scholarly journals Identification of Recurrent Genetic Patterns From Targeted Sequencing Panels With Advanced Data Science: A Case-Study On Sporadic And Genetic Neurodegenerative Diseases

2021 ◽  
Author(s):  
Martina Tarozzi ◽  
Anna Bartoletti-Stella ◽  
Daniele Dall'Olio ◽  
Tommaso Matteuzzi ◽  
Simone Baiardi ◽  
...  
Keyword(s):  
Genetic Information ◽  
Binary Data ◽  
Data Science ◽  
Disease Risk ◽  
Genetic Profile ◽  
Predictive Tool ◽  
Target Sequencing ◽  
Network Analyses ◽  
Starting Point ◽  
Genetic Patterns

Abstract BACKGROUND: Targeted Next Generation Sequencing is a common and powerful approach used in both clinical and research settings. However, at present, a large fraction of the acquired genetic information is not used since pathogenicity cannot be assessed for most variants. Further complicating this scenario is the increasingly frequent description of a poli/oligogenic pattern of inheritance showing the contribution of multiple variants in increasing disease risk. We present an approach in which the entire genetic information provided by target sequencing is transformed into binary data on which we performed statistical, machine learning, and network analyses to extract all valuable information from the entire genetic profile. To test this approach and unbiasedly explore the presence of recurrent genetic patterns, we studied a cohort of 112 patients affected either by genetic Creutzfeldt-Jakob (CJD) disease caused by two mutations in the PRNP gene (p.E200K and p.V210I) with different penetrance or by sporadic Alzheimer disease (sAD).RESULTS: Unsupervised methods can identify functionally relevant sources of variation in the data, like haplogroups and polymorphisms that do not follow Hardy-Weinberg equilibrium, such as the NOTCH3 rs11670823 (c.3837+21T>A). Supervised classifiers can recognize clinical phenotypes with high accuracy based on the mutational profile of patients. In addition, we found a similar alteration of allele frequencies compared the European population in sporadic patients and in V210I-CJD, a poorly penetrant PRNP mutation, and sAD, suggesting shared oligogenic patterns in different types of dementia. Pathway enrichment and protein-protein interaction network revealed different altered pathways between the two PRNP mutations.CONCLUSIONS: We propose this workflow as a possible approach to gain deeper insights into the genetic information derived from target sequencing, to identify recurrent genetic patterns and improve the understanding of complex diseases. This work could also represent a possible starting point of a predictive tool for personalized medicine and advanced diagnostic applications.

scholarly journals Fifty years of digital literacy studies: A meta-research for interdisciplinary and conceptual convergence

2020 ◽  
Author(s):  
María-Cristina Martínez-Bravo ◽  
Charo Sádaba-Chalezquer ◽  
Javier Serrano-Puche
Keyword(s):  
Digital Literacy ◽  
Data Science ◽  
Human Life ◽  
Literacy Studies ◽  
Starting Point ◽  
Systematized Review ◽  
Key Terms ◽  
Different Levels ◽  
Central Term ◽  
Previous Existence

The following research has as its starting point the previous existence of different approaches to the study of digital literacy, which reflect a specialisation by area of study as well as connections and complementarity between them. The paper analyses research from the last 50 years through 11 key terms associated with the study of this subject. The article seeks to understand the contribution of each term for an integrated conceptualisation of digital literacy. From the data science approach, the methodology used is based on a systematized review of the literature and a network analysis using Gephi. The study analyses 16,753 articles from WoS and 5,809 from Scopus, between the period of 1968 to 2017. The results present the input to each key term studied as a map of keywords and a conceptual framework in different levels of analysis; in these, we show digital literacy as a central term that connects and integrates the others, and we define it as a process that integrates all the perspectives. The conclusions emphasise the comprehensive sense of digital literacy and its social condition, as well as the transversality to human life. This research aims to understand the relationships that exist between the different areas and contribute to the debate from a meta-theoretical level, validating meta-research for this interdisciplinary purpose.

scholarly journals Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 12 ◽  
Author(s):  
Stéphanie Boué ◽  
Thomas Exner ◽  
Samik Ghosh ◽  
Vincenzo Belcastro ◽  
Joh Dokler ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Science ◽  
Disease Risk ◽  
Open Data ◽  
Supporting Evidence ◽  
Tobacco Products ◽  
Us Fda ◽  
Using Data

The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products.  Establishing a product’s potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking.  Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website (INTERVALS) has been developed to share results from both in vivo inhalation studies and in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.

scholarly journals Post–Modern Epidemiology: When Methods Meet Matter

2019 ◽  
Vol 188 (8) ◽  
pp. 1410-1419 ◽  
Author(s):  
George Davey Smith
Keyword(s):  
Causal Inference ◽  
20Th Century ◽  
Data Science ◽  
Disease Risk ◽  
Epidemiologic Studies ◽  
High Income ◽  
Noncommunicable Diseases ◽  
Primary Concern ◽  
Disease Etiology ◽  
Major Progress

Abstract In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post–Modern Epidemiology world.

scholarly journals From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

2020 ◽  
Vol 10 (1) ◽  
pp. 15 ◽  
Author(s):  
Enrico Capobianco ◽  
Marco Dominietto
Keyword(s):  
Data Science ◽  
Response To Therapy ◽  
Data Types ◽  
Clinical Value ◽  
Multiple Data ◽  
Starting Point ◽  
Personal Histories ◽  
Treatment Plans ◽  
Precision Health

Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.

