root Systems Using

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作者： Ernst D. Schäfer Markus R. Owen Johannes A. Postma Christian Kuppe Christopher K. Black Jonathan P. Lynch

Functional–structural plant models are valuable modeling tools in analyzing plant development. A functional–structural plant model combines a three-dimensional representation of plant structure with models for physiological functions in order to better understand plant development. We present a guide to simulating crop root systems with OpenSimRoot, a feature-rich, highly cited, and open-source functional–structural root architecture model. We describe in detail how to create your own input files in conjunction with some examples. The aim of this guide is to highlight the potential of computational modeling in biology and to make modeling more accessible to the plant science community. less

关键词： Python programming language Plant development Nutrient uptake Modelling Organ growth Mathematical modelling Root system architectures

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Transcription Factor Chromatin ImmunoprecipitationChromatin Immunoprecipitation (ChIP) in Endothelial Cells

Transcription Factor Chromatin ImmunoprecipitationChromatin ...

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作者： Philip Kitchen Kevin Gaston Padma-Sheela Jayaraman

Interactions between DNA and proteins are crucial for the regulation of gene expression. Chromatin immunoprecipitation (ChIP) is a powerful technique that allows the study of specific protein–DNA interactions in cultured cells and fresh or fixed tissue. Chromatin is isolated and sheared, and antibodies against the protein(s) of interest are used to isolate specific protein–DNA complexes. Subsequent analysis by real-time polymerase chain reaction (qPCR) or next-generation sequencing (NGS) allows identification and quantification of the co-purified DNA fragments, and NGS also gives insight into the genomic binding sites of a protein. Here we describe a cross-linking ChIP (X-ChIP) protocol, based around the example of a myc-tagged Proline-Rich Homeodomain (PRH) protein expressed in human umbilical vein endothelial cells. We also describe how to analyse specific known or suspected binding sites using quantitative PCR as well as how to analyse genome-wide binding from ChIP sequencing data. less

关键词： ChIP-seq Real-time PCR Immunoprecipitation Cell And Tissue Culture Cross-linking DNA Extraction ChIP Assay Sonication DNA Purification Agarose Gel Electrophoresis Illumina Sequencing Library Preparation Bowtie Genome Visualization Peak Calling Genome Annotation DNA Ligation programming language Chromatin Transcription factor PRH HHEX

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New Tools for Streamlined In Vivo Homing Peptide Identification

New Tools for Streamlined In Vivo Homing Peptide Identificat...

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作者： Kristina Põšnograjeva Karlis Pleiko Maarja Haugas Tambet Teesalu

In vivo peptide-phage display is an unbiased technique for mapping of the vascular diversity and identification of homing peptides. This chapter is intended to serve as a structured practical guide to execute in vivo T7 phage biopanning and data analysis experiments. We discuss experimental designs and protocols with emphasis on application of high-throughput sequencing-based technologies for streamlined in vivo biopanning and validation of homing peptides. less

关键词： High-Throughput Sequencing Phage Display Peptide Identification Pull-down Assay Sanger Sequencing Ultracentrifugation Dialysis Intravenous Injection RNA-seq DNA Extraction Capillary Electrophoresis (CE) Emulsion PCR Differential Binding Analysis Affinity Chromatography Snap Freeze Peptide Conjugation Mass Spectrometry PCR Dissection Data analysis programming language Enterobacteria phage T7 Mus musculus Homing peptide Vascular ZIP codes Affinity targeting

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A Practical Guide to Using Structural Variants for Genome-Wide Association Studies

A Practical Guide to Using Structural Variants for Genome-Wi...

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作者： Marc-André Lemay Sidiki Malle

Structural variants (SVs) are known to have large functional impacts on phenotypes of agricultural interest, but they have yet to be routinely used for GWAS. Apart from the difficulty in obtaining high-quality SV genotype data for large populations, one of the main hurdles to using SVs for GWAS lies in formatting of genotype data for use with popular GWAS programs. This protocol describes how typical SV genotype data can be formatted for input to three GWAS programs commonly used by the plant genetics community: TASSEL, GAPIT, and mrMLM. less

关键词： programming language Whole-genome Sequencing Glycine max Structural variation TASSEL GAPIT mrMLM VCF file

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Atomistic Molecular Dynamics Simulations of DNA in Complex 3D Arrangements for Comparison with Lower Resolution Structural Experiments

Atomistic Molecular Dynamics Simulations of DNA in Complex 3...

