scholarly journals Unsupervised deep learning on biomedical data with BoltzmannMachines.jl

2019 ◽  
Author(s):  
Stefan Lenz ◽  
Moritz Hess ◽  
Harald Binder
Keyword(s):  
Input Data ◽  
Genomic Data ◽  
Small Sample ◽  
Pattern Detection ◽  
Biomedical Data ◽  
Data Types ◽  
Boltzmann Machines ◽  
Link Type ◽  
Unsupervised Deep Learning ◽  
Small Sample Sizes

AbstractDeep Boltzmann machines (DBMs) are models for unsupervised learning in the field of artificial intelligence, promising to be useful for dimensionality reduction and pattern detection in clinical and genomic data. Multimodal and partitioned DBMs alleviate the problem of small sample sizes and make it possible to combine different input data types in one DBM model. We present the package “BoltzmannMachines” for the Julia programming language, which makes this model class available for practical use in working with biomedical data.AvailabilityNotebook with example data: http://github.com/stefan-m-lenz/BMs4BInf2019 Julia package: http://github.com/stefan-m-lenz/BoltzmannMachines.jl

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

Large-Scale Analysis of Genetic and Clinical Patient Data

2018 ◽  
Vol 1 (1) ◽  
pp. 263-274 ◽  
Author(s):  
Marylyn D. Ritchie
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Data Science ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Types ◽  
Phenotypic Data ◽  
Clinical Patient

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

scholarly journals BioJS InterMineTable Component: A BioJS component for displaying data from InterMine compatible webservice endpoints

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 46 ◽  
Author(s):  
Alexis Kalderimis ◽  
Radek Stepan ◽  
Julie Sullivan ◽  
Rachel Lyne ◽  
Michael Lyne ◽  
...  
Keyword(s):  
Genomic Data ◽  
Data Warehouses ◽  
Data Types ◽  
Link Type ◽  
Wide Range ◽  
Flexible Queries

Summary: The InterMineTable component is a reusable JavaScript component as part of the BioJS project. It enables users to embed powerful table-based query facilities in their websites with access to genomic data-warehouses such as http://www.flymine.org, which allow users to perform flexible queries over a wide range of integrated data types.Availability: http://github.com/alexkalderimis/im-tables-biojs; http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.8301.

scholarly journals BioJS InterMine List Analysis: A BioJS component for displaying graphical or statistical analysis of collections of items from InterMine endpoints

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 45 ◽  
Author(s):  
Alexis Kalderimis ◽  
Radek Stepan ◽  
Julie Sullivan ◽  
Rachel Lyne ◽  
Michael Lyne ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Genomic Data ◽  
Data Warehouses ◽  
Data Types ◽  
Link Type ◽  
Wide Range ◽  
Flexible Queries

Summary: The InterMineTable component is a reusable JavaScript component as part of the BioJS project. It enables users to embed powerful table-based query facilities in their websites with access to genomic data-warehouses such as http://www.flymine.org, which allow users to perform flexible queries over a wide range of integrated data types.Availability:  http://github.com/alexkalderimis/im-tables-biojs; http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.8301.

scholarly journals diploS/HIC: an updated approach to classifying selective sweeps

2018 ◽  
Author(s):  
Andrew D. Kern ◽  
Daniel R. Schrider
Keyword(s):  
Machine Learning ◽  
Genomic Data ◽  
Difficult Problem ◽  
Small Sample ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Selective Sweeps ◽  
Machine Learning Approach ◽  
Small Sample Sizes ◽  
Soft Selective Sweeps

AbstractIdentifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective sweeps in genomes on the basis of patterns of genetic variation surrounding a window of the genome. While S/HIC was shown to be both powerful and precise, the utility of S/HIC was limited by the use of phased genomic data as input. In this report we describe a deep learning variant of our method, diploS/HIC, that uses unphased genotypes to accurately classify genomic windows. diploS/HIC is shown to be quite powerful even at moderate to small sample sizes

scholarly journals Obstacles to the reuse of study metadata in ClinicalTrials.gov

2019 ◽  
Author(s):  
Laura Miron ◽  
Rafael S. Gonçalves ◽  
Mark A. Musen
Keyword(s):  
Free Text ◽  
Biomedical Data ◽  
Biomedical Ontologies ◽  
Experimental Protocol ◽  
Data Types ◽  
Eligibility Criteria ◽  
Government Regulations ◽  
Link Type ◽  
Contact Information ◽  
Mesh Terms

AbstractMetadata that are structured using principled schemas and that use terms from ontologies are essential to making biomedical data findable and reusable for downstream analyses. The largest source of metadata that describes the experimental protocol, funding, and scientific leadership of clinical studies is ClinicalTrials.gov. We evaluated whether values in 302,091 trial records adhere to expected data types and use terms from biomedical ontologies, whether records contain fields required by government regulations, and whether structured elements could replace free-text elements. Contact information, outcome measures, and study design are frequently missing or underspecified. Important fields for search, such as condition and intervention, are not restricted to ontologies, and almost half of the conditions are not denoted by MeSH terms, as recommended. Eligibility criteria are stored as semi-structured free text. Enforcing the presence of all required elements, requiring values for certain fields to be drawn from ontologies, and creating a structured eligibility criteria element would improve the reusability of data from ClinicalTrials.gov in systematic reviews, metanalyses, and matching of eligible patients to trials.

scholarly journals Go Get Data (GGD) is a framework that facilitates reproducible access to genomic data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Michael J. Cormier ◽  
Jonathan R. Belyeu ◽  
Brent S. Pedersen ◽  
Joseph Brown ◽  
Johannes Köster ◽  
...  
Keyword(s):  
Data Integration ◽  
Genomic Data ◽  
Data Types ◽  
Multiple Genome ◽  
Link Type ◽  
Wide Range

AbstractThe rapid increase in the amount of genomic data provides researchers with an opportunity to integrate diverse datasets and annotations when addressing a wide range of biological questions. However, genomic datasets are deposited on different platforms and are stored in numerous formats from multiple genome builds, which complicates the task of collecting, annotating, transforming, and integrating data as needed. Here, we developed Go Get Data (GGD) as a fast, reproducible approach to installing standardized data recipes. GGD is available on Github (https://gogetdata.github.io/), is extendable to other data types, and can streamline the complexities typically associated with data integration, saving researchers time and improving research reproducibility.

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

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