4,690 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
RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
Extreme learning machine (ELM) as an emerging branch of shallow networks has
shown its excellent generalization and fast learning speed. However, for
blended data, the robustness of ELM is weak because its weights and biases of
hidden nodes are set randomly. Moreover, the noisy data exert a negative
effect. To solve this problem, a new framework called RMSE-ELM is proposed in
this paper. It is a two-layer recursive model. In the first layer, the
framework trains lots of ELMs in different groups concurrently, then employs
selective ensemble to pick out an optimal set of ELMs in each group, which can
be merged into a large group of ELMs called candidate pool. In the second
layer, selective ensemble is recursively used on candidate pool to acquire the
final ensemble. In the experiments, we apply UCI blended datasets to confirm
the robustness of our new approach in two key aspects (mean square error and
standard deviation). The space complexity of our method is increased to some
degree, but the results have shown that RMSE-ELM significantly improves
robustness with slightly computational time compared with representative
methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential
framework to solve robustness issue of ELM for high-dimensional blended data in
the future.Comment: Accepted for publication in Mathematical Problems in Engineering,
09/22/201
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification
Stacking-based deep neural network (S-DNN) is aggregated with pluralities of
basic learning modules, one after another, to synthesize a deep neural network
(DNN) alternative for pattern classification. Contrary to the DNNs trained end
to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable
module, is to be trained decisively and independently without BP intervention.
In this paper, a ridge regression-based S-DNN, dubbed deep analytic network
(DAN), along with its kernelization (K-DAN), are devised for multilayer feature
re-learning from the pre-extracted baseline features and the structured
features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by
perturbing the intra/inter-class variations, apart from diminishing the
prediction errors. We scrutinize the DAN/K-DAN performance for pattern
classification on datasets of varying domains - faces, handwritten digits,
generic objects, to name a few. Unlike the typical BP-optimized DNNs to be
trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable
using only CPU even for small-scale training sets. Our experimental results
disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained
DNNs, including multiplayer perceptron, deep belief network, etc., without data
augmentation applied.Comment: 14 pages, 7 figures, 11 table
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