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Genetic and Evolutionary Computation Conference (GECCO)

作者： Yoo, Shin Korea Adv Inst Sci & Technol 291 Daehak Ro Daejeon 34141 South Korea

ISBN: (纸本)9781450349390

We present a vision of genetic improvement firmly embedded in, and supported by, programming languages. Genetic improvement has already been envisioned as the next compiler, which would take human written programs as input and return versions optimised with respect to various objectives. As an intermediate stage, or perhaps to complement the fully automated vision, we imagine genetic improvement processes that are hinted at and directed by humans but understood and undertaken by programming languages and their runtimes, via interactions through the source code. We examine existing similar ideas and examine the benefits of embedding them within programming languages.

关键词： Genetic Improvement Self Adaptation programming language

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An omnidirectional visualization model of personalized gene regulatory networks

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NPJ SYSTEMS BIOLOGY AND APPLICATIONS 2019年第1期5卷 38页

作者： Chen, Chixiang Jiang, Libo Fu, Guifang Wang, Ming Wang, Yaqun Shen, Biyi Liu, Zhenqiu Wang, Zuoheng Hou, Wei Berceli, Scott A. Wu, Rongling Penn State Univ Ctr Stat Genet Dept Publ Hlth Sci & Stat Hershey PA 17033 USA Penn State Coll Med Dept Publ Hlth Sci Hershey PA 17033 USA Beijing Forestry Univ Coll Biol Sci & Technol Ctr Computat Biol Beijing 100083 Peoples R China SUNY Binghamton Univ Dept Math Sci Binghamton NY 13902 USA Rutgers Sch Publ Hlth Dept Biostat & Epidemiol Piscataway NJ 08854 USA Yale Sch Publ Hlth Dept Biostat New Heaven CT 06520 USA Stony Brook Sch Med Dept Family Populat & Prevent Med Stony Brook NY 11794 USA Malcom Randall VA Med Ctr Gainesville FL 32610 USA Univ Florida Dept Surg Box 100128 Gainesville FL 32610 USA Univ Florida Dept Biomed Engn Gainesville FL 32610 USA

Gene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the individual's response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene coexpression, but such graphs cannot quantify how gene-gene interactions vary among individuals and how they alter structurally across spatiotemporal gradients. Here, we develop a general framework for inferring informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from routine transcriptional experiments. This framework is constructed by a system of quasi-dynamic ordinary differential equations (qd0DEs) derived from the combination of ecological and evolutionary theories. We reconstruct idopNetworks using genomic data from a surgical experiment and illustrate how network structure is associated with surgical response to infrainguinal vein bypass grafting and the outcome of grafting. idopNetworks may shed light on genotype-phenotype relationships and provide valuable information for personalized medicine.

关键词： programming language Statistics

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Identification of Cell Types from Single-Cell Transcriptomic Data

Identification of Cell Types from Single-Cell Transcriptomic...

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作者： Karthik Shekhar Vilas Menon

Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the “cell types” that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field. less

关键词： Single-cell RNA-seq Machine Learning Statistical Calculation Single Nucleus RNA-Seq Seurat programming language Clustering Transcriptomic classification Cell-type identification Cell taxonomy Cross-species comparison of cell-types

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The Role of Logic programming in ICT Specialists Curricula 28

The Role of Logic Programming in ICT Specialists Curricula

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28th EAEEIE Annual Conference (EAEEIE)

作者： Hynek, Josef Univ Hradec Kralove Fac Informat & Management Hradec Kralove Czech Republic

ISBN: (纸本)9781538677117

Prolog was a modern, promising and a very popular programming language thirty years ago and Logic programming acquired a firm place within Computer Science as well as in ICT programs curricula especially at European and Asian Universities. However, the rapid development of new programming languages moved Prolog far away from the beginning of the list of the most widely utilized programming languages. Therefore the logical question is whether there are still some good and justified reasons to keep Logic programming in the present ICT specialists curricula. This paper outlines some general reasons for maintaining it on the list of the subjects taught and it brings out some basic examples showing the usefulness of the logic programming paradigm especially when explaining and teaching the efficient utilization of recursion.

