A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology
2019
International Conference on Machine Learning
Preserved Fulltext
Rather than performing survival analysis on each data set to predict survival times of cancer patients, we developed a novel multitask approach based on multiple kernel learning (MKL). ...
Our multitask MKL algorithm both works on multiple cancer data sets and integrates cancer-related pathways/gene sets into survival analysis. ...
In this study, we combined survival analysis, MKL (for pathway selection) and multitask learning (for modeling multiple cohorts) in a unified formulation for the first time. ...
dblp:conf/icml/DereliOG19
fatcat:zzktvfce3ne5nakic4yfpg27x4
Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation
2016
BMC Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We then generalize this formulation to multitask learning setting to model multiple related datasets conjointly. ...
We also show that our multitask learning formulation enables us to further improve the generalization performance and to better understand biological processes behind disease phenotypes. ...
To this aim, we propose a Bayesian multiple kernel learning algorithm, which produces a classifier with sparse gene set weights, by extending our earlier Bayesian formulation [8] . ...
doi:10.1186/s12859-016-1311-3
pmid:28105911
pmcid:PMC5249028
fatcat:bllzdzxgyzestgfevzus7sw454
PIMKL: Pathway-Induced Multiple Kernel Learning
2019
npj Systems Biology and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm ...
We propose Pathway-Induced Multiple Kernel Learning (PIMKL), a methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. ...
ACKNOWLEDGEMENTS We thank Yupeng Cun for kindly providing results 13 for the creation of Figs. 1a and S1. ...
doi:10.1038/s41540-019-0086-3
pmid:30854223
pmcid:PMC6401099
fatcat:nv7rall7crepdcnk6gwzyqwk3e
Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer
2020
Frontiers in Oncology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the ...
analysis and understanding of multi-omics data for patient-specific observations. ...
The authors acknowledge the CSIR-Indian Institute of Chemical Biology for infrastructural support. ...
doi:10.3389/fonc.2020.588221
pmid:33154949
pmcid:PMC7591760
fatcat:5kfd6jid6vcx7h4qvk52pngo5m
A community effort to assess and improve drug sensitivity prediction algorithms
2014
Nature Biotechnology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
a n a ly s i s advance online publication nature biotechnology a n a ly s i s models. ...
The evolution of the DREAM project will continue as the challenges in biomedical research expand to the genome scale. ...
Team Synopsis
The second set a
Original views
Bayesian multitask multiple kernel learning
Multitask learning
Multiple kernel learning
Kernels
K 1
K K
K
Kernel weights
Kernel 1
Kernel ...
doi:10.1038/nbt.2877
pmid:24880487
pmcid:PMC4547623
fatcat:psjjxz2orbga7newjwf7d6bqea
Machine Learning and Integrative Analysis of Biomedical Big Data
2019
Genes
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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. ...
Multiple kernel learning (MKL), a robust integrative analysis approach with heterogenous data, employs different kernels or similarity functions for data from different sources and fuses them into a global ...
doi:10.3390/genes10020087
pmid:30696086
pmcid:PMC6410075
fatcat:vopnjgke4fculmr7t3n43ewfiy
Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications
2022
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Special gratitude and appreciation are extended to all the contributors for their high-quality submissions and the reviewers for volunteering their time and expertise to review the scientific merit of ...
ACKNOWLEDGMENTS We sincerely appreciate Frontiers in Genetics giving us this opportunity to organize this research topic. ...
The method uses multitask learning integrated with the bidirectional gated recurrent units (BGRU). ...
doi:10.3389/fgene.2022.870795
pmid:35368698
pmcid:PMC8969764
fatcat:vxxdfzq2rnbqboqww6gcxpvema
Reconstructing cancer drug response networks using multitask learning
2017
BMC Systems Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Translating in vitro results to clinical tests is a major challenge in systems biology. ...
We used top proteins from each drug network to predict survival for patients prescribed the drug. ...
Acknowledgements Not applicable. ...
doi:10.1186/s12918-017-0471-8
pmid:29017547
pmcid:PMC5635550
fatcat:h3omquo2azb2la56pcu6rcoji4
Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge
2021
PLoS Computational Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates ...
, survival in breast cancer, and cell line response to gene knockdowns. ...
Consequently, many bioinformatics approaches seek to combine data at the level of a biological process to benefit machine-learning applications in the cancer genomics setting. ...
doi:10.1371/journal.pcbi.1008878
pmid:33861732
fatcat:qo3d2mzfhnftrglo4yhqlsw5dm
Applications of Machine Learning in Drug Discovery I: Target Discovery and Small Molecule Drug Design
[chapter]
2020
Artificial Intelligence in Oncology Drug Discovery and Development
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Machine learning (ML) has re-emerged in the last several years as a powerful set of tools for unlocking value from large datasets. ...
We focus our analysis on oncology, though make reference to the wider field of human health and disease. Learning in Drug Discovery I: Target Discovery and Small Molecule... ...
For these reasons, the application of ML to the field of modern biology is extremely well suited. ...
doi:10.5772/intechopen.93159
fatcat:z5xuzsnga5ggfnp5sofk6hyodi
Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
2022
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics ...
Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. ...
Multiple imputation; MICE, multivariate imputation by chained equation; MIDA, denoising autoencoder-based MI; MI-MFA, MI for multiple factor analysis; MKL, Multiple kernel learning; ML, Machine learning ...
doi:10.3389/fgene.2022.824451
pmid:35154283
pmcid:PMC8829119
fatcat:n5infqv5ifgeveaksrtdco4jui
Cascaded Wx: A Novel Prognosis-Related Feature Selection Framework in Human Lung Adenocarcinoma Transcriptomes
2019
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Artificial neural network-based analysis has recently been used to predict clinical outcomes in patients with solid cancers, including lung cancer. ...
The CWx framework ranks features according to the survival of a given cohort by training neural networks with three different high- and low-risk groups in a cascaded fashion. ...
Machine learning (ML) algorithms can be a useful approach to the analysis of high volumes of data if a model is well constructed with high-quality input data for training. ...
doi:10.3389/fgene.2019.00662
pmid:31379926
pmcid:PMC6658675
fatcat:cnssawo2xfgpnfr26sqgfruqwi
Applications of machine learning in drug discovery and development
2019
Nature reviews. Drug discovery
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. ...
With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential ...
Papa for helpful comments, M. Segler for contributing to the small-molecule optimization subsection and A. Janowczyk for providing the pathology images in Figure 4 . ...
doi:10.1038/s41573-019-0024-5
pmid:30976107
pmcid:PMC6552674
fatcat:4gubtr5kz5fe7khrbtfrre4lhy
Structural and functional radiomics for lung cancer
2021
European Journal of Nuclear Medicine and Molecular Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. ...
Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer ...
Radiomics refers to the extraction and analysis of quantitative image features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes ...
doi:10.1007/s00259-021-05242-1
pmid:33693966
pmcid:PMC8484174
fatcat:obo4bghkp5gx3a52ibiuvq22hq
An overview of machine learning methods for monotherapy drug response prediction
2021
Briefings in Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses ...
ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. ...
Acknowledgements We thank the anonymous reviewers and Krister Wennerberg for helpful comments on a previous version of this article. ...
doi:10.1093/bib/bbab408
pmid:34619752
pmcid:PMC8769705
fatcat:t4gwewnsarb5rm6tx3zdbqnhhm
« Previous
Showing results 1 — 15 out of 92 results