Deep learning methods for drug response prediction in cancer: predominant and emerging trends
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 …
available in recent years, by in large cancer remains unsolved. Exploiting computational …
A cross-study analysis of drug response prediction in cancer cell lines
To enable personalized cancer treatment, machine learning models have been developed
to predict drug response as a function of tumor and drug features. However, most algorithm …
to predict drug response as a function of tumor and drug features. However, most algorithm …
Converting tabular data into images for deep learning with convolutional neural networks
Convolutional neural networks (CNNs) have been successfully used in many applications
where important information about data is embedded in the order of features, such as …
where important information about data is embedded in the order of features, such as …
Anatomic radical prostatectomy: evolution of the surgical technique
PC Walsh - The Journal of urology, 1998 - auajournals.org
Purpose: Although radical prostatectomy provided excellent cancer control, it never gained
widespread popularity because of the major side effects of incontinence, impotence and …
widespread popularity because of the major side effects of incontinence, impotence and …
AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection
Protein-ligand docking is a computational method for identifying drug leads. The method is
capable of narrowing a vast library of compounds down to a tractable size for downstream …
capable of narrowing a vast library of compounds down to a tractable size for downstream …
A systematic literature review for the prediction of anticancer drug response using various machine‐learning and deep‐learning techniques
Computational methods have gained prominence in healthcare research. The accessibility
of healthcare data has greatly incited academicians and researchers to develop executions …
of healthcare data has greatly incited academicians and researchers to develop executions …
Integrating multi-omics using bayesian ridge regression with iterative similarity bagging
Cancer research has increasingly utilized multi-omics analysis in recent decades to obtain
biomolecular information from multiple layers, thereby gaining a better understanding of …
biomolecular information from multiple layers, thereby gaining a better understanding of …
Improving model transferability for clinical note section classification models using continued pretraining
Objective The classification of clinical note sections is a critical step before doing more fine-
grained natural language processing tasks such as social determinants of health extraction …
grained natural language processing tasks such as social determinants of health extraction …
DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques
Drug response classification constitutes a major challenge in personalized medicine. The
suitable drug selection for cancer patients is substantial and the drug response prediction is …
suitable drug selection for cancer patients is substantial and the drug response prediction is …
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A
primary challenge in modeling drug response prediction (DRP) with PDXs and neural …
primary challenge in modeling drug response prediction (DRP) with PDXs and neural …