scholarly journals Optimality, Accuracy, and Efficiency of an Exact Functional Test

2020 ◽  
Author(s):  
Hien H. Nguyen ◽  
Hua Zhong ◽  
Mingzhou Song
Keyword(s):  
Artificial Intelligence ◽  
Function Versus ◽  
Null Distribution ◽  
R Package ◽  
Functional Dependency ◽  
Functional Test ◽  
Theoretical Justification ◽  
Model Free ◽  
Causal Direction ◽  
Functional Inference

Functional dependency can lead to discoveries of new mechanisms not possible via symmetric association. Most asymmetric methods for causal direction inference are not driven by the function-versus-independence question. A recent exact functional test (EFT) was designed to detect functionally dependent patterns model-free with an exact null distribution. However, the EFT lacked a theoretical justification, had not been compared with other asymmetric methods, and was practically slow. Here, we prove the functional optimality of the EFT statistic, demonstrate its advantage in functional inference accuracy over five other methods, and develop a branch-and-bound algorithm with dynamic and quadratic programming to run at orders of magnitude faster than its previous implementation. Our results make it practical to answer the exact functional dependency question arising from discovery-driven artificial intelligence applications. Software that implements EFT is freely available in the R package 'FunChisq' (≥2.5.0) at https://cran.r-project.org/package=FunChisq

scholarly journals Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 441
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
Belal Alsinglawi ◽  
Li-Fong Lin ◽  
Shuo-Chen Chien ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
English Language ◽  
R Package ◽  
Research Area ◽  
Republic Of China ◽  
Comprehensive Picture ◽  
The Usa ◽  
The Republic ◽  
Academy Of Sciences

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.

scholarly journals MOVICS: an R package for multi-omics integration and visualization in cancer subtyping

2020 ◽  
Author(s):  
Xiaofan Lu ◽  
Jialin Meng ◽  
Yujie Zhou ◽  
Liyun Jiang ◽  
Fangrong Yan
Keyword(s):  
Clustering Algorithms ◽  
R Package ◽  
Supplementary Information ◽  
Multiple Perspectives ◽  
Model Free ◽  
Omics Integration ◽  
Wide Range ◽  
Breast Cancer Cohort ◽  
The One ◽  
Minimal Effort

Abstract Summary Stratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here, we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using two model-free approaches for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort. By analysing two published breast cancer cohort, we signifies that MOVICS can serve a wide range of users and assist cancer therapy by moving away from the ‘one-size-fits-all’ approach to patient care. Availability and implementation MOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MOVICS: an R package for multi-omics integration and visualization in cancer subtyping

2020 ◽  
Author(s):  
Xiaofan Lu ◽  
Jialin Meng ◽  
Yujie Zhou ◽  
Liyun Jiang ◽  
Fangrong Yan
Keyword(s):  
State Of The Art ◽  
Clustering Algorithms ◽  
R Package ◽  
Molecular Subgroups ◽  
Multiple Perspectives ◽  
Online Tutorial ◽  
Model Free ◽  
Omics Integration ◽  
Model Free Approach ◽  
Minimal Effort

AbstractSummaryStratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using a model-free approach for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort.Availability and implementationMOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS.

scholarly journals Fast Nonnegative Matrix Factorization and Applications to Pattern Extraction, Deconvolution and Imputation

2018 ◽  
Author(s):  
Xihui Lin ◽  
Paul C. Boutros
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Prior Knowledge ◽  
Matrix Factorization ◽  
Missing Values ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
R Package ◽  
Slow Convergence ◽  
R Packages

AbstractNonnegative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence, and cannot handle missing values. In addition, most NMF research focuses only on blind decompositions: decomposition without utilizing prior knowledge. We adapt the idea of sequential coordinate-wise descent to NMF to increase the convergence rate. Our NMF algorithm thus handles missing values naturally and integrates prior knowledge to guide NMF towards a more meaningful decomposition. To support its use, we describe a novel imputation-based method to determine the rank of decomposition. All our algorithms are implemented in the R package NNLM, which is freely available on CRAN.

