scholarly journals Bayesian generalized biclustering analysis via adaptive structured shrinkage

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 610-624
Author(s):  
Ziyi Li ◽  
Changgee Chang ◽  
Suprateek Kundu ◽  
Qi Long
Keyword(s):  
Negative Binomial ◽  
Feature Space ◽  
Biological Information ◽  
Superior Performance ◽  
Data Matrix ◽  
Data Types ◽  
Binomial Data ◽  
Multiple Data ◽  
Local Patterns ◽  
Shrinkage Prior

Summary Biclustering techniques can identify local patterns of a data matrix by clustering feature space and sample space at the same time. Various biclustering methods have been proposed and successfully applied to analysis of gene expression data. While existing biclustering methods have many desirable features, most of them are developed for continuous data and few of them can efficiently handle -omics data of various types, for example, binomial data as in single nucleotide polymorphism data or negative binomial data as in RNA-seq data. In addition, none of existing methods can utilize biological information such as those from functional genomics or proteomics. Recent work has shown that incorporating biological information can improve variable selection and prediction performance in analyses such as linear regression and multivariate analysis. In this article, we propose a novel Bayesian biclustering method that can handle multiple data types including Gaussian, Binomial, and Negative Binomial. In addition, our method uses a Bayesian adaptive structured shrinkage prior that enables feature selection guided by existing biological information. Our simulation studies and application to multi-omics datasets demonstrate robust and superior performance of the proposed method, compared to other existing biclustering methods.

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

scholarly journals Imaging anisotropic layering with Bayesian inversion of multiple data types

2016 ◽  
Vol 206 (1) ◽  
pp. 605-629 ◽  
Author(s):  
T. Bodin ◽  
J. Leiva ◽  
B. Romanowicz ◽  
V. Maupin ◽  
H. Yuan
Keyword(s):  
Bayesian Inversion ◽  
Data Types ◽  
Multiple Data

scholarly journals Optical analysis of complex multilayer structures using multiple data types

1994 ◽  
Author(s):  
Blaine D. Johs ◽  
Roger H. French ◽  
Franklin D. Kalk ◽  
William A. McGahan ◽  
John A. Woollam
Keyword(s):  
Multilayer Structures ◽  
Optical Analysis ◽  
Data Types ◽  
Multiple Data

scholarly journals Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map

2021 ◽  
Vol 11 (8) ◽  
pp. 785
Author(s):  
Quentin Miagoux ◽  
Vidisha Singh ◽  
Dereck de Mézquita ◽  
Valerie Chaudru ◽  
Mohamed Elati ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
State Of The Art ◽  
Biological Information ◽  
Response To Treatment ◽  
Patient Specific ◽  
Learning Approaches ◽  
Data Types ◽  
Disease Heterogeneity ◽  
Combining Data

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

scholarly journals Single-Trait and Multiple-Trait Genomic Prediction From Multi-Class Bayesian Alphabet Models Using Biological Information

2021 ◽  
Vol 12 ◽  
Author(s):  
Zigui Wang ◽  
Hao Cheng
Keyword(s):  
Molecular Markers ◽  
Genomic Prediction ◽  
Simulated Data ◽  
Software Tool ◽  
Biological Information ◽  
Superior Performance ◽  
Multiple Trait ◽  
Bayesian Lasso ◽  
Trait Analysis ◽  
Causal Variants

Genomic prediction has been widely used in multiple areas and various genomic prediction methods have been developed. The majority of these methods, however, focus on statistical properties and ignore the abundant useful biological information like genome annotation or previously discovered causal variants. Therefore, to improve prediction performance, several methods have been developed to incorporate biological information into genomic prediction, mostly in single-trait analysis. A commonly used method to incorporate biological information is allocating molecular markers into different classes based on the biological information and assigning separate priors to molecular markers in different classes. It has been shown that such methods can achieve higher prediction accuracy than conventional methods in some circumstances. However, these methods mainly focus on single-trait analysis, and available priors of these methods are limited. Thus, in both single-trait and multiple-trait analysis, we propose the multi-class Bayesian Alphabet methods, in which multiple Bayesian Alphabet priors, including RR-BLUP, BayesA, BayesB, BayesCΠ, and Bayesian LASSO, can be used for markers allocated to different classes. The superior performance of the multi-class Bayesian Alphabet in genomic prediction is demonstrated using both real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.

