scholarly journals The Mysterious Case of the Delayed Twin: using research data to resolve linkage questions.

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Daniel Avery
Keyword(s):  
Machine Learning ◽  
Administrative Data ◽  
Research Data ◽  
Statistical Techniques ◽  
Questionnaire Data ◽  
A Genome ◽  
Familial Relationship ◽  
Form Part

IntroductionIn a large biobank of over half a million people, we have several pairs of participants who appear to share their genome. As more individuals are sequenced, more pairs are likely to be found. If these are twins then this is great news, but it isn’t quite that simple. Objectives and ApproachWhere 2 people share a genome we need to be able to confirm that these pairs are twins. However, there are a number of issues which could cause 2 people to appear to share a genome; for example being recruited twice, donating blood on another’s behalf, etc. We already identify and exclude participant data based on these conditions. We developed our methodology by looking at the first identified pair in great detail, looking for evidence which specifically ruled out possible alternate explanations, and then applying and refining the method on later pairs. ResultsWe were able to demonstrate the pair were almost certainly twins using their biochemistry and family questionnaire data as principal sources. We also identified a number of variables which were useful in indicating the likelihood of a twin, and now form part of a methodology which we are still developing. Even more usefully, we identified a number of variables that seemed like useful measures but proved extremely misleading. To date we have 26 pairs of possible twins, with 9 confirmed as twins and the remainder looking likely to be twins but falling short of a threshold for confidence. We also have 75 pairs which confirm duplicate participants we have already excluded. Conclusion/ImplicationsWe formed two lessons: even very simply linkages come with pitfalls, and you should gather more administrative data than you think. We’re proposing the collection of additional familial relationship data in our third resurvey. We are also looking into machine learning and statistical techniques to better identify twins and duplicates.

Analysis of agricultural crop yield prediction using statistical techniques of machine learning

2021 ◽  
Author(s):  
Janmejay Pant ◽  
R.P. Pant ◽  
Manoj Kumar Singh ◽  
Devesh Pratap Singh ◽  
Himanshu Pant
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Statistical Techniques ◽  
Yield Prediction ◽  
Agricultural Crop

scholarly journals Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 241
Author(s):  
Dongwon Seo ◽  
Sunghyun Cho ◽  
Prabuddha Manjula ◽  
Nuri Choi ◽  
Young-Kuk Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Fixation Index ◽  
Machine Learning Classification ◽  
Genetic Components ◽  
Marker Combination ◽  
A Genome ◽  
Minimum Number ◽  
Native Chickens

A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

scholarly journals The Open Data Challenge: An Analysis of 124,000 Data Availability Statements and an Ironic Lesson about Data Management Plans

2020 ◽  
Vol 2 (4) ◽  
pp. 554-568
Author(s):  
Chris Graf ◽  
Dave Flanagan ◽  
Lisa Wylie ◽  
Deirdre Silver
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Open Data ◽  
Research Data ◽  
Data Availability ◽  
Policy Changes ◽  
Management Plans ◽  
Trends Over Time ◽  
New Research ◽  
Over Time

Data availability statements can provide useful information about how researchers actually share research data. We used unsupervised machine learning to analyze 124,000 data availability statements submitted by research authors to 176 Wiley journals between 2013 and 2019. We categorized the data availability statements, and looked at trends over time. We found expected increases in the number of data availability statements submitted over time, and marked increases that correlate with policy changes made by journals. Our open data challenge becomes to use what we have learned to present researchers with relevant and easy options that help them to share and make an impact with new research data.

scholarly journals Machine-learning annotation of human splicing branchpoints

2016 ◽  
Author(s):  
Bethany Signal ◽  
Brian S Gloss ◽  
Marcel E Dinger ◽  
Timothy R Mercer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Gene Splicing ◽  
Genetic Encoding ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
A Genome ◽  
Wide Scale ◽  
The Impact ◽  
Splicing Patterns

