DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network
Abstract Analysis and prediction of drug-target interactions (DTIs) play an important role in
understanding drug mechanisms, as well as drug repositioning and design. Machine …
understanding drug mechanisms, as well as drug repositioning and design. Machine …
Protein encoder: An autoencoder-based ensemble feature selection scheme to predict protein secondary structure
U Manzoor, Z Halim - Expert Systems with Applications, 2023 - Elsevier
Proteins play a vital role in the human body as they perform important metabolic tasks.
Experimental identification of protein structure is expensive and time consuming. The …
Experimental identification of protein structure is expensive and time consuming. The …
DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system
L Yuan, X Hu, Y Ma, Y Liu - RSC advances, 2022 - pubs.rsc.org
Protein secondary structure prediction (PSSP) is not only beneficial to the study of protein
structure and function but also to the development of drugs. As a challenging task in …
structure and function but also to the development of drugs. As a challenging task in …
Protein secondary structure prediction using data-partitioning combined with stacked convolutional neural networks and bidirectional gated recurrent units
Protein secondary structure prediction (PSSP) is one of the challenging tasks in
computational biology. Deep neural networks have significantly improved the accuracy of …
computational biology. Deep neural networks have significantly improved the accuracy of …
IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction
There is a close relationship between the tertiary structure and the function of a protein. One
of the important steps to determine the tertiary structure is protein secondary structure …
of the important steps to determine the tertiary structure is protein secondary structure …
Protein secondary structure prediction using a lightweight convolutional network and label distribution aware margin loss
W Yang, Z Hu, L Zhou, Y Jin - Knowledge-Based Systems, 2022 - Elsevier
Protein secondary structure prediction (PSSP) is an important task in computational
molecular biology. Recently, deep neural networks have demonstrated great potential in …
molecular biology. Recently, deep neural networks have demonstrated great potential in …
Protein secondary structure prediction using cascaded feature learning model
The protein secondary structure prediction (PSSP) is pivotal for predicting tertiary structure,
which is proliferating in demand for drug design and development. Further, it can be used to …
which is proliferating in demand for drug design and development. Further, it can be used to …
[PDF][PDF] Integrating artificial intelligence in disease diagnosis, treatment, and formulation development: a review
Artificial intelligence (AI) is rapidly advancing and significantly impacting clinical care and
treatment. Machine learning and deep learning, as core digital AI technologies, are being …
treatment. Machine learning and deep learning, as core digital AI technologies, are being …
Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory
L Yuan, Y Ma, Y Liu - Frontiers in Bioengineering and Biotechnology, 2023 - frontiersin.org
Protein secondary structure prediction (PSSP) is a challenging task in computational
biology. However, existing models with deep architectures are not sufficient and …
biology. However, existing models with deep architectures are not sufficient and …
IGPRED-MultiTask: a deep learning model to predict protein secondary structure, torsion angles and solvent accessibility
Protein secondary structure, solvent accessibility and torsion angle predictions are
preliminary steps to predict 3D structure of a protein. Deep learning approaches have …
preliminary steps to predict 3D structure of a protein. Deep learning approaches have …