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projectR: An R/Bioconductor package for transfer learning via PCA, NMF, correlation, and clustering
[article]
2019
bioRxiv
pre-print
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We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis. ...
Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically-driven validation by evaluating their use in or association with ...
Acknowledgements We thank Timothy Triche, Jr and Thomas Sherman for feedback.
Funding This work was supported the NIH (R01CA177669, U01CA196390, and U01CA212007 to EJF), the NSF (IOS-1656592 ...
doi:10.1101/726547
fatcat:5hkcb5oaaje5lntwr5ulszwyge
Toxicogenomics: A 2020 Vision
2019
TIPS - Trends in Pharmacological Sciences
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Lastly, we touch on the topics of how TGx approaches could facilitate adverse outcome pathways (AOP) development and enhance read-across strategies to further regulatory application. ...
We also highlight the role of machine learning (particularly deep learning) in developing TGx-based predictive models. ...
be transferred to learn from a new dataset with limited information. ...
doi:10.1016/j.tips.2018.12.001
pmid:30594306
pmcid:PMC9988209
fatcat:bzqazo7oenhavkon7u6qn4zh4e
Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data
2015
BMC Bioinformatics
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In addition, TRL-FM performed better than other integrative models driven by meta-analysis and cross-platform data merging. ...
To this end, we have developed a methodology that leverages transfer rule learning and functional modules, which we call TRL-FM, to capture and abstract domain knowledge in the form of classification rules ...
We would also like to extend our gratitude to Jeya Balaji Balasubraman and Aditya Nemlekar for their contributions to the design and implementation of TRL-FM. ...
doi:10.1186/s12859-015-0643-8
pmid:26202217
pmcid:PMC4512094
fatcat:5zrainy2xnduddw5wnurfojhbu
Integration and transfer learning of single-cell transcriptomes via cFIT
2021
Proceedings of the National Academy of Sciences of the United States of America
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Here, we present a simple yet surprisingly effective method named common factor integration and transfer learning (cFIT) for capturing various batch effects across experiments, technologies, subjects, ...
The model parameters are learned under an iterative nonnegative matrix factorization (NMF) framework and then used for synchronized integration from across-domain assays. ...
We thank Kevin Lin for helpful comments. This work was supported in part by National Institute of Mental Health Grants R01MH123184 (to K.R.) and R37MH057881 (to K.R.). ...
doi:10.1073/pnas.2024383118
pmid:33658382
pmcid:PMC7958425
fatcat:d5habjbhtjhqznh3rnrdn4vz4m
Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species
[article]
2018
bioRxiv
pre-print
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labels or information from one dataset to be used for annotation of the other — a machine learning concept called transfer learning. ...
factors reflect biologically meaningful relationships across different platforms, tissues and species. ...
Zack for assistance with FACS analysis, J. Taroni for discussions on transfer learning and low dimensional representations, A. Wolf and F. ...
doi:10.1101/395004
fatcat:6xp3vxa4j5aergwdpiovvc2hoq
Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications
2018
Complexity
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Processing, mining, and learning complex data refer to an advanced study area of data mining and knowledge discovery concerning the development and analysis of approaches for discovering patterns and learning ...
Experiments conducted on the transfer learning datasets transfer knowledge from image to image. ...
Acknowledgments The Guest Editorial Team would like to express their gratitude to all the authors for their interest in selecting this special issue as a venue for their scholarly work dissemination. ...
doi:10.1155/2018/7861860
fatcat:6mc7cqtqzjcjlghqbmlhb5hifa
Community-wide hackathons to identify central themes in single-cell multi-omics
2021
Genome Biology
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Hackathon 2: cross-platform and cross-study integration with spatial proteomics The second hackathon focused on an integrative data analysis across studies and platforms with limited overlap in proteins ...
is a transfer learning framework to rapidly explore latent spaces across independent datasets R package SingleCellMultiModal Serves multiple datasets obtained from GEO and other sources and represents ...
