Deep learning in drug discovery: an integrative review and future challenges
H Askr, E Elgeldawi, H Aboul Ella… - Artificial Intelligence …, 2023 - Springer
Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …
Deep learning for drug repurposing: Methods, databases, and applications
Drug development is time‐consuming and expensive. Repurposing existing drugs for new
therapies is an attractive solution that accelerates drug development at reduced …
therapies is an attractive solution that accelerates drug development at reduced …
Treatment response prediction in hepatitis C patients using machine learning techniques
AA Kashif, B Bakhtawar, A Akhtar… - International Journal …, 2021 - journals.gaftim.com
The proper prognosis of treatment response is crucial in any medical therapy to reduce the
effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus …
effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus …
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …
innovatiaon and impact. However, advancement in this field requires formulation of …
Multi-disease prediction based on deep learning: a survey
S Xie, Z Yu, Z Lv - Computer Modeling in Engineering & …, 2021 - ingentaconnect.com
In recent years, the development of artificial intelligence (AI) and the gradual beginning of
AI's research in the medical field have allowed people to see the excellent prospects of the …
AI's research in the medical field have allowed people to see the excellent prospects of the …
[HTML][HTML] Predicting drug response and synergy using a deep learning model of human cancer cells
Most drugs entering clinical trials fail, often related to an incomplete understanding of the
mechanisms governing drug response. Machine learning techniques hold immense promise …
mechanisms governing drug response. Machine learning techniques hold immense promise …
BioRED: a rich biomedical relation extraction dataset
Automated relation extraction (RE) from biomedical literature is critical for many downstream
text mining applications in both research and real-world settings. However, most existing …
text mining applications in both research and real-world settings. However, most existing …
A review of deep learning applications in human genomics using next-generation sequencing data
WS Alharbi, M Rashid - Human Genomics, 2022 - Springer
Genomics is advancing towards data-driven science. Through the advent of high-throughput
data generating technologies in human genomics, we are overwhelmed with the heap of …
data generating technologies in human genomics, we are overwhelmed with the heap of …
Biology and medicine in the landscape of quantum advantages
BA Cordier, NPD Sawaya… - Journal of the …, 2022 - royalsocietypublishing.org
Quantum computing holds substantial potential for applications in biology and medicine,
spanning from the simulation of biomolecules to machine learning methods for subtyping …
spanning from the simulation of biomolecules to machine learning methods for subtyping …
[HTML][HTML] Machine learning and deep learning methods that use omics data for metastasis prediction
Knowing metastasis is the primary cause of cancer-related deaths, incentivized research
directed towards unraveling the complex cellular processes that drive the metastasis …
directed towards unraveling the complex cellular processes that drive the metastasis …