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A statistical framework for genomic data fusion
2004
Bioinformatics
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Results: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. ...
Motivation: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data ...
This paper presents a computational and statistical framework for integrating heterogeneous descriptions of the same set of genes. ...
doi:10.1093/bioinformatics/bth294
pmid:15130933
fatcat:5si35euhezfo7h4pbudpq7d73u
eMAGMA: An eQTL-informed method to identify risk genes using genome-wide association study summary statistics
[article]
2019
bioRxiv
pre-print
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To address this challenge, we developed a methodological framework, eQTL-MAGMA (eMAGMA), that converts SNP-level summary statistics into gene-level association statistics by assigning non-coding SNPs to ...
We compared eMAGMA to three eQTL informed gene-based approaches—S-PrediXcan, FUSION, and SMR—using simulated phenotype data. ...
In conclusion, we present a modified MAGMA framework, eMAGMA that aggregates eQTL summary statistics into gene level association statistics for gene-level analyses. ...
doi:10.1101/854315
fatcat:ruiuaju4mnb5llkf74coqa3xh4
Translational systems genomics: ontology and imaging
2009
Summit on translational bioinformatics
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In developing an integrated framework for translational bioinformatics, we consider bioimaging in the NIH Roadmap that exploits high-resolution genomic imaging for clinical applications to the diagnosis ...
Foundational Model of Anatomy (FMA) and Microarry Gene Expression Data Ontology (MGED) in this framework. ...
CONCLUSIONS This paper develops an integrated framework for translational bioinformatics in the area of bioimaging that exploits high-resolution genomic imaging for the clinical applications to the diagnosis ...
pmid:21347165
pmcid:PMC3041582
fatcat:y23hfrpykjae5phxfpknde2o3i
Transcriptome wide association studies: general framework and methods
2021
Quantitative Biology
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With the recent progress in expression quantitative trait loci (eQTL) studies, transcriptome-wide association studies (TWAS) provide a framework to test for gene-trait associations by integrating information ...
by limited statistical power and difficulties in biological interpretation. ...
ACKNOWLEDGEMENTS We thank Zhaolong Yu for suggestions and Michael Farruggia for English language polishing. L. H. acknowledges the following fundings: the ...
doi:10.15302/j-qb-020-0228
fatcat:tp34rkvrmzempevsqq37lh67gq
RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy
[article]
2022
arXiv
pre-print
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Nevertheless, how to combine them into a single multimodal framework is still an open issue. ...
The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. ...
Acknowledgements This work was partially founded by Università Campus Bio-Medico di Roma under the programme "University Strategic Projects", within the project "a CoLlAborative multi-sources Radiopathomics ...
arXiv:2204.12423v1
fatcat:6c375e3sfvetzmev4bwxqnqggq
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
[article]
2020
arXiv
pre-print
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In this work, we propose Pathomic Fusion, an interpretable strategy for end-to-end multimodal fusion of histology image and genomic (mutations, CNV, RNA-Seq) features for survival outcome prediction. ...
deep networks trained on histology and genomic data alone. ...
across modalities. omic Fusion, a novel framework for multimodal fusion of histology and genomic features (Fig. 1 ). ...
arXiv:1912.08937v3
fatcat:uruvdqhve5fu3e3amoce5pykmy
Heterogeneous data fusion for brain tumor classification
2012
Oncology Reports
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In this report, we present a novel machine learning framework for brain tumor classification based on heterogeneous data fusion of metabolic and molecular datasets, including state-of-the-art high-resolution ...
Our experimental results show that our novel framework outperforms any analysis using individual dataset. ...
The authors would like to thank Dr Peter Black for providing us with the biopsies and Dr Dionyssios Mintzopoulos for organizing the MRS data. ...
doi:10.3892/or.2012.1931
pmid:22842996
fatcat:sjh4yt6nfzeitihyrafv4uxokm
Variant interpretation through Bayesian fusion of frequency and genomic knowledge
2015
Genome Medicine
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Variant interpretation is a central challenge in genomic medicine. ...
A recent study demonstrates the power of Bayesian statistical approaches to improve interpretation of variants in the context of specific genes and syndromes. ...
