[HTML][HTML] Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
Toward better drug discovery with knowledge graph
Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …
increasing data from existing chemical libraries and data banks. The knowledge graph is …
A review of approaches to identifying patient phenotype cohorts using electronic health records
C Shivade, P Raghavan… - Journal of the …, 2014 - academic.oup.com
Objective To summarize literature describing approaches aimed at automatically identifying
patients with a common phenotype. Materials and methods We performed a review of …
patients with a common phenotype. Materials and methods We performed a review of …
TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models
Drug–target interactions (DTIs) are central to current drug discovery processes and public
health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse …
health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse …
Identification of drug-side effect association via multiple information integration with centered kernel alignment
In medicine research, drug discovery aims to develop a drug to patients who will benefit from
it and try to avoid some side effects. However, the tradition experiment is time consuming …
it and try to avoid some side effects. However, the tradition experiment is time consuming …
[HTML][HTML] A review of data mining using big data in health informatics
M Herland, TM Khoshgoftaar, R Wald - Journal of Big data, 2014 - Springer
The amount of data produced within Health Informatics has grown to be quite vast, and
analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained …
analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained …
The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases
B Ozenne, F Subtil, D Maucort-Boulch - Journal of clinical epidemiology, 2015 - Elsevier
Objectives Compare the area under the receiver operating characteristic curve (AUC) vs. the
area under the precision–recall curve (AUPRC) in summarizing the performance of a …
area under the precision–recall curve (AUPRC) in summarizing the performance of a …
Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Abstract Objective Drug–drug interactions (DDIs) are an important consideration in both
drug development and clinical application, especially for co-administered medications …
drug development and clinical application, especially for co-administered medications …
Drug similarity integration through attentive multi-view graph auto-encoders
Drug similarity has been studied to support downstream clinical tasks such as inferring novel
properties of drugs (eg side effects, indications, interactions) from known properties. The …
properties of drugs (eg side effects, indications, interactions) from known properties. The …
[HTML][HTML] Predicting the frequencies of drug side effects
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in
humans. Currently, frequencies are experimentally determined in randomised controlled …
humans. Currently, frequencies are experimentally determined in randomised controlled …