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Classification of information fusion methods in systems biology

Jane Synnergren, Björn Olsson, Jonas Gamalielsson
2009 In Silico Biology  
We here review representative examples of bioinformatic methods for fusion-oriented interpretation of multiple heterogeneous biological data, and propose a classification into three categories of tasks  ...  In addition, there is also an increasing need for integrative analysis, where knowledge about the biological system is derived by data fusion, using heterogeneous data sets as input.  ...  retrieval, organization, visualization and statistical analysis of large sets of genes.  ... 
pmid:19795566 fatcat:fnri743i7nfwhg6ch327ewevmq

LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification [article]

Robert Zhang, Rachael Stolzenberg-Solomon, Shannon M. Lynch, Ryan J. Urbanowicz
2021 arXiv   pre-print
This work introduces the LCS Discovery and Visualization Environment (LCS-DIVE), an automated LCS model interpretation pipeline for complex biomedical classification.  ...  then applied to characterize associations within a real-world study of pancreatic cancer.  ...  Next, we introduce LCS-DIVE (LCS Discovery and Visualization Environment), an automated LCS model interpretation pipeline for noisy, complex classification problems.  ... 
arXiv:2104.12844v1 fatcat:hlajhzalpre2dfdwnhv6id6umm

Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities [article]

Manu Goyal, Thomas Knackstedt, Shaofeng Yan, Saeed Hassanpour
2020 arXiv   pre-print
in this domain.  ...  With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians  ...  M.G. contributed to the literature review and analysis of the study and drafting the manuscript.  ... 
arXiv:1911.11872v3 fatcat:hez3zmpfgzar3cjpa4ye67xczi

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics [chapter]

Cagatay Turkay, Fleur Jeanquartier, Andreas Holzinger, Helwig Hauser
2014 Lecture Notes in Computer Science  
In these approaches, the automated methods are used seamlessly within interactive visual analysis. Sophisticated interaction mechanisms make the automated tools an integral part of the visualization.  ...  of the colon cancer network.  ... 
doi:10.1007/978-3-662-43968-5_7 fatcat:ekpuulvb4nhuzgrbjbq3igylei

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data [article]

Robert Krueger, Johanna Beyer, Won-Dong Jang, Nam Wook Kim, Artem Sokolov, Peter K Sorger, Hanspeter Pfister
2019 bioRxiv   pre-print
To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states.  ...  We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.  ...  This work is supported by the Ludwig Center at Harvard Medical School, by NCI Grant U54-CA225088, by King Abdullah University of Science and Technology (KAUST) and the KAUST Office of Sponsored Research  ... 
doi:10.1101/722918 fatcat:t7lob3q2fndmxngn4udvqxr7ea

2018 Index IEEE Transactions on Visualization and Computer Graphics Vol. 24

2019 IEEE Transactions on Visualization and Computer Graphics  
., TVCG Aug. 2018 2284-2297 Inpainting via Two-Stage Low Rank Approximation; TVCG June 2018 2023-2036 Summarization and Stage Analysis of Event Sequence Data; TVCG Jan. 2018 56-65 Guo, Y., see Liu  ...  ., þ, TVCG Jan. 2018 371-381 Genomics An Analysis of Automated Visual Analysis Classification: Interactive Visualization Task Inference of Cancer Genomics Domain Experts.  ...  ., þ, TVCG Aug. 2018 2424-2439 An Analysis of Automated Visual Analysis Classification: Interactive Visualization Task Inference of Cancer Genomics Domain Experts.  ... 
doi:10.1109/tvcg.2018.2885445 fatcat:xtccdnalwfhudp3qdxofz4kqpi

AI and Medical Imaging Informatics: Current Challenges and Future Directions

Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita (+4 others)
2020 IEEE journal of biomedical and health informatics  
The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented.  ...  It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already  ...  , classification and inference.  ... 
doi:10.1109/jbhi.2020.2991043 pmid:32609615 pmcid:PMC8580417 fatcat:dcaefxwwqjfwla5asin34x2hxm

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Xiao-Xia Yin, Lihua Yin, Sillas Hadjiloucas
2020 Applied Sciences  
Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward.  ...  The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.  ...  In deep learning, features are extracted from data directly, using non-linear inference engines in a similar way to that of an expert performing the diagnosis.  ... 
doi:10.3390/app10207201 fatcat:tofpvyllzbautos4my26xajqfe

