A Deep Learning Approach for Modelling the Complex Relationship between Environmental Factors and Biological Features
Tripathi, D.; Basu, A.
Show abstract
Environmental factors play a pivotal role in shaping the genetic and phenotypic diversity among organisms. Understanding the influence of the environment on a biological phenomenon is essential for deciphering the mechanisms resulting in trait differences among organisms. In this study, we present a novel approach utilizing an Artificial Neural Network (ANN) model to investigate the impact of environmental factors on a wide range of biological phenomena. Our proposed workflow includes hyperparameter optimization using model-based methods such as Bayesian and direct-search methods such as Random Search, and a new approach combining random search and linear models (RandomSearch+lm) to ensure a robust ANN architecture. Moreover, we employed a generalized version of the variable importance method to generate the feature importance metric using estimated weights from ANN. By applying this comprehensive ANN-based approach to functional genomics, we can gain valuable insights into the mechanisms underlying trait differentiation in organisms, while simultaneously enabling prediction and feature selection tasks. This methodology provides a robust and efficient framework for studying the complex relationships between environmental factors and biological features in biological systems.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Convergence, Sampling And Total Order Estimator Effects On Parameter Orthogonality In Global Sensitivity Analysis 94%
- CrossLabFit: A Novel Framework for Integrating Qualitative and Quantitative Data Across Multiple Labs for Model Calibration 94%
- Relating sparse/predictive coding to divisive normalization 94%
Similar papers in this journal
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 94%
- Adaptive time scales in recurrent neural networks 94%
- Robust Feature Selection for Cancer Microarray Data Using a Hybrid mRMR and Binary Lion Optimization Algorithm 93%
Similar papers in this journal
- Transfer Learning Models for Bacterial Strain Dissemination Biomarkers using Weighted Non-Parallel Proximal Support Vector Machines 94%
- Practical Identifiability in the Frame of Nonlinear Mixed Effects Models: the Example of the in vitro Erythropoiesis 94%
- GMMchi: Gene Expression Clustering Using Gaussian Mixture Modeling 94%
Similar papers in this journal
- Genomic prediction using machine learning: A comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data 94%
- Hierarchical non-negative matrix factorization using clinical information for microbial communities. 92%
- MOSCATO: A Supervised Approach for Analyzing Multi-Omic Single-Cell Data 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.