Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …
process of drug discovery. There is a need to develop novel and efficient prediction …
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
DeepDTA: deep drug–target binding affinity prediction
Motivation The identification of novel drug–target (DT) interactions is a substantial part of the
drug discovery process. Most of the computational methods that have been proposed to …
drug discovery process. Most of the computational methods that have been proposed to …
Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Motivation In bioinformatics, machine learning-based methods that predict the compound–
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …
and chemogenomics studies, and proteins without three-dimensional structure account for a …
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models
Since the problem proposed in late 2000s, microRNA–disease association (MDA)
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
[HTML][HTML] Deep learning in virtual screening: recent applications and developments
TB Kimber, Y Chen, A Volkamer - International journal of molecular …, 2021 - mdpi.com
Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …
methods, such as virtual screening, to speed up and guide the design of new compounds …
[HTML][HTML] A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
The emergence of large-scale genomic, chemical and pharmacological data provides new
opportunities for drug discovery and repositioning. In this work, we develop a computational …
opportunities for drug discovery and repositioning. In this work, we develop a computational …
Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder
W Liu, H Lin, L Huang, L Peng, T Tang… - Briefings in …, 2022 - academic.oup.com
Increasing evidences show that the occurrence of human complex diseases is closely
related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease …
related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease …
[HTML][HTML] MDHGI: matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction
Recently, a growing number of biological research and scientific experiments have
demonstrated that microRNA (miRNA) affects the development of human complex diseases …
demonstrated that microRNA (miRNA) affects the development of human complex diseases …