ABSTRACTBackgroundThe branchpoint element is required for the first lariat-forming reaction in splicing. However due to difficulty in experimentally mapping at a genome-wide scale, current catalogues are incomplete.ResultsWe have developed a machine-learning algorithm trained with empirical human branchpoint annotations to identify branchpoint elements from primary genome sequence alone. Using this approach, we can accurately locate branchpoints elements in 85% of introns in current gene annotations. Consistent with branchpoints as basal genetic elements, we find our annotation is unbiased towards gene type and expression levels. A major fraction of introns was found to encode multiple branchpoints raising the prospect that mutational redundancy is encoded in key genes. We also confirmed all deleterious branchpoint mutations annotated in clinical variant databases, and further identified thousands of clinical and common genetic variants with similar predicted effects.ConclusionsWe propose the broad annotation of branchpoints constitutes a valuable resource for further investigations into the genetic encoding of splicing patterns, and interpreting the impact of common- and disease-causing human genetic variation on gene splicing.

scholarly journals Russian Regional Politicians’ Income and Property Declarations: a Pilot Study and Quality Assessment of Administrative Data

2021 ◽  
Vol 27 (3) ◽  
pp. 8-34
Author(s):  
Tatyana Cherkashina
Keyword(s):  
Pilot Study ◽  
Data Quality ◽  
Quality Assessment ◽  
Administrative Data ◽  
Measurement Errors ◽  
Private Property ◽  
Research Data ◽  
Quality Of Data ◽  
Data Set ◽  
Research Problems

The article presents the experience of converting non-targeted administrative data into research data, using as an example data on the income and property of deputies from local legislative bodies of the Russian Federation for 2019, collected as part of anticorruption operations. This particular empirical fragment was selected for the pilot study of administrative data, which includes assessing the possibility of integrating scattered fragments of information into a single database, assessing quality of data and their relevance for solving research problems, particularly analysis of high-income strata and the apparent trends towards individualization of private property. The system of indicators for assessing data quality includes their timeliness, availability, interpretability, reliability, comparability, coherence, errors of representation and measurement, and relevance. In the case of the data set in question, measurement errors are more common than representation errors. Overall the article emphasizes the notion that introducing new non-target data into circulation requires their preliminary testing, while data quality assessment becomes distributed both in time and between different subjects. The transition from created data to «obtained» data shifts the functions of evaluating its quality from the researcher-creator to the researcheruser. And though in this case data quality is in part ensured by the legal support for their production, the transformation of administrative data into research data involves assessing a variety of quality measurements — from availability to uniformity and accuracy.

Identification of Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

2020 ◽  
Author(s):  
Dongwon Seo ◽  
Sunghyun Cho ◽  
Prabuddha Manjula ◽  
Nuri Choi ◽  
Young Kuk Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Snp Array ◽  
Machine Learning Algorithms ◽  
Case Group ◽  
Machine Learning Classification ◽  
Genetic Components ◽  
Native Chicken ◽  
Marker Combination ◽  
A Genome ◽  
Minimum Number

Abstract BackgroundA marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would also facilitate the protection of genetic resources, especially in developing countries. MethodsIn this study, a total of 20 lines 283 samples which were consist of Korean native chicken, commercial native chicken, and commercial broilers with layer population were used for finding the minimum number of marker combinations through the 600k high-density single nucleotide polymorphism (SNP) array. Application of the machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group from control chicken groups. In the verification of the selected markers, a total of 12 lines 182 samples were used to confirm the change in the accuracy of the target chicken breed identification.ResultsA total of 47,303 SNPs was used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by Adaboost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0% and 97.9%, respectively. The selected marker combinations increased the genetic distance between the case and control groups, and reduced the number of genetic components, confirming that an efficient classification of the groups was possible using small number of marker sets. In a verification study including additional chicken breeds and samples, the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations.ConclusionsThe GWAS and PCA analysis, machine learning algorithm used in this study is able to be applied efficiently to explore the minimum combination of markers that can distinguish varieties among a large number of SNP markers.

scholarly journals In the News

2020 ◽  
Vol 81 (6) ◽  
pp. 265
Author(s):  
David Free
Keyword(s):  
Machine Learning ◽  
Social Justice ◽  
Open Access ◽  
Learning Analytics ◽  
Streaming Media ◽  
Research Data ◽  
Review Committee ◽  
Data Services ◽  
Systems Learning ◽  
Digital Pedagogy

Welcome to the June 2020 issue of C&RL News. Every two years, ACRL’s Research Planning and Review Committee produces their “Top trends in academic libraries.” The 2020 edition discusses change management; evolving integrated library systems; learning analytics; machine learning and AI; the state of open access and research data services; social justice, critical librarianship, and critical digital pedagogy; streaming media; and student wellbeing. Many thanks to the committee for pulling together this important survey of the current landscape of academic and research librarianship.

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