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Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach
2017
Bioinformatics
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Results: Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. ...
To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass ...
for his help in setting up the model for analysis. ...
doi:10.1093/bioinformatics/btx766
pmid:29186355
pmcid:PMC5905617
fatcat:j52olwwrjfglrc4frurdc7xysa
Machine learning and feature selection for drug response prediction in precision oncology applications
2018
Biophysical Reviews
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In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual ...
We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional ...
Predictive models can also be tailored, for instance, to specific cancer type or drug classes, or alternatively, one may choose a pan-cancer approach, to model multi-drug class or even combinatorial drug ...
doi:10.1007/s12551-018-0446-z
pmid:30097794
fatcat:mnrzqyngmbgx5jpwpf3fa7bws4
Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools
2020
Frontiers in Oncology
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The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. ...
data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. ...
A Kernel-based approach, combined with non-linear regression and Bayesian inference, resulted to be the best performing algorithm in a drug sensitivity prediction challenge (26) . ...
doi:10.3389/fonc.2020.01030
pmid:32695678
pmcid:PMC7338582
fatcat:wr3auiukhrdm7o76ksgayy4aim
Predictive approaches for drug combination discovery in cancer
2016
Briefings in Bioinformatics
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Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. ...
We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches. ...
Systems-based versus isolated perspective A systems-based approach views cancer as a complicated disease resulting from interactions between many genomics, proteomics, transcriptomics and metabolomics ...
doi:10.1093/bib/bbw104
pmid:27881431
pmcid:PMC6018991
fatcat:y5gn5va2nfdk3nl2sot2zlrowq
Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology
2020
Frontiers in Pharmacology
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AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification ...
Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. ...
Drug sensitivity prediction models, which are entirely based on gene expression profile, are less trustworthy compared to those which are based on integrated multi-omics profiling. ...
doi:10.3389/fphar.2020.01177
pmid:32903628
pmcid:PMC7438594
fatcat:u7mdynhnwfazbn6jhvcagorp2a
From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology
2018
High-Throughput
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Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity ...
By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. ...
] and drug sensitivity/resistance prediction [84, 85] . ...
doi:10.3390/ht7040033
pmid:30373182
pmcid:PMC6306876
fatcat:usqtuskklng55fptttdvntwfcm
Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes
2021
Molecules and Cells
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A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome ...
Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. ...
ACKNOWLEDGMENTS This work was supported by the Korean NRF Grant 2019M3E5D3073568 (to J.Y.A.) and a Korea University Grant. ...
doi:10.14348/molcells.2021.0042
pmid:34238766
pmcid:PMC8334347
fatcat:vpztnxnfa5gsrfnvgi4fybke5m
Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer
2020
Frontiers in Oncology
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As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression ...
Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types ...
Pan-cancer analysis of nine cancers has revealed that proteomics data combined with gene expression, miRNA expressions, and genomics performs better in predicting the sensitivity of chemotherapeutics and ...
doi:10.3389/fonc.2020.588221
pmid:33154949
pmcid:PMC7591760
fatcat:5kfd6jid6vcx7h4qvk52pngo5m
AutoOmics: An AutoML Tool for Multi-Omics Research
[article]
2020
bioRxiv
pre-print
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We evaluate our method in four different tasks: drug repositioning, target gene prediction, breast cancer subtyping and cancer type prediction, and all the four tasks achieved state of art performances ...
In this paper, we present a novel multimodal approach that could efficiently integrate information from different omics data and achieve better accuracy than previous approaches. ...
prediction and d. pan-cancer patient stratification, the performances are compared both with popular multi-omics approaches and single-omics approaches, the results show us the significantly improvements ...
doi:10.1101/2020.04.02.021345
fatcat:yqy2zrbtdnhdvmdrxhntau3fqm
Adversary-aware Multimodal Neural Networks for Cancer Susceptibility Prediction from Multi-omics Data
2022
IEEE Access
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In this paper, we propose an adversaryaware multimodal convolutional autoencoder (MCAE) model for cancer susceptibility prediction from multi-omics data consisting of copy number variations (CNVs), miRNA ...
Based on different representational learning techniques, the MCAE model learns multimodal feature representations from multi-omics data, followed by classifying the patient cohorts into different cancer ...
Multi-omics data covering CNVs, miRNA, and GE profiles of 9,074 patients from the Pan-Cancer Atlas project covering 33 tumour types are considered. ...
doi:10.1109/access.2022.3175816
fatcat:fdpdj6i7xzherkz5tklhftazbm
Proteogenomic convergence for understanding cancer pathways and networks
2014
Clinical Proteomics
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With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect ...
It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the ...
As a result, genomic alterations associated with cancer have been produced through multi-dimensional datasets that include high level integrative analysis with omics datasets. ...
doi:10.1186/1559-0275-11-22
pmid:24994965
pmcid:PMC4067069
fatcat:6so4o3yqzbg6rau4z53xnneu4q
Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome
[article]
2019
bioRxiv
pre-print
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We assessed pan-cancer pathway activities for a large cohort of patient samples (>7700) from The Cancer Proteome Atlas across 30+ tumor types and a set of 640 cancer cell lines from the M.D. ...
activities, globally assess cell lines as representative models for patients and develop drug sensitivity prediction models. ...
Additionally, we used drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) 6 database, with the sensitivity of 481 drugs assessed on a subset of 254 cell lines (ST S4). ...
doi:10.1101/806596
fatcat:t2snhz4h65hy3nn5ffcvoszlxa
Multi-omics Data Integration, Interpretation, and Its Application
2020
Bioinformatics and Biology Insights
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With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and ...
To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and ...
Furthermore, associating the integrated clusters with the pharmacological profiles of 24 anticancer drug compounds revealed selective sensitivity to MEK inhibitors in a subset of hematopoietic cell lines ...
doi:10.1177/1177932219899051
pmid:32076369
pmcid:PMC7003173
fatcat:dchnmbmzh5di7jcuc7ilxjsk3e
Integration of Online Omics-Data Resources for Cancer Research
2020
Frontiers in Genetics
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The advantage of multi-omics data integration comes with a trade-off in the form of an added layer of complexity originating from inherently diverse types of omics-datasets that may pose a challenge to ...
Finally, we propose the multi-omics driven systems-biology approaches to realize the potential of precision onco-medicine as the future of cancer research. ...
This model uses a shrunken gene-centroid algorithm to the resulting interaction scores to select the best predictive sub-networks for breast cancer (BC) phenotypic groups. ...
doi:10.3389/fgene.2020.578345
pmid:33193699
pmcid:PMC7645150
fatcat:adq25boimjbkjgrrdpdwgtmfma
Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
2021
Biomedicines
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We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. ...
The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/biomedicines9111733
pmid:34829962
pmcid:PMC8615388
fatcat:oqgigce2bvaitl5sjflutsccsm
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