Genomics, Insurance and Human Rights: Is there a Place for Regulatory Frameworks in Africa?

2006 ◽  
Vol 2 (1) ◽  
pp. 20-34
Author(s):  
Vincent O. Nmehielle
Keyword(s):  
Human Rights ◽  
Health Insurance ◽  
Genetic Information ◽  
Insurance Industry ◽  
Genetic Discrimination ◽  
Insurance Companies ◽  
Genetic Profile ◽  
Regulatory Frameworks ◽  
Health Care Context ◽  
Insurance Decisions

AbstractThis article examines the human rights dimension of genetic discrimination in Africa, exploring the place of regulatory frameworks while taking into account the disadvantaged position of the average African. This is in response to the tendency of insurance companies toward making health insurance decisions on the basis of individual genetic information, which could result in genetic discrimination or health insurance discrimination based on a person's genetic profile. The author considers such questions as the intersection between human rights (right to life, health, privacy, human dignity and against genetic discrimination) in relation to the insurance industry, as well as the obligations of state and non-state actors to promote, respect, and protect the enjoyment of these rights. The article argues that African nations should not stand aloof in trying to balance the competing interests (scientific, economic and social) presented by the use of genetic information in the health care context and that ultimately it is the responsibility of states to develop domestic policies to protect their most vulnerable citizens and to prevent entrenched private discrimination based on an individual's genes.

scholarly journals The genetic contribution to disease risk and variability in response to diet: where is the hidden heritability?

2012 ◽  
Vol 72 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Anne Marie Minihane
Keyword(s):  
Genetic Information ◽  
Disease Risk ◽  
Health Benefit ◽  
Genetic Knowledge ◽  
Genetic Contribution ◽  
Public Health Benefit ◽  
Common Genetic Variants ◽  
Technological Developments ◽  
Response To Environmental Change ◽  
The Impact

Ten years ago, it was assumed that disease risk prediction and personalised nutrition based on genetic information would now be in widespread use. However, this has not (yet) transpired. The interaction of genetic make-up, diet and health is far more complex and subtle than originally thought. With a few notable exceptions, the impact of identified common genetic variants on phenotype is relatively small and variable in their penetrance. Furthermore, the known variants account for only a fraction of what we believe to be the total genetic contribution to disease risk and heterogeneity in response to environmental change. Here, the question ‘how far have we progressed and are we likely to get there’ (Rimbach and Minihane, 2009) is revisited with regard to the translation of genetic knowledge into public health benefit. It is concluded that progress to date has been modest. It is hoped that recent technological developments allowing the detection of rarer variants and future use of more hypothesis-driven targeted data analysis will reveal most of the currently ‘hidden’ significant genetic variability.

scholarly journals Accurate genetic profiling of anthropometric traits using a big data approach

2015 ◽  
Author(s):  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
John A. Woolliams ◽  
Albert Tenesa
Keyword(s):  
Disease Management ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Genomic Medicine ◽  
Full Potential ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Starting Point ◽  
The Individual

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations, which used more than twice as many training samples. Across-population predictions in White non-British individuals were similar to those of White-British whilst those in Asian and Black individuals were informative but less accurate. The genotyping of circa 500,000 UK Biobank participants will yield predictions ranging between 66% and 83% of the maximum. We anticipate that our models and a common panel of genetic markers, which can be used across multiple traits and diseases, will be the starting point to tailor disease management to the individual. Ultimately, we will be able to capitalise on whole-genome sequence and environmental risk factors to realise the full potential of genomic medicine.

scholarly journals Toxicological evaluation of the plant products using Brine Shrimp (Artemia salina L.) model

2014 ◽  
Vol 60 (01) ◽  
pp. 9-18 ◽  
Author(s):  
Mentor R. Hamidi ◽  
Blagica Jovanova ◽  
Tatjana Kadifkova Panovska
Keyword(s):  
Plant Extracts ◽  
Brine Shrimp ◽  
Biological Activities ◽  
Artemia Salina ◽  
Herbal Extract ◽  
Predictive Tool ◽  
Brine Shrimp Lethality ◽  
Plant Products ◽  
Brine Shrimp Lethality Assay ◽  
Starting Point

