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Prediction of protein-protein interactions based on elastic net and deep forest
[article]
2020
bioRxiv
pre-print
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Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep forest is built up. ...
Prediction of protein-protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to predict PPIs are limited and costly. ...
Then using 329 elastic net to find effective, significant, and valuable feature subset. Finally, the GcForest-PPI 330 model is constructed based on deep forest. ...
doi:10.1101/2020.04.23.058644
fatcat:p6aolklvj5bofjqgylsuw2zjom
Interpreting the contributions of transcript features to protein abundance prediction models
[article]
2022
bioRxiv
pre-print
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Network analysis reveals a proteome-wide interdependency of protein abundance on the transcript levels of interacting proteins. ...
Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known interaction partners and protein complex members of the protein of interest. ...
Acknowledgments This work was supported in part by NIH Office of the Director award R03-OD032666 and NIH/NHLBI award R00-HL144829 to E.L. ...
doi:10.1101/2022.03.14.484316
fatcat:oxltszsyp5ayzotdncrcfxr2nu
Algorithms for Drug Sensitivity Prediction
2016
Algorithms
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A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes ...
We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/a9040077
fatcat:5txgj6a2dbhe5mrmkvsxxcomuq
A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimer's Disease Within Different Brain Regions
[article]
2021
bioRxiv
pre-print
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Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus ...
Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing ...
The Elastic Net classifier obtained excellent performance in the majority of scenarios, followed by the random forest classifier and SVM. ...
doi:10.1101/2021.03.03.433689
fatcat:tueep4u6nzcepjv5y4uw7wko2q
Network-based drug sensitivity prediction
2020
BMC Medical Genomics
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Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support ...
Conclusions Network-based feature selection method and prediction models improve the performance of the drug response prediction. ...
The full contents of the supplement are available at https ://bmcme dgeno mics.biome dcent ral.com/artic les/suppl ement s/volum e-13-suppl ement -11. ...
doi:10.1186/s12920-020-00829-3
pmid:33371891
fatcat:bhbxbj4xorbmhauzjahb6mifta
Machine Learning and Integrative Analysis of Biomedical Big Data
2019
Genes
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In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing ...
Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. ...
In [50] , Linked Data, biomedical ontologies and ontology-based annotations were integrated, facilitating functional prediction and the predictions of protein-protein interaction (PPI), drug target relations ...
doi:10.3390/genes10020087
pmid:30696086
pmcid:PMC6410075
fatcat:vopnjgke4fculmr7t3n43ewfiy
A deep auto-encoder model for gene expression prediction
2017
BMC Genomics
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This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). ...
Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. ...
NIH (R01GM093123 to JC), NSF (DBI1149224 to JC, DGE-1523154 and IIS-1502172 to XS), and U.S. Department of Education (GAANN fellowship to AQ). ...
doi:10.1186/s12864-017-4226-0
pmid:29219072
pmcid:PMC5773895
fatcat:rx74p4ha6zboppnp2bi3swf3g4
Deep graph embedding for prioritizing synergistic anticancer drug combinations
[article]
2019
arXiv
pre-print
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The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. ...
Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. ...
Funding This research was supported in part by Canadian Breast Cancer Foundation, Natural Sciences and Engineering Research Council of Canada, Mitacs and University of Manitoba. ...
arXiv:1911.10316v1
fatcat:crbwxw6uljhhrmit6oiuvimvei
A Methodology for the Prediction of Drug Target Interaction using CDK Descriptors
[article]
2022
arXiv
pre-print
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To meet these challenges, we propose a DTI prediction model built on molecular structure of drugs and sequence of target proteins. ...
Although certain methods have been developed for this cause, numerous interactions are yet to be discovered, and prediction accuracy is still low. ...
As a result, high-efficiency computational prediction techniques to investigate drug target interactions based on Machine Learning (ML) and Deep Learning (DL) have sparked a lot of attention in recent ...
arXiv:2210.11482v1
fatcat:uttvgq4smrbtxcnfen7v5dxxoa
Machine‐learning scoring functions for structure‐based drug lead optimization
2020
Wiley Interdisciplinary Reviews. Computational Molecular Science
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Against the expectations of many experts, SFs employing deep learning techniques were not always more predictive than those based on more established machine learning techniques and, when they were, the ...
A classical SF assumes a predetermined theory-inspired functional form for the relationship between the features characterizing the structure of the protein-ligand complex and its predicted binding affinity ...
A GBDT-based SF yielded R p = 0.77, outperforming three baseline ML-based SFs using Elastic Net, SVM, and RF. ...
doi:10.1002/wcms.1465
fatcat:qnrk4qw3h5gjtcxncourqzhkxe
Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
[article]
2022
bioRxiv
pre-print
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Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. ...
Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. ...
Acknowledgments We thank Ricard Argelaguet for helpful comments and discussions on the implementations of the MOFA analysis, and Keith Ashman for discussions on optimizing the bank of mass ...
doi:10.1101/2022.02.26.482008
fatcat:nuibkaf7araqvgfdrjpdkfiz6q
A novel artificial intelligence protocol to investigate potential leads for Parkinson's disease
2020
RSC Advances
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Acknowledgements This work was supported by Guangzhou science and technology fund (Grant No. 201803010072), Science, Technology & Innovation Commission of Shenzhen Municipality (JCYL 20170818165305521) ...
Notes and references ...
Those interactions are based on the structure of target protein and compounds, and the more interactions between them, the better activity it will be. ...
doi:10.1039/d0ra04028b
pmid:35520357
pmcid:PMC9054719
fatcat:gxd7dojadjgixc3hojvgtxdypq
ADH-PPI: An Attention based Deep Hybrid Model for Protein Protein Interaction Prediction
2022
iScience
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Protein-protein interaction (PPI) prediction is essential to understand the functions of proteins in various biological processes and their roles in the development, progression, and treatment of different ...
However, these approaches have limited predictive performance due to the use of in-effective statistical representation learning methods and predictors that lack the ability to extract comprehensive discriminative ...
Declaration of Competing Interest The authors declare no competing interests. ...
doi:10.1016/j.isci.2022.105169
pmid:36267921
pmcid:PMC9576568
fatcat:d2yc7hfu6net5oqdsfqga7mf6y
Enhancing Student Competency Models for Game-Based Learning with a Hybrid Stealth Assessment Framework
2020
Educational Data Mining
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Stealth assessment models utilize students' observed gameplay behaviors using challenge-and session-based features to predict students' learning outcomes on identified concepts. ...
We present single-task and multi-task models for predicting students' mastery of concepts and the results suggest that the hybrid stealth assessment framework outperforms individual models and holds significant ...
Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. ...
dblp:conf/edm/HendersonKMMWBL20
fatcat:32zhsartevhzlkkv7byqcmxim4
Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches
2020
International Journal of Molecular Sciences
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diagnosis prediction, and the detection of potential biomarkers. ...
In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, ...
In the predictive model, the adaptive elastic net employs elastic net estimates as the initial weight [26, 45] . ...
doi:10.3390/ijms21030969
pmid:32024055
pmcid:PMC7037937
fatcat:wc3cjt6dxncjlhyilafo743lvy
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