scholarly journals WisecondorFF: Improved Fetal Aneuploidy Detection from Shallow WGS through Fragment Length Analysis

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 59
Author(s):  
Tom Mokveld ◽  
Zaid Al-Ars ◽  
Erik A. Sistermans ◽  
Marcel Reinders
Keyword(s):  
Fragment Length ◽  
Fragment Size ◽  
Data Sources ◽  
Prenatal Diagnostics ◽  
Size Distributions ◽  
Read Coverage ◽  
Data Types ◽  
Fetal Aneuploidy ◽  
Reference Set ◽  
Length Analysis

In prenatal diagnostics, NIPT screening utilizing read coverage-based profiles obtained from shallow WGS data is routinely used to detect fetal CNVs. From this same data, fragment size distributions of fetal and maternal DNA fragments can be derived, which are known to be different, and often used to infer fetal fractions. We argue that the fragment size has the potential to aid in the detection of CNVs. By integrating, in parallel, fragment size and read coverage in a within-sample normalization approach, it is possible to construct a reference set encompassing both data types. This reference then allows the detection of CNVs within queried samples, utilizing both data sources. We present a new methodology, WisecondorFF, which improves sensitivity, while maintaining specificity, relative to existing approaches. WisecondorFF increases robustness of detected CNVs, and can reliably detect even at lower fetal fractions (<2%).

Analysis of fragment size distributions in collisions of monocharged ions with the C60molecule

2005 ◽  
Vol 38 (7) ◽  
pp. 789-806 ◽  
Author(s):  
A Rentenier ◽  
P Moretto-Capelle ◽  
D Bordenave-Montesquieu ◽  
A Bordenave-Montesquieu
Keyword(s):  
Fragment Size ◽  
Size Distributions

Models and Data Sources Used in Systems Medicine

2016 ◽  
Vol 55 (02) ◽  
pp. 107-113 ◽  
Author(s):  
M. Löpprich ◽  
C. Karmen ◽  
M. Ganzinger ◽  
M. Gietzelt
Keyword(s):  
Treatment Strategies ◽  
Computer Applications ◽  
Data Sources ◽  
Search Term ◽  
Systems Medicine ◽  
Data Types ◽  
Research Activities ◽  
Meta Analyses ◽  
Modelling Methods ◽  
Modelling Approaches

SummaryBackground: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.

scholarly journals PENGARUH PENERAPAN E-SPT TAHUNAN BADAN, TINGKAT PEMAHAMAN PERPAJAKAN DAN KESADARAN WAJIB PAJAK TERHADAP KEPATUHAN WAJIB PAJAK BADAN DALAM MELAPORKAN SPT TAHUNAN BADAN

2021 ◽  
Vol 2 (2) ◽  
pp. 95-103
Author(s):  
Trie Nadia Ayu Lizara ◽  
Timbul Simangunsong
Keyword(s):  
Quantitative Data ◽  
Sampling Method ◽  
Data Sources ◽  
Primary Data ◽  
Data Types ◽  
The West ◽  
Purposive Sampling

The Influence of the Implementation of the Annual Entity E-SPT, Understanding Taxation, Tax Awareness Awareness of Taxpayer Compliance of the corporate taxpayers in reporting as the agency annual. In this study using quantitative data types and data sources, namely primary data. Primary data were obtained from questionnaires distributed to corporate taxpayers at random, using the purposive sampling method at the West Jakarta Middle Tax Office. The number of questionnaires distributed was 100 questionnaires. The results of this study indicate that the Application of the Annual Annual E-SPT of the Agency has a significant effect on taxpayer compliance and taxpayer awareness. While Understanding Taxation has no significant effect on Taxpayer Compliance.

Fragment size distributions in brittle deformed rocks.

