Machine learning for the advancement of genome-scale metabolic modeling
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the
interrelations between genotype, phenotype, and external environment. The recent …
interrelations between genotype, phenotype, and external environment. The recent …
Latent representation learning in biology and translational medicine
A Kopf, M Claassen - Patterns, 2021 - cell.com
Current data generation capabilities in the life sciences render scientists in an apparently
contradicting situation. While it is possible to simultaneously measure an ever-increasing …
contradicting situation. While it is possible to simultaneously measure an ever-increasing …
Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation
Despite the availability of chromatin conformation capture experiments, discerning the
relationship between the 1D genome and 3D conformation remains a challenge, which …
relationship between the 1D genome and 3D conformation remains a challenge, which …
Searching for protein variants with desired properties using deep generative models
Y Li, Y Yao, Y Xia, M Tang - BMC bioinformatics, 2023 - Springer
Background Protein engineering aims to improve the functional properties of existing
proteins to meet people's needs. Current deep learning-based models have captured …
proteins to meet people's needs. Current deep learning-based models have captured …
VirusBERTHP: Improved Virus Host Prediction Via Attention-based Pre-trained Model Using Viral Genomic Sequences
Y Wang, J Yang, Y Cai - 2023 IEEE International Conference …, 2023 - ieeexplore.ieee.org
Virus has become the most prominent cause of infectious diseases which greately threaten
human health. Determining whether a viral genome can possess human host infectivity …
human health. Determining whether a viral genome can possess human host infectivity …
Machine Learning in Genomics
Abstract Machine learning (ML) techniques are implemented for handling and analyzing the
large genomics datasets to address complex biological problems. It comprises …
large genomics datasets to address complex biological problems. It comprises …
Evaluation of distributed DNA representations on the classification of conserved non-coding elements
N Gialitsis, G Giannakopoulos… - 11th Hellenic Conference …, 2020 - dl.acm.org
The representation of DNA sequences has been an interesting topic of discussion for many
years. Presently, given the usefulness of representations built upon embeddings for Natural …
years. Presently, given the usefulness of representations built upon embeddings for Natural …
Towards a robust out-of-the-box neural network model for genomic data
Z Zhang, S Cheng, C Solis-Lemus - BMC bioinformatics, 2022 - Springer
Background The accurate prediction of biological features from genomic data is paramount
for precision medicine and sustainable agriculture. For decades, neural network models …
for precision medicine and sustainable agriculture. For decades, neural network models …
Applying Deep Learning to Discover Highly Functionalized Nucleic Acid Polymers That Bind to Small Molecules
M Wornow - 2020 - dash.lib.harvard.edu
Developing novel binders for small molecule and protein targets has been at the core of a
number of recent medical breakthroughs. A popular developmental method focuses on …
number of recent medical breakthroughs. A popular developmental method focuses on …
[图书][B] Understanding the Geometry of Structured Vectorized Representations
PO Aboagye - 2023 - search.proquest.com
In machine learning and deep learning paradigms, high-dimensional vectorized
embeddings have emerged as a powerful and useful method for representing structured …
embeddings have emerged as a powerful and useful method for representing structured …