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

Jason A. Fries, Paroma Varma, Vincent S. Chen, Ke Xiao, Heliodoro Tejeda, Priyanka Saha, Jared Dunnmon, Henry Chubb, Shiraz Maskatia, Madalina Fiterau, Scott Delp, Euan Ashley (+2 others)
2019 Nature Communications  
Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels.  ...  In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event.  ...  This work was supported in part by the Mobilize Center, a National Institutes of Health Big Data to Knowledge (  ... 
doi:10.1038/s41467-019-11012-3 pmid:31308376 pmcid:PMC6629670 fatcat:lxq45lvamjhyvice4ev4mjwiri

Text-mining approaches in molecular biology and biomedicine

2005 Drug Discovery Today  
Also , the use of text mining to aid the interpretation of microarray data and the analysis of pathology reports will be discussed.  ...  In this article, important applications such as the identification of biologically relevant entities in free text and the construction of literature-based networks of protein-protein interactions will  ...  'Breast cancer' is used as an initial query concept.  ... 
doi:10.1016/s1359-6446(05)03376-3 pmid:15808823 fatcat:w5i3ciuq4zhk7oq4kbbv5wqfma

Planning bioinformatics workflows using an expert system

Xiaoling Chen, Jeffrey T. Chang
2017 Bioinformatics  
In sum, an expert system approach can facilitate exploratory bioinformatic analysis by automating the development of workflows, a task that requires significant domain expertise.  ...  BETSY is a backwards-chaining rule-based expert system comprised of a data model that can capture the richness of biological data, and an inference engine that reasons on the knowledge base to produce  ...  Funding This work was supported by grants R1006 and UL1 TR000371 from the Cancer Prevention and Research Institute of Texas and TL1TR000371 from the National Institutes of Health.  ... 
doi:10.1093/bioinformatics/btw817 pmid:28052928 pmcid:PMC5860174 fatcat:zyclakthere5rnrwl44tgelora

Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming [article]

Andrew Sohn and Randal S. Olson and Jason H. Moore
2017 arXiv   pre-print
In TPOT-MDR, we implement Multifactor Dimensionality Reduction (MDR) as a feature construction method for modeling higher-order feature interactions, and combine it with a new expert knowledge-guided feature  ...  However, effectively using machine learning methods requires considerable domain expertise, which can be a barrier of entry for bioinformaticians new to computational data science methods.  ...  ACKNOWLEDGEMENTS We thank the Penn Medicine Academic Computing Services for the use of their computing resources. This work was supported by National Institutes of Health grant AI116794.  ... 
arXiv:1702.01780v1 fatcat:lt2sacuhzfcevgmj6xchj2ctha

Data Mining, a Tool for Systems Biology or a Systems Biology Tool

Nicolas Turenne
2009 Journal of computer science and systems biology  
from efficient classification methods to clustering, outlier analysis, frequent, sequential, and structured pattern analysis methods, and visualization and spatial/temporal data analysis tools.  ...  In 2004 a team from the University of Colorado developed an algorithm, PathMiner, based on heuristic search, to extract, or infer, biotransformation rules from the Kyoto Encyclopedia of Genes and Genomes  ... 
doi:10.4172/jcsb.1000034e fatcat:it7fvvc5zjbsvaz5io7dp2fy74

MEDUSA: large scale automatic selection and visual assessment of PCR primer pairs

R. M. Podowski, E. L. L. Sonnhammer
2001 Bioinformatics  
The system was able to achieve high quality gene disambiguation using scalable automated techniques.  ...  The bioinformatics approaches to this problem assist researchers in advancing our understanding of the functional of human protein encoding genes.  ...  , yet conserved across species throughout evolution N Exploratory visualization of complementary genomic-scale, experimental and computational data with parallel heatmaps N An automated text classification  ... 
doi:10.1093/bioinformatics/17.7.656 pmid:11448885 fatcat:bmepasqiznhvdabpxrnwlzqy2q

The GeneMine system for genome/proteome annotation and collaborative data mining

C. Lee, K. Irizarry
2001 IBM Systems Journal  
The GeneMine system for small-to medium-scale genome analysis provides: (1) automated analysis of DNA (deoxyribonucleic acid) and protein sequence data using over 50 different analysis servers via the  ...  features, etc., (2) automated filtering and data reduction to highlight significant and interesting patterns, (3) a visual data-mining interface for rapidly exploring correlations, patterns, and contradictions  ...  First, we opted to focus on information visualization, providing an interactive, visual tool for human scientists to make and validate discoveries, as opposed to automated data-mining programs for computers  ... 
doi:10.1147/sj.402.0592 fatcat:fnrivwqh2jbqxp5p45bzjyfydm
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