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 …
of massive amounts of omics data from multiple sources: genome, epigenome …
Computational methods for gene regulatory networks reconstruction and analysis: a review
FM Delgado, F Gómez-Vela - Artificial intelligence in medicine, 2019 - Elsevier
In the recent years, the vast amount of genetic information generated by new-generation
approaches, have led to the need of new data handling methods. The integrative analysis of …
approaches, have led to the need of new data handling methods. The integrative analysis of …
Gene regulatory network inference from sparsely sampled noisy data
The complexity of biological systems is encoded in gene regulatory networks. Unravelling
this intricate web is a fundamental step in understanding the mechanisms of life and …
this intricate web is a fundamental step in understanding the mechanisms of life and …
An extreme learning machine optimized by differential evolution and artificial bee colony for predicting the concentration of whole blood with Fourier Transform Raman …
Q Wang, S Song, L Li, D Wen, P Shan, Z Li… - Spectrochimica Acta Part …, 2023 - Elsevier
Raman spectroscopy, with its advantages of non-contact nature, rapid detection, and
minimum water interference, is promising for non-invasive blood detection or diagnosis in …
minimum water interference, is promising for non-invasive blood detection or diagnosis in …
Uncovering protein networks in cardiovascular proteomics
M Hasman, M Mayr, K Theofilatos - Molecular & Cellular Proteomics, 2023 - ASBMB
Biological networks have been widely used in many different diseases to identify potential
biomarkers and design drug targets. In the present review, we describe the main …
biomarkers and design drug targets. In the present review, we describe the main …
From gene to biomolecular networks: a review of evidences for understanding complex biological function in plants
Highlights•Biological function of genes is a complex interaction among genes and
environment.•PPIN, co-expression and GRN are system-level tools to study beyond single …
environment.•PPIN, co-expression and GRN are system-level tools to study beyond single …
Modeling regulatory networks using machine learning for systems metabolic engineering
Highlights•Systems metabolic engineering can benefit from regulatory network
models.•Regulatory network models should be more considered during strain development.• …
models.•Regulatory network models should be more considered during strain development.• …
A neuro-evolution approach to infer a Boolean network from time-series gene expressions
S Barman, YK Kwon - Bioinformatics, 2020 - academic.oup.com
In systems biology, it is challenging to accurately infer a regulatory network from time-series
gene expression data, and a variety of methods have been proposed. Most of them were …
gene expression data, and a variety of methods have been proposed. Most of them were …
Inference of gene regulatory networks based on the Light Gradient Boosting Machine
Inference of gene regulatory networks (GRNs) is one of the major challenges in molecular
biology, understanding of which can reveal the regulatory relationship between transcription …
biology, understanding of which can reveal the regulatory relationship between transcription …
CNNGRN: A convolutional neural network-based method for gene regulatory network inference from bulk time-series expression data
Z Gao, J Tang, J Xia, CH Zheng… - IEEE/ACM Transactions …, 2023 - ieeexplore.ieee.org
Gene regulatory networks (GRNs) participate in many biological processes, and
reconstructing them plays an important role in systems biology. Although many advanced …
reconstructing them plays an important role in systems biology. Although many advanced …