关键词： programming language logic programing PROLOG ICT curricula recursion

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CRM Discovery Beyond Model Insects

CRM Discovery Beyond Model Insects

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作者： Majid Kazemian Marc S. Halfon

Although the number of sequenced insect genomes numbers in the hundreds, little is known about gene regulatory sequences in any species other than the well-studied Drosophila melanogaster. We provide here a detailed protocol for using SCRMshaw, a computational method for predicting cis-regulatory modules (CRMs, also “enhancers”) in sequenced insect genomes. SCRMshaw is effective for CRM discovery throughout the range of holometabolous insects and potentially in even more diverged species, with true-positive prediction rates of 75% or better. Minimal requirements for using SCRMshaw are a genome sequence and training data in the form of known Drosophila CRMs; a comprehensive set of the latter can be obtained from the SCRMshaw download site. For basic applications, a user with only modest computational know-how can run SCRMshaw on a desktop computer. SCRMshaw can be run with a single, narrow set of training data to predict CRMs regulating a specific pattern of gene expression, or with multiple sets of training data covering a broad range of CRM activities to provide an initial rough regulatory annotation of a complete, newly-sequenced genome. less

关键词： Genome Annotation Genomics Genome Visualization Genome Assembly Statistical Calculation bedtools programming language Drosophila melanogaster Bombyx mori Drosophila suzukii Enhancer prediction Non-model insects Transcriptional gene regulation

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Light-Induced Transcription Activation for Time-Lapse Microscopy Experiments in Living Cells

Light-Induced Transcription Activation for Time-Lapse Micros...

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作者： Jorge Trojanowski Anne Rademacher Fabian Erdel Karsten Rippe

Gene expression can be monitored in living cells via the binding of fluorescently tagged proteins to RNA repeats engineered into a reporter transcript. This approach makes it possible to trace temporal changes of RNA production in real time in living cells to dissect transcription regulation. For a mechanistic analysis of the underlying activation process, it is essential to induce gene expression with high accuracy. Here, we describe how this can be accomplished with an optogenetic approach termed blue light-induced chromatin recruitment (BLInCR). It employs the recruitment of an activator protein to a target promoter via the interaction between the PHR and CIBN plant protein domains. This process occurs within seconds after setting the light trigger and is reversible. Protocols for continuous activation as well as pulsed activation and reactivation with imaging either by laser scanning confocal microscopy or automated widefield microscopy are provided. For the semiautomated quantification of the resulting image series, an approach has been implemented in a set of scripts in the R programming language. Thus, the complete workflow of the BLInCR method is described for mechanistic studies of the transcription activation process as well as the persistence and memory of the activated state. less

关键词： Optogenetics Cell And Tissue Culture Image Analysis Microscopy Time Lapse Microscopy Confocal Microscopy Transfection Fiji ImageJ programming language Image quantification Transcription dynamics

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Multi-Level Modeling and Simulation of Cellular Systems: An Introduction to ML-Rules

Multi-Level Modeling and Simulation of Cellular Systems: An ...

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作者： Tobias Helms Tom Warnke Adelinde M. Uhrmacher

ML-Rules is a rule-based language for multi-level modeling and simulation. ML-Rules supports dynamic nesting of entities and applying arbitrary functions on entity attributes and content, as well as for defining kinetics of reactions. This allows describing and simulating complex cellular dynamics operating at different organizational levels, e.g., to combine intra-, inter-, and cellular dynamics, like the proliferation of cells, or to include compartmental dynamics like merging and splitting of mitochondria or endocytosis. The expressiveness of the language is bought with additional efforts in executing ML-Rules models. Therefore, various simulators have been developed from which the user and automatic procedures can select. The experiment specification language SESSL facilitates design, execution, and reuse of simulation experiments. The chapter illuminates the specific features of ML-Rules as a rule-based modeling language, the implications for an efficient execution, and shows ML-Rules at work. less