Case study for spatial transcriptomics: integration of scRNA-seq + seqFISH. S2. Case study for cross-study and cross-platform analysis: spatial proteomics. S3. ...
doi:10.1186/s13059-021-02433-9
pmid:34353350
pmcid:PMC8340473
fatcat:lvcuong62jbdxbfcmyfvsws7si
Practical considerations for active machine learning in drug discovery
2020
Drug Discovery Today : Technologies
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With the recent re-discovered enthusiasm for artificial intelligence as well as improved flexibility of laboratory automation, active learning is expected to surge and become a key technology for molecular ...
Although a long established theoretical concept and introduced to drug discovery approximately 15 years ago, the deployment of active learning technology in the discovery pipelines across academia and ...
Even more importantly, a surge of recent papers have advocated for the utility of alternative learning approaches such as meta-learning [26] , transfer-learning [27] , multi-task learning [28] , few-shot ...
doi:10.1016/j.ddtec.2020.06.001
pmid:33386097
fatcat:sd6rivhyc5gj3puykka2wpvixy
The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms
2020
Sensors
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We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). ...
Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals ...
This need would require a certain level of embedded capacity for data acquisition and treatment from all the involved platforms or the efficient data transfer to a central data bank for post-processing ...
doi:10.3390/s20061751
pmid:32245204
fatcat:xyabit6slrcx7cqwkfhwywmq4m
Challenges and Opportunities for Marketing Scholars in Times of the Fourth Industrial Revolution
2020
Journal of Interactive Marketing
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All in all, marketing scholars should focus on enhancing their abilities in knowledge integration across boundaries to sustain their role as cutting-edge scientists. ...
We propose that crossing syntactic, semantic, and pragmatic boundaries is facilitated by three FIR phenomena (big data, machine learning, and AI). ...
All papers published in this special issue have gone through at least two rounds of reviews and revisions, and a team of three reviewers per submission provided valuable and constructive feedback. ...
doi:10.1016/j.intmar.2020.06.001
fatcat:iga5jts4xrauvn3l4gajfnxxrq
sciCAN: Single-cell chromatin accessibility and gene expression data integration via Cycle-consistent Adversarial Network
[article]
2021
bioRxiv
pre-print
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Here, we present a novel adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. ...
With advances in single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge and gains more and more attention within the community. ...
We would also like to thank Heng Li for manuscript proofing and editing. ...
doi:10.1101/2021.11.30.470677
fatcat:4ib3nkesqnesrabvrwar7c3sc4
Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species
2019
Cell Systems
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transfer learning. ...
, and a murine-cell type atlas to identify shared biological features. ...
Taroni for discussions on transfer learning and low dimensional representations. The authors would like to thank C.A. Berlinicke and D.J. Zack for assistance with FACS analysis, A. Wolf and F. ...
doi:10.1016/j.cels.2019.04.004
pmid:31121116
pmcid:PMC6588402
fatcat:qldx5xfwmnalhicnxj3efm6fne
The role of transfer learning in enhancing model generalization in deep learning
2018
International Journal of Applied Research
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These features can be transferred and fine-tuned for specific tasks, allowing models to learn more efficiently with limited labeled data. ...
Transfer learning involves leveraging knowledge gained from a source task to improve performance on a target task. ...
Similarly, Yang and Gao incorporated multi-view information for knowledge transfer between domains, and Feuz and Cook introduced a multi-view transfer learning approach for activity learning across heterogeneous ...
doi:10.22271/allresearch.2018.v4.i10a.11453
fatcat:2fpm3mn5sjex7addlmjqgjpvv4
Using genome-wide expression compendia to study microorganisms
[article]
2022
arXiv
pre-print
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A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. ...
Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to ...
See section 'Challenges integrating across experiments' for a discussion of batch correction. ...
arXiv:2203.13946v1
fatcat:k623wzobk5fijfj3dqsw2wzzwu
Organizing genome engineering for the gigabase scale
[article]
2019
arXiv
pre-print
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In particular, we find that a major under-recognized challenge is coordinating the flow of models, designs, constructs, and measurements across the large teams and complex technological systems that will ...
new technologies to address major open questions around data curation and quality control, 3) conducting fundamental research on the integration of modeling and design at the genomic scale, and 4) developing ...
platforms and manufacturers, and straightforward ways to integrate automated steps into a larger automated workflow, as well as integrate that workflow with machine learning. ...
arXiv:1909.01468v1
fatcat:tze7mtdi4bhqloq35gfy7wjivm
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