A comprehensive review on the emerging role of long non-coding RNAs in the regulation of NF-κB signaling in inflammatory lung diseases
Public health globally faces significant risks from conditions like acute respiratory distress
syndrome (ARDS), chronic obstructive pulmonary disease (COPD), and various …
syndrome (ARDS), chronic obstructive pulmonary disease (COPD), and various …
Artificial intelligence: practical primer for clinical research in cardiovascular disease
Artificial intelligence (AI) has begun to permeate and reform the field of medicine and
cardiovascular medicine. Impacting about 100 million patients in the United States, the …
cardiovascular medicine. Impacting about 100 million patients in the United States, the …
Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank
Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for
10–14% of postmarket withdrawals. In this study, we explored the capabilities of chemical …
10–14% of postmarket withdrawals. In this study, we explored the capabilities of chemical …
An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects
P Das, DH Mazumder - Artificial Intelligence Review, 2023 - Springer
Approved drugs for sale must be effective and safe, implying that the drug's advantages
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …
Application progress of organoids in colorectal cancer
L Luo, Y Ma, Y Zheng, J Su, G Huang - Frontiers in Cell and …, 2022 - frontiersin.org
Currently, colorectal cancer is still the third leading cause of cancer-related mortality, and the
incidence is rising. It is a long time since the researchers used cancer cell lines and animals …
incidence is rising. It is a long time since the researchers used cancer cell lines and animals …
Brmcf: Binary relevance and mlsmote based computational framework to predict drug functions from chemical and biological properties of drugs
In silico machine learning based prediction of drug functions considering the drug properties
would substantially enhance the speed and reduce the cost of identifying promising drug …
would substantially enhance the speed and reduce the cost of identifying promising drug …
Computational healthcare: Present and future perspectives
A Asai, M Konno, M Taniguchi… - Experimental and …, 2021 - spandidos-publications.com
Artificial intelligence (AI) has been developed through repeated new discoveries since
around 1960. The use of AI is now becoming widespread within society and our daily lives …
around 1960. The use of AI is now becoming widespread within society and our daily lives …
MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional …
P Das, DH Mazumder - ETRI Journal, 2024 - Wiley Online Library
To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been
approved. Due to the global pandemic status of COVID, several medicines are being …
approved. Due to the global pandemic status of COVID, several medicines are being …
Quantitative structure–activity relationship (QSAR) model for the severity prediction of drug-induced rhabdomyolysis by using random forest
Y Zhou, S Li, Y Zhao, M Guo, Y Liu, M Li… - Chemical Research in …, 2021 - ACS Publications
Drug-induced rhabdomyolysis (DIR) is a rare and potentially life-threatening muscle injury
that is characterized by low incidence and high risk. To our best knowledge, the …
that is characterized by low incidence and high risk. To our best knowledge, the …
NDDSA: A network-and domain-based method for predicting drug-side effect associations
S Shabani-Mashcool, SA Marashi… - Information Processing & …, 2020 - Elsevier
Finding side effects of drugs, before reaching the animal and clinical test, can decrease the
cost and time of developing new drugs. Also, a lot of side effects are reported after going to …
cost and time of developing new drugs. Also, a lot of side effects are reported after going to …