Many natural products could serve as the starting point in the development of modern medicines because of their numerous biological and pharmacological activities. However, some of them are known to carry toxicological properties as well. In order to achieve a safe treatment with plant products, numerous research studies have recently been focused on both pharmacology and toxicity of medicinal plants. Moreover, these studies employed efforts for alternative biological assays. Brine Shrimp Lethality Assay is the most convenient system for monitoring biological activities of various plant species. This method is very useful for preliminary assessment of toxicity of the plant extracts. Rapidness, simplicity and low requirements are several advantages of this assay. However, several conditions need to be completed, especially in the means of standardized experimental conditions (temperature, pH of the medium, salinity, aeration and light). The toxicity of herbal extracts using this assay has been determined in a concentration range of 10, 100 and 1000 µg/ml of the examined herbal extract. Most toxicity studies which use the Brine Shrimp Lethality Assay determine the toxicity after 24 hours of exposure to the tested sample. The median lethal concentration (LC50) of the test samples is obtained by a plot of percentage of the dead shrimps against the logarithm of the sample concentration. LC50 values are estimated using a probit regression analysis and compared with either Meyer’s or Clarkson’s toxicity criteria. Furthermore, the positive correlation between Meyer’s toxicity scale for Artemia salina and Gosselin, Smith and Hodge’s toxicity scale for higher animal models confirmed that the Brine Shrimp Lethality Assay is an excellent predictive tool for the toxic potential of plant extracts in humans.

scholarly journals Dietary, Cultural and Pathogens-related Selective Pressures Shaped Differential Adaptive Evolution Among Native Mexican Populations

2021 ◽  
Author(s):  
Claudia Ojeda-Granados ◽  
Paolo Abondio ◽  
Alice Setti ◽  
Stefania Sarno ◽  
Guido Alberto Gnecchi-Ruscone ◽  
...  
Keyword(s):  
Native American ◽  
Adaptive Evolution ◽  
Disease Risk ◽  
Biological Traits ◽  
Network Analyses ◽  
Selective Pressures ◽  
Genomic Signatures ◽  
Genetic Components ◽  
Increased Risk ◽  
Mexican Populations

Native American genetic ancestry has been remarkably implicated with increased risk of diverse health issues in several Mexican populations, especially in relation to the dramatic changes in environmental, dietary and cultural settings they have recently undergone. In particular, the effects of these ecological transitions and Westernization of lifestyles have been investigated so far predominantly on Admixed individuals. Nevertheless, indigenous groups, rather than admixed Mexicans, have plausibly retained the highest proportions of genetic components shaped by natural selection in response to the ancient milieu experienced by Mexican ancestors during their pre-Columbian evolutionary history. These formerly adaptive alleles/haplotypes have the potential to represent the genetic determinants of some biological traits peculiar to the Mexican people and a reservoir of loci with potential biomedical relevance. To test such a hypothesis, we used high-resolution genomic data to infer the unique adaptive evolution of 15 Native Mexican groups selected as reasonable descendants of the main pre-Columbian Mexican civilizations. A combination of haplotype-based and gene-network analyses enabled us to detect genomic signatures ascribable to polygenic adaptive traits evolved by the main genetic clusters of indigenous Mexican populations to cope with local environmental and/or cultural conditions. Some of them were also found to play a role in modulating the susceptibility/resistance of these groups to certain pathological conditions, thus providing new evidence for diverse selective pressures having contributed to shape current biological and disease-risk patterns in present-day Native and Mestizo Mexican populations.

Technology Focus: Completions (April 2021)

2021 ◽  
Vol 73 (04) ◽  
pp. 41-41
Author(s):  
Doug Lehr
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Science ◽  
Detection System ◽  
Streaming Data ◽  
Predictive Tool ◽  
Well Completion ◽  
Ensemble Machine Learning ◽  
Casing Damage ◽  
Technology Focus

In the 2020 Completions Technology Focus, I stated that digitization will forever change how the most complex problems in our industry are solved. And, despite another severe downturn in the upstream industry, data science continues to provide solutions for complex unconventional well problems. Casing Damage Casing collapse is an ongoing problem and almost always occurs in the heel of the well. It prevents passage of frac plugs and milling tools. Forcing a frac plug through the collapsed section damages the plug, predisposing it to failure, which leads to more casing damage and poor stimulation. One team has developed a machine-learning (ML) model showing a positive correlation between zones with high fracturing gradients and collapsed casing. The objective is a predictive tool that enables a completion design that avoids these zones. Fracture-Driven Interactions (FDIs) Can Be Avoided in Real Time Pressurized fracturing fluids from one well can communicate with fractures in a nearby well or can intersect that well-bore. Such FDIs can occur while fracturing a child well and can negatively affect production in the parent well. FDIs are caused by well spacing, depletion, or completion design but, until recently, were not quickly diagnosed. Analytics and machine learning now are being used to analyze streaming data sets during a frac job to detect FDIs. A recently piloted detection system alerts the operator in real time, which enables avoidance of FDIs on the fly. Data Science Provides the Tools Analyzing casing damage and FDIs is a complex task involving large amounts of data already available or easily acquired. Tools such as ML perform the data analysis and enable decision making. Data science is enabling the unconventional “onion” to be peeled many layers at a time. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199967 - Artificial Intelligence for Real-Time Monitoring of Fracture-Driven Interactions and Simultaneous Completion Optimization by Hayley Stephenson, Baker Hughes, et al. SPE 201615 - Novel Completion Design To Bypass Damage and Increase Reservoir Contact: A Middle Magdalena, Central Colombian Case History by Rosana Polo, Oxy, et al. SPE 202966 - Well Completion Optimization in Canada Tight Gas Fields Using Ensemble Machine Learning by Lulu Liao, Sinopec, et al.

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