Artificial intelligence in cancer research and precision medicine

B Bhinder, C Gilvary, NS Madhukar… - Cancer discovery, 2021 - aacrjournals.org
Artificial intelligence (AI) is rapidly resha** cancer research and personalized clinical
care. Availability of high-dimensionality datasets coupled with advances in high …

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

JPO Li, H Liu, DSJ Ting, S Jeon, RVP Chan… - Progress in retinal and …, 2021 - Elsevier
The simultaneous maturation of multiple digital and telecommunications technologies in
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …

Network-based machine learning approach to predict immunotherapy response in cancer patients

JH Kong, D Ha, J Lee, I Kim, M Park, SH Im… - Nature …, 2022 - nature.com
Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer
patients over the past several years. However, only a minority of patients respond to ICI …

Machine learning methods in drug discovery

L Patel, T Shukla, X Huang, DW Ussery, S Wang - Molecules, 2020 - mdpi.com
The advancements of information technology and related processing techniques have
created a fertile base for progress in many scientific fields and industries. In the fields of drug …

Gene expression based inference of cancer drug sensitivity

S Chawla, A Rockstroh, M Lehman, E Ratther… - Nature …, 2022 - nature.com
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer
and are responsible for imparting differential drug responses in cancer patients. Recently …

[HTML][HTML] Predicting drug response and synergy using a deep learning model of human cancer cells

BM Kuenzi, J Park, SH Fong, KS Sanchez, J Lee… - Cancer cell, 2020 - cell.com
Most drugs entering clinical trials fail, often related to an incomplete understanding of the
mechanisms governing drug response. Machine learning techniques hold immense promise …

Deep learning methods for drug response prediction in cancer: predominant and emerging trends

A Partin, TS Brettin, Y Zhu, O Narykov, A Clyde… - Frontiers in …, 2023 - frontiersin.org
Cancer claims millions of lives yearly worldwide. While many therapies have been made
available in recent years, by in large cancer remains unsolved. Exploiting computational …

Artificial intelligence and machine learning approaches for drug design: Challenges and opportunities for the pharmaceutical industries

C Selvaraj, I Chandra, SK Singh - Molecular diversity, 2022 - Springer
The global spread of COVID-19 has raised the importance of pharmaceutical drug
development as intractable and hot research. Develo** new drug molecules to overcome …

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 …

Cellular senescence and cardiovascular diseases: moving to the “heart” of the problem

K Evangelou, PVS Vasileiou… - Physiological …, 2023 - journals.physiology.org
Cardiovascular diseases (CVDs) constitute the prime cause of global mortality, with an
immense impact on patient quality of life and disability. Clinical evidence has revealed a …