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作者： George Watson Victor Velasco-Berrelleza Agnes Noy

Atomic-level computer simulations are a very useful tool for describing the structure and dynamics of complex biomolecules such as DNA and for providing detail at a resolution where experimental techniques cannot arrive. Molecular dynamics (MD) simulations of mechanically distorted DNA caused by agents like supercoiling and protein binding are computationally challenging due to the large size of the associated systems and timescales. However, nowadays they are achievable thanks to the efficient usage of GPU and to the improvements of continuum solvation models. This together with the concurrent improvements in the resolution of single-molecule experiments, such as atomic force microscopy (AFM), makes possible the convergence between the two. Here we present detailed protocols for doing so: for performing molecular dynamics (MD) simulations of DNA adopting complex three-dimensional arrangements and for comparing the outcome of the calculations with single-molecule experimental data with a lower resolution than atomic. less

关键词： Molecular Dynamics Simulation Atomic Force Microscopy (AFM) Python Imaging Techniques Image Analysis programming language DNA topology DNA protein recognition

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Finding Gene Associations by Text Mining and Annotating it with Gene Ontology

Finding Gene Associations by Text Mining and Annotating it w...

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作者： Oviya Ramalakshmi Iyyappan Sharanya Manoharan

Digitalization of the research articles and their maintenance in a database was the first stage toward the development of biomedical research. With the large amounts of research being published daily, it has created a large gap in accessing all the articles for review to a given problem. To understand any biological process, an insight into the role of each element in the genome is essential. But with this gap in manual curation of literature, there are chances that important biological information may be lost. Hence, text mining plays an important role in bridging this gap and extracting important biological information from the text, finding associations among them and predicting annotations. An annotation may be gene, gene products, gene names, their physical and functional characteristics, and so on. The process of annotations may be classified as structural annotation, functional annotation, and relational annotation. In this chapter, a basic protocol utilizing text mining to extract biological information and predict their functional role based on Gene Ontology is provided. less

关键词： Text Mining programming language Python Expasy Protein Analysis Proteomics Statistical Calculation Functional annotation Semantic similarity MeSH terms Gene Ontology (GO) Gene function prediction

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Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering

Mining of Single-Cell Signaling Time-Series for Dynamic Phen...

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作者： Maciej Dobrzyński Marc-Antoine Jacques Olivier Pertz

Fluorescent live cell time-lapse microscopy is steadily contributing to our better understanding of the relationship between cell signaling and fate. However, large volumes of time-series data generated in these experiments and the heterogenous nature of signaling responses due to cell-cell variability hinder the exploration of such datasets. The population averages insufficiently describe the dynamics, yet finding prototypic dynamic patterns that relate to different cell fates is difficult when mining thousands of time-series. Here we demonstrate a protocol where we identify such dynamic phenotypes in a population of PC-12 cells that respond to a range of sustained growth factor perturbations. We use Time-Course Inspector, a free R/Shiny web application to explore and cluster single-cell time-series. less

关键词： Data analysis Time Lapse Microscopy Statistical Calculation FRET programming language Clustering Computational biology Signaling dynamics Signal processing Time-series Single-cell data Cell-cell heterogeneity

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Extracting Significant Comorbid Diseases from MeSH Index of PubMed

Extracting Significant Comorbid Diseases from MeSH Index of ...

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作者： Dheepa Anand Sharanya Manoharan Oviya Ramalakshmi Iyyappan Sadhanha Anand Kalpana Raja

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level. less

关键词： Text Mining Natural language Processing programming language Python Statistical Calculation Information retrieval Information extraction Disease comorbidity

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