A Latent Feature Model Approach to Biclustering

2016 ◽  
Vol 6 (2) ◽  
pp. 11-28
Author(s):  
José Caldas ◽  
Samuel Kaski
Keyword(s):  
Expectation Maximization Algorithm ◽  
Learning Task ◽  
Biological Data ◽  
Feature Model ◽  
Data Matrix ◽  
Data Types ◽  
Data Set ◽  
Modelling Framework ◽  
Number Variation ◽  
Mirna Expression Data

Biclustering is the unsupervised learning task of mining a data matrix for useful submatrices, for instance groups of genes that are co-expressed under particular biological conditions. As these submatrices are expected to partly overlap, a significant challenge in biclustering is to develop methods that are able to detect overlapping biclusters. The authors propose a probabilistic mixture modelling framework for biclustering biological data that lends itself to various data types and allows biclusters to overlap. Their framework is akin to the latent feature and mixture-of-experts model families, with inference and parameter estimation being performed via a variational expectation-maximization algorithm. The model compares favorably with competing approaches, both in a binary DNA copy number variation data set and in a miRNA expression data set, indicating that it may potentially be used as a general-problem solving tool in biclustering.

scholarly journals KECA Similarity-Based Monitoring and Diagnosis of Faults in Multi-Phase Batch Processes

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 121 ◽  
Author(s):  
Yongsheng Qi ◽  
Xuebin Meng ◽  
Chenxi Lu ◽  
Xuejin Gao ◽  
Lin Wang
Keyword(s):  
Feature Space ◽  
Batch Processes ◽  
Data Matrix ◽  
Nonlinear Characteristics ◽  
Nonlinear Contribution ◽  
Fault Monitoring ◽  
Feature Information ◽  
Stage Transition ◽  
Multi Phase ◽  
Entropy Component

Multiple phases with phase to phase transitions are important characteristics of many batch processes. The linear characteristics between phases are taken into consideration in the traditional algorithms while nonlinearities are neglected, which can lead to inaccuracy and inefficiency in monitoring. The focus of this paper is nonlinear multi-phase batch processes. A similarity metric is defined based on kernel entropy component analysis (KECA). A KECA similarity-based method is proposed for phase division and fault monitoring. First, nonlinear characteristics can be extracted in feature space via performing KECA on each preprocessed time-slice data matrix. Then phase division is achieved with the similarity variation of the extracted feature information. Then, a series of KECA models and slide-KECA models are established for steady and transitions phases respectively, which can reflect the diversity of transitional characteristics objectively and preferably deal with the stage-transition monitoring problem in multistage batch processes. Next, in order to overcome the problem that the traditional contribution plot cannot be applied to the kernel mapping space, a nonlinear contribution plot diagnosis algorithm is proposed, which is easier, more intuitive and implementable compared with the traditional one. Finally, simulations are performed on penicillin fermentation and industrial application. Specifically, the proposed method detects the abnormal agitation power and the abnormal substrate supply at 47 h and 86 h, respectively. Compared with traditional methods, it has better real-time performance and higher efficiency. Results demonstrate the ability of the proposed method to detect faults accurately and effectively in practice.

scholarly journals Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

Space Weather ◽  
2019 ◽  
Author(s):  
Chalachew Kindie Mengist ◽  
Nicholas Ssessanga ◽  
Se‐Heon Jeong ◽  
Jeong‐Heon Kim ◽  
Yong Ha Kim ◽  
...  
Keyword(s):  
Data Types ◽  
Ionosphere Model ◽  
Multiple Data
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