14,485 research outputs found
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. 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 data, class imbalance and scalability issues
Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study
Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model.
Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained.
Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches.
Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR
Data Imputation through the Identification of Local Anomalies
We introduce a comprehensive and statistical framework in a model free
setting for a complete treatment of localized data corruptions due to severe
noise sources, e.g., an occluder in the case of a visual recording. Within this
framework, we propose i) a novel algorithm to efficiently separate, i.e.,
detect and localize, possible corruptions from a given suspicious data instance
and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As
a generalization to Euclidean distance, we also propose a novel distance
measure, which is based on the ranked deviations among the data attributes and
empirically shown to be superior in separating the corruptions. Our algorithm
first splits the suspicious instance into parts through a binary partitioning
tree in the space of data attributes and iteratively tests those parts to
detect local anomalies using the nominal statistics extracted from an
uncorrupted (clean) reference data set. Once each part is labeled as anomalous
vs normal, the corresponding binary patterns over this tree that characterize
corruptions are identified and the affected attributes are imputed. Under a
certain conditional independency structure assumed for the binary patterns, we
analytically show that the false alarm rate of the introduced algorithm in
detecting the corruptions is independent of the data and can be directly set
without any parameter tuning. The proposed framework is tested over several
well-known machine learning data sets with synthetically generated corruptions;
and experimentally shown to produce remarkable improvements in terms of
classification purposes with strong corruption separation capabilities. Our
experiments also indicate that the proposed algorithms outperform the typical
approaches and are robust to varying training phase conditions
RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning
Anonymized electronic medical records are an increasingly popular source of
research data. However, these datasets often lack race and ethnicity
information. This creates problems for researchers modeling human disease, as
race and ethnicity are powerful confounders for many health exposures and
treatment outcomes; race and ethnicity are closely linked to
population-specific genetic variation. We showed that deep neural networks
generate more accurate estimates for missing racial and ethnic information than
competing methods (e.g., logistic regression, random forest). RIDDLE yielded
significantly better classification performance across all metrics that were
considered: accuracy, cross-entropy loss (error), and area under the curve for
receiver operating characteristic plots (all ). We made specific
efforts to interpret the trained neural network models to identify, quantify,
and visualize medical features which are predictive of race and ethnicity. We
used these characterizations of informative features to perform a systematic
comparison of differential disease patterns by race and ethnicity. The fact
that clinical histories are informative for imputing race and ethnicity could
reflect (1) a skewed distribution of blue- and white-collar professions across
racial and ethnic groups, (2) uneven accessibility and subjective importance of
prophylactic health, (3) possible variation in lifestyle, such as dietary
habits, and (4) differences in background genetic variation which predispose to
diseases
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