Navigating the pitfalls of applying machine learning in genomics
The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …
Graph neural network approaches for drug-target interactions
Develo** new drugs remains prohibitively expensive, time-consuming, and often involves
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
DeepTraSynergy: drug combinations using multimodal deep learning with transformers
Motivation Screening bioactive compounds in cancer cell lines receive more attention.
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Circular RNAs and complex diseases: from experimental results to computational models
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules
with a variety of biological functions. Studies have shown that circRNAs are involved in a …
with a variety of biological functions. Studies have shown that circRNAs are involved in a …
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 …
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
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 …
DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Motivation Drug discovery demands rapid quantification of compound–protein interaction
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …
Predicting miRNA–disease association based on inductive matrix completion
X Chen, L Wang, J Qu, NN Guan, JQ Li - Bioinformatics, 2018 - academic.oup.com
Motivation It has been shown that microRNAs (miRNAs) play key roles in variety of
biological processes associated with human diseases. In Consideration of the cost and …
biological processes associated with human diseases. In Consideration of the cost and …