Acknowledgements IMC is a fellow of the Baylor College of Medicine Medical Scientist Training Program (T32 GM007330) and was supported by a fellowship from the National Institute of Neurological Disorders ...
doi:10.1186/s13073-015-0129-3
pmid:25632303
pmcid:PMC4308929
fatcat:gakudogjfvcmnebumuqpfbza3m
Statistical algorithms improve accuracy of gene fusion detection
2017
Nucleic Acids Research
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In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, ...
These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions ...
We would like to acknowledge the support of the Stanford Center for Computational, Evolutionary and Human Genomics.
FUNDING ...
doi:10.1093/nar/gkx453
pmid:28541529
pmcid:PMC5737606
fatcat:ajg25lfodrajzd63kvvja2jsqq
Heterogeneous Data Fusion to Type Brain Tumor Biopsies
[chapter]
2009
IFIP Advances in Information and Communication Technology
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In this paper, we use machine learning algorithms to create a novel framework to perform the heterogeneous data fusion on both metabolic and molecular datasets, including state-of-the-art high-resolution ...
Our experimental results show our novel framework outperforms any analysis using individual dataset. ...
Since our framework is a general method, it can also be applied to any other biomedical and biological data fusion for sample classification and biomarker detection. ...
doi:10.1007/978-1-4419-0221-4_28
fatcat:i5yhlkv5prejtd5np5sdjduhli
MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features
[article]
2023
arXiv
pre-print
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of a spatially corresponding relationship between histopathology images and genomic molecular data; and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture ...
To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic ...
We started with a basic model (Model A) based on the simple concatenation of WSI and genomic data. Deep Fusion Strategy. ...
arXiv:2311.11659v1
fatcat:huujd7dwbvezpmvqxelzqnorfi
Findings from the Section on Bioinformatics and Translational Informatics
2017
IMIA Yearbook of Medical Informatics
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for cancer genomics and non-cancer complex diseases. ...
Methods: We provide a synopsis of the articles selected for the IMIA Yearbook 2017, from which we attempt to derive a synthetic overview of current and future activities in the field. ...
residues using a Bayes inference statistical framework. ...
doi:10.15265/iy-2016-050
pmid:27830252
pmcid:PMC5171571
fatcat:yf2ehgjj6bfwjm2ooniyr6a4pu
Findings from the Section on Bioinformatics and Translational Informatics
2017
IMIA Yearbook of Medical Informatics
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for cancer genomics and non-cancer complex diseases. ...
Methods: We provide a synopsis of the articles selected for the IMIA Yearbook 2017, from which we attempt to derive a synthetic overview of current and future activities in the field. ...
residues using a Bayes inference statistical framework. ...
doi:10.1055/s-0037-1606501
fatcat:fdjdngr3jnhxvnf7pr6z4k73ay
Improved detection of gene fusions by applying statistical methods reveals oncogenic RNA cancer drivers
2019
Proceedings of the National Academy of Sciences of the United States of America
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The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into ...
Here, we introduce Data-Enriched Efficient PrEcise STatistical fusion detection (DEEPEST), an algorithm that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion ...
We thank Steven Artandi for useful discussions, and members of J.S.'s laboratory for feedback on the manuscript. ...
doi:10.1073/pnas.1900391116
pmid:31308241
pmcid:PMC6681709
fatcat:mxzz5q3ihzbhtghuqnkeqjd3su
Inferring Personalized and Race-Specific Causal Effects of Genomic Aberrations on Gleason Scores: A Deep Latent Variable Model
2020
Frontiers in Oncology
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The core of the proposed model is a deep variational autoencoder (VAE) framework, which follows the causal structure of inference with proxies. ...
a genomic aberration may exert on the Gleason Score (GS) of each individual PCa patient. ...
For example, we find that for low-grade PCa, both AICEs and GRSs of AAs are statistically significantly higher than those of EAs over most of the studied genomic aberrations (i.e., ERG fusions, somatic ...
doi:10.3389/fonc.2020.00272
pmid:32231997
pmcid:PMC7082760
fatcat:t7yq5x75szcgxicubrot5l7hmy
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