The Machine-Learning Approach of Reinforcement Learning

2020 ◽  
pp. 105-109
Author(s):  
Thomas Boraud
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Algorithms ◽  
Model Free ◽  
Machine Learning Approach ◽  
Markov Decision ◽  
Decision Making Processes ◽  
Alternative Approaches

This chapter assesses alternative approaches of reinforcement learning that are developed by machine learning. The initial goal of this branch of artificial intelligence, which appeared in the middle of the twentieth century, was to develop and implement algorithms that allow a machine to learn. Originally, they were computers or more or less autonomous robotic automata. As artificial intelligence has developed and cross-fertilized with neuroscience, it has begun to be used to model the learning and decision-making processes for biological agents, broadening the meaning of the word ‘machine’. Theoreticians of this discipline define several categories of learning, but this chapter only deals with those which are related to reinforcement learning. To understand how these algorithms work, it is necessary first of all to explain the Markov chain and the Markov decision-making process. The chapter then goes on to examine model-free reinforcement learning algorithms, the actor-critic model, and finally model-based reinforcement learning algorithms.

scholarly journals A comparison of rotation-based scores for gene set analysis

2021 ◽  
Author(s):  
Adria Caballe Mestres ◽  
Antonio Berenguer Llergo ◽  
Camille Stephan-Otto Attolini
Keyword(s):  
Null Distribution ◽  
R Package ◽  
Gene Set Analysis ◽  
Enrichment Score ◽  
Dependence Structure ◽  
Major Step ◽  
Vast Number ◽  
Score Functions ◽  
Gene Set ◽  
Computationally Intensive

Gene-wise differential expression is usually the first major step in the statistical analysis of high-throughput data obtained from techniques such as microarrays or RNA-sequencing. The analysis at gene level is often complemented by the screening of the data in a broader biological context that considers as unit of analysis meaningful groups of genes that may have functions in certain biological processes. Among the vast number of publications about gene set analysis, the rotation test for gene set analysis, also referred by roast, is a general sample randomization approach that maintains the integrity of the intra-gene set correlation structure in defining the null distribution of the test. In this work we compare the performance of several enrichment score functions using such rotational approach for hypothesis testing. We find that computationally intensive measures based on Kolmogorov-Smirnov statistics fail to improve the rates of simpler measures of GSA like mean and maxmean scores. We also show the importance of accounting for the gene linear dependence structure of the testing set, which it is linked to the loss of effective signature size. In this regard, weighted statistics are introduced with the aim of maximizing the effective signature size. These are found to out-power other usual scores in some simulations scenarios. The average of absolute values is found to be the most powerful score using both simulated and benchmarking data. All tools are available in the roastgsa R package.

scholarly journals methyvim: Targeted, robust, and model-free differential methylation analysis in R

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1424
Author(s):  
Nima S. Hejazi ◽  
Rachael V. Phillips ◽  
Alan E. Hubbard ◽  
Mark J. van der Laan
Keyword(s):  
Statistical Inference ◽  
Multiple Testing ◽  
R Package ◽  
Differential Methylation ◽  
Computationally Efficient ◽  
Multiple Testing Correction ◽  
Statistical Parameters ◽  
Cpg Sites ◽  
Model Free ◽  
Data Adaptive

We present methyvim, an R package implementing an algorithm for the nonparametric estimation of the effects of exposures on DNA methylation at CpG sites throughout the genome, complete with straightforward statistical inference for such estimates. The approach leverages variable importance measures derived from statistical parameters arising in causal inference, defined in such a manner that they may be used to obtain targeted estimates of the relative importance of individual CpG sites with respect to a binary treatment assigned at the phenotype level, thereby providing a new approach to identifying differentially methylated positions. The procedure implemented is computationally efficient, incorporating a preliminary screening step to isolate a subset of sites for which there is cursory evidence of differential methylation as well as a unique multiple testing correction to control the False Discovery Rate with the same rigor as would be available if all sites were subjected to testing. This novel technique for analysis of differentially methylated positions provides an avenue for incorporating flexible state-of-the-art data-adaptive regression procedures (i.e., machine learning) into the estimation of differential methylation effects without the loss of interpretable statistical inference for the estimated quantity.

scholarly journals AN AI APPROACH TO MEASURING FINANCIAL RISK

2019 ◽  
pp. 1-21 ◽  
Author(s):  
LINING YU ◽  
WOLFGANG KARL HÄRDLE ◽  
lUKAS BORKE ◽  
THIJS BENSCHOP
Keyword(s):  
Artificial Intelligence ◽  
Parallel Computing ◽  
Granger Causality ◽  
Financial Institutions ◽  
Systemic Risk ◽  
Financial Risk ◽  
Risk Measure ◽  
R Package ◽  
Lasso Regression ◽  
Publicly Traded

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter ([Formula: see text]) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly-traded financial institutions. We demonstrate the suitability of this AI-based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword [Formula: see text] FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm.

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