关键词： Stochastic Simulation Modeling And Simulation Statistical Calculation Simulation Experiment programming language Computational biology Rule-based modeling Multi-level modeling Cell biological systems Experiment specification

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MACC: An OpenACC Transpiler for Automatic Multi-GPU Use 4th

MACC: An OpenACC Transpiler for Automatic Multi-GPU Use

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4th Asian Conference on Supercomputing Frontiers (SCFA)

作者： Matsumura, Kazuaki Sato, Mitsuhisa Boku, Taisuke Podobas, Artur Matsuoka, Satoshi Tokyo Inst Technol Tokyo Japan AIST Tokyo Tech Real World Big Data Computat Ope Tokyo Japan RIKEN Adv Inst Computat Sci Kobe Hyogo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9783319699530;9783319699523

Graphics Processing Units (GPUs) perform the majority of computations in state-of-the-art supercomputers. programming these GPUs is often assisted using a programming model such as (amongst others) the directive-driven OpenACC. Unfortunately, OpenACC (and other similar models) are incapable of automatically targeting and distributing work across several GPUs, which decreases productivity and forces needless manual labor upon programmers. We propose a method that enables OpenACC applications to target multi-GPU. Workload distribution, data transfer and inter-GPU communication (including modern GPU-to-GPU links) are automatically and transparently handled by our compiler with no user intervention and no changes to the program code. Our method leverages existing OpenMP and OpenACC backends, ensuring easy integration into existing HPC infrastructure. Empirically we quantify performance gains and losses in our data coherence method compared to similar approaches, and also show that our approach can compete with the performance of hand-written MPI code.

关键词： programming language Compiler Multi-GPU

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Cluster Analysis of Untargeted Metabolomic Experiments

Cluster Analysis of Untargeted Metabolomic Experiments

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作者： Joshua Heinemann

Untargeted metabolite profiling based upon LC-MS methodology can be used to identify unique metabolic phenotypes associated with stress, disease or environmental exposure of cells using mathematical clustering. Here, we show how unsupervised data analysis is a powerful tool for both quality control and answering simple biological questions. We will demonstrate how to format untargeted mass spectrometry data for import into R, a programming language and software environment for statistical computing (R Development Core Team. R: A language and environment for statistical computing, reference index version 2.15. R Foundation for Statistical Computing, Vienna, 2012). Using R, we transform untargeted metabolite data using hierarchical clustering and principal component analysis (PCA) to create visual representations of change between biological samples and explore how these can be used predictively, in determining environmental stress, health and metabolic insight. less

关键词： Data Mining Metabolomics Liquid Chromatography-mass Spectrometry Mass Spectrometry Principal Component Analysis Data analysis programming language Cluster analysis Phenotyping Clustering Pattern recognition

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Mapping the

Mapping the

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作者： Daniel J. Nightingale Stephen G. Oliver Kathryn S. Lilley

The subcellular localization of proteins is a posttranslational modification of paramount importance. The ability to study subcellular and organelle proteomes improves our understanding of cellular homeostasis and cellular dynamics. In this chapter, we describe a protocol for the unbiased and high-throughput study of protein subcellular localization in the yeast Saccharomyces cerevisiae: hyperplexed localization of organelle proteins by isotope tagging (hyperLOPIT), which involves biochemical fractionation of Saccharomyces cerevisiae and high resolution mass spectrometry-based protein quantitation using TMT 10-plex isobaric tags. This protocol enables the determination of the subcellular localizations of thousands of proteins in parallel in a single experiment and thereby deep sampling and high-resolution mapping of the spatial proteome. less

关键词： Subcellular Fractionation Cell And Tissue Culture Solid-phase Extraction (SPE) Mass Spectrometry Localization Of Organelle Proteins By Isotope Tagging Fractionation Isobaric Mass Labeling Sonication BCA assay Western Blot Chromatography hyperLOPIT Spatial proteomics programming language Saccharomyces cerevisiae Protein localization Organelle Anti-Cox2p

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