2021 ◽  
pp. 104496
Author(s):  
Alison Ord ◽  
Thomas Blenkinsop ◽  
Bruce Hobbs
Keyword(s):  
Fragment Size ◽  
Size Distributions

Big Data Architecture Components

2018 ◽  
pp. 10-31
Keyword(s):  
Big Data ◽  
Missing Values ◽  
Noise Removal ◽  
Data Sources ◽  
Data Types ◽  
Layer Analysis ◽  
External Sources ◽  
Internal Sources ◽  
High Level

The previous chapter overviewed big data including its types, sources, analytic techniques, and applications. This chapter briefly discusses the architecture components dealing with the huge volume of data. The complexity of big data types defines a logical architecture with layers and high-level components to obtain a big data solution that includes data sources with the relation to atomic patterns. The dimensions of the approach include volume, variety, velocity, veracity, and governance. The diverse layers of the architecture are big data sources, data massaging and store layer, analysis layer, and consumption layer. Big data sources are data collected from various sources to perform analytics by data scientists. Data can be from internal and external sources. Internal sources comprise transactional data, device sensors, business documents, internal files, etc. External sources can be from social network profiles, geographical data, data stores, etc. Data massage is the process of extracting data by preprocessing like removal of missing values, dimensionality reduction, and noise removal to attain a useful format to be stored. Analysis layer is to provide insight with preferred analytics techniques and tools. The analytics methods, issues to be considered, requirements, and tools are widely mentioned. Consumption layer being the result of business insight can be outsourced to sources like retail marketing, public sector, financial body, and media. Finally, a case study of architectural drivers is applied on a retail industry application and its challenges and usecases are discussed.

scholarly journals Evolution of fragment size distributions from the crushing of granular materials

2019 ◽  
Vol 99 (1) ◽  
Author(s):  
Pavel S. Iliev ◽  
Falk K. Wittel ◽  
Hans J. Herrmann
Keyword(s):  
Granular Materials ◽  
Fragment Size ◽  
Size Distributions

scholarly journals Strategi Pengembangan Pantai Melasti sebagai Daya Tarik Wisata di Desa Ungasan, Kecamatan Kuta Selatan, Kabupaten Badung

2019 ◽  
Vol 7 (1) ◽  
pp. 188
Author(s):  
I Putu Widhi Eka Julyantara ◽  
I Nyoman Sunarta
Keyword(s):  
Development Strategy ◽  
Qualitative Data ◽  
Secondary Data ◽  
Data Sources ◽  
Strategy Development ◽  
Tourist Attraction ◽  
Data Types ◽  
The Government ◽  
Tourism Potential ◽  
The Village

Melasti beach is a tourist attraction of the beach that is just starting to develop. Melasti Beach still in development in the governance by the village of Ungasan and yet the existence of a management system. Melasti beach is located in Ungasan, South Kuta, Badung Regency, has the potentialities are wonderful and interesting to develop. The assumption behind this is based on the chosen research topic "melasti Beach Development Strategy as a tourist attraction in the village of Ungasan, South Kuta, Badung Regency. Data types and data sources that are used i.e. qualitative data, primary and secondary data. Data collection done by way of observation, unstructured interviews, study kepustakan, documentation, research instrument is the guidance interview. The analysis of the data used is descriptive qualitative data analysis with the use of tourism potential and analysis approach to SWOT analysis which clearly sets forth the findings based on the issues examined with accurate data sources. In the results of this research demonstrating that the Melasti Beach has the potential of nature is very beautiful, it also has the potential of culture as well as potential human or artificial to developed, capable of attracting tourists visit. Although still in the development phase is already visited by tourists and foreigners to the archipelago. The conclusions from the results of Shore Development Melasti is need for management systems, many still lack facilities to complement the tourist attraction due to the lack of funds and contributions from the Government is still waiting for him. Keywords: Tourist Attraction, Tourism Potential, Strategy Development

scholarly journals Text mining analysis roadmap (TMAR) for service research

2020 ◽  
Vol 34 (1) ◽  
pp. 30-47 ◽  
Author(s):  
Mohamed Zaki ◽  
Janet R. McColl-Kennedy
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Design Methodology ◽  
Data Sources ◽  
Theory Testing ◽  
Service Research ◽  
Data Types ◽  
Content Type ◽  
Research And Practice ◽  
Mining Methods

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

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