K Hyperparameter Tuning in High Dimensional Genomics Using Joint Optimization of Deep Diferential Evolutionary Algorithm and Unsupervised Transfer Learning from Intelligent Genoumap Embeddings
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Date
2024-07
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Journal ISSN
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Publisher
Int. j. inf. tecnol
Abstract
K-hyperparameter optimization in high-dimensional genomics remains a critical challenge, impacting the
quality of clustering. Improved quality of clustering can
enhance models for predicting patient outcomes and identifying personalized treatment plans. Subsequently, these
enhanced models can facilitate the discovery of biomarkers,
which can be essential for early diagnosis, prognosis, and
treatment response in cancer research. Our paper addresses
this challenge through a four-fold approach. Firstly, we
empirically evaluate the k-hyperparameter optimization
algorithms in genomics analysis using a correlation based
feature selection method and a stratifed k-fold cross-validation strategy. Secondly, we evaluate the performance of the
best optimization algorithm in the frst step using a variety of
the dimensionality reduction methods applied for reducing
the hyperparameter search spaces in genomics. Building on
the two, we propose a novel algorithm for this optimization
problem in the third step, employing a joint optimization
of Deep-Diferential-Evolutionary Algorithm and Unsupervised Transfer Learning from Intelligent GenoUMAP
(Uniform Manifold Approximation and Projection). Finally,
we compare it with the existing algorithms and validate its efectiveness. Our approach leverages UMAP pre-trained
special autoencoder and integrates a deep-diferential-evolutionary algorithm in tuning k. These choices are based
on empirical analysis results. The novel algorithm balances
population size for exploration and exploitation, helping to
fnd diverse solutions and the global optimum. The learning
rate balances iterations and convergence speed, leading to
stable convergence towards the global optimum. UMAP’s
superior performance, demonstrated by short whiskers and
higher median values in the comparative analysis, informs
its choice for training the special autoencoder in the new
algorithm. The algorithm enhances clustering by balancing
reconstruction accuracy, local structure preservation, and
cluster compactness. The comprehensive loss function optimizes clustering quality, promotes hyperparameter diversity, and facilitates efective knowledge transfer. This algorithm’s multi-objective joint optimization makes it efective
in genomics data analysis. The validation on this algorithm
on three genomic datasets demonstrates superior clustering
scores. Additionally, the convergence plots indicate relatively smoother curves and an excellent ftness landscape.
These fndings hold signifcant promise for advancing cancer
research and computational genomics at large
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Citation
Gikera, R., Maina, E., Mambo, S. M., & Mwaura, J. (2024). K-hyperparameter tuning in high-dimensional genomics using joint optimization of deep differential evolutionary algorithm and unsupervised transfer learning from intelligent GenoUMAP embeddings. International Journal of Information Technology, 1-23.