Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
Network pharmacology approach for medicinal plants: review and assessment
Natural products have played a critical role in medicine due to their ability to bind and
modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive …
modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive …
A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs
and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an …
and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an …
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
INTRODUCTION Our understanding of the pathophysiology of psychiatric disorders,
including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD) …
including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD) …
AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors
Abstract The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to
provide the most comprehensive and accurate information for animal transcription factors …
provide the most comprehensive and accurate information for animal transcription factors …
RNAInter v4. 0: RNA interactome repository with redefined confidence scoring system and improved accessibility
Establishing an RNA-associated interaction repository facilitates the system-level
understanding of RNA functions. However, as these interactions are distributed throughout …
understanding of RNA functions. However, as these interactions are distributed throughout …
iGRLDTI: an improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network
Motivation The task of predicting drug–target interactions (DTIs) plays a significant role in
facilitating the development of novel drug discovery. Compared with laboratory-based …
facilitating the development of novel drug discovery. Compared with laboratory-based …
[HTML][HTML] A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19
The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and …
respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and …
Super-enhancer RNA m6A promotes local chromatin accessibility and oncogene transcription in pancreatic ductal adenocarcinoma
R Li, H Zhao, X Huang, J Zhang, R Bai, L Zhuang… - Nature Genetics, 2023 - nature.com
The biological functions of noncoding RNA N 6-methyladenosine (m6A) modification remain
poorly understood. In the present study, we depict the landscape of super-enhancer RNA …
poorly understood. In the present study, we depict the landscape of super-enhancer RNA …
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 …