In the ever-evolving landscape of biomedical research, understanding the relationship between drugs and diseases is paramount. The efficacy and safety of pharmaceutical agents hinge upon their interactions with various biological pathways. At CD ComputaBio, we provide cutting-edge drug-disease relationship analysis services that leverage advanced artificial intelligence (AI) methods to uncover these complex interactions. Our commitment is to assist pharmaceutical companies, researchers, and healthcare providers in making data-driven decisions that propel drug discovery and development.
Drug-disease relationship analysis enables researchers to:
At CD ComputaBio, we aim to bridge the gap between advanced bioinformatics and the pharmaceutical industry. Our cutting-edge services in drug-disease relationship analysis provide crucial insights that empower drug discovery, precision medicine, and therapeutic development.
Network Analysis
Understanding drug-disease interactions requires sophisticated analysis of biological networks. We provide services for building interaction networks, such as protein-protein interaction (PPI) networks and drug-target interaction (DTI) networks.
Predictive Modeling
Adverse Drug Reaction (ADR) Analysis
| Methods | Description |
|---|---|
| Network-Based Method | Network-based analysis is the most widely applied strategy for drug repositioning. The network includes PPI networks, KEGG, gene co-expression networks, and integrated networks, etc. Machine learning algorithms such as random forests, feed-forward neural networks, and graph neural networks have been used to find candidate drugs for diseases. |
| Data-Based Method | Prediction algorithms are proposed to predict the efficacies of drug combinations. These computational approaches focus on drug characteristics, fully accounting for disease factors by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. |
| Natural Language Processing (NLP) | NLP techniques are employed to extract valuable insights from vast amounts of unstructured text data, such as: Literature Mining: Identifying relevant research articles, clinical trial results, and ADR reports to inform our analysis. Sentiment Analysis: Gauging public perceptions and patient experiences to guide treatment strategies. |
AUC (Area under ROC curve) and AUPR (Area under the precision-recall curve) are popular metrics for evaluating prediction models. Since drug-disease pairs without associations are much more than known drug-disease associations, AUPR is adopted as the primary metric, which takes into recall and precision. Several binary classification metrics are also considered, i.e. sensitivity (SN, also known as recall), specificity (SP), accuracy (ACC) and F-measure (F).
Expertise in AI
Our diverse team of experts includes bioinformaticians, data scientists, and pharmacologists who bring cutting-edge knowledge in AI methodologies and drug discovery.
Advanced Technologies
CD ComputaBio utilizes the latest in technological advancements, including cloud computing, high-performance computing, evaluation metrics and advanced AI frameworks.
High Data Integrity
We prioritize the accuracy and reliability of our data sources. Our rigorous validation processes ensure that the insights you receive are built on credible and high-quality data.
At CD ComputaBio, we specialize in advanced drug-disease relationship analysis, aimed at enhancing pharmaceutical research and development. Our comprehensive suite of services combines cutting-edge technology with deep expertise to provide insights that drive innovation in drug discovery and precision medicine. If you are interested in our services or have any questions, please feel free to contact us.
Services