Predicting Results of Medical Experiments: An Overview
In the realm of healthcare and drug development, predicting the success or failure of clinical trials is a significant challenge. A new project aims to address this issue by leveraging advanced data embedding techniques and machine learning models.
The project, inspired by a previous study, will focus on embedding multi-modal clinical trial data for outcome prediction. This approach will utilise a combination of BioBERT, SBERT, DeepPurpose, and XGBoost, following the principles outlined in the "clinical trial embedding tutorial."
The first part of the project involves the collection and preprocessing of clinical trial records from ClinicalTrials.gov. The obtained XML files will be read and parsed to extract essential information such as disease indications, inclusion/exclusion criteria, sponsor information, and drug names.
For textual data, BioBERT or SBERT will be used to convert the extracted information into semantic embeddings that capture domain-specific language and trial contexts. For molecular data, DeepPurpose will be employed to convert drug chemical structures (SMILES) into vector embeddings using pretrained models tailored for bioactivity prediction.
After obtaining embeddings from each modality, they will be integrated and fused into a common latent space or concatenated into a joint multi-modal representation suitable for downstream models like XGBoost. This step aligns with the typical approach in multimodal models where embeddings from different encoders are combined to enable joint reasoning.
In the second part of the project, an XGBoost model will be trained to predict clinical trial outcomes based on these fused embeddings. The performance of the simple XGBoost model will be compared to the HINT model's performance from the article that inspired this project.
To encode molecular compounds, Morgan encoding from DeepPurpose will be used. Sponsor information will be encoded using all-MiniLM-L6-v2 from SentenceBERT. It is recommended to run this process in the command line due to its time- and space-consuming nature.
Wget needs to be installed for the process to run smoothly, and the project will utilise various tools such as BioBERT, SBERT, and DeepPurpose for data embedding. When trials include multiple indications, their mean will be taken as the vector representation. The sentence-transformers library will be used for information embedding.
The aim of this project is to predict a binary outcome: fail vs. success, using publicly available information from ClinicalTrials.gov. The process will create dictionaries that map SMILES to their Morgan representation and clinical trial identifiers (NCTIDs) directly to their Morgan representation.
In summary, the overall workflow is: Preprocess clinical trial data → generate modality-specific embeddings (BioBERT/SBERT for text, DeepPurpose for molecular) → project and fuse embeddings into a shared vector space → train XGBoost classifier on fused embeddings to predict outcomes. This innovative approach has the potential to revolutionise the way we predict clinical trial outcomes, ultimately expediting the development of new treatments and therapies.
- Applying the fusion of BioBERT, SBERT, DeepPurpose, and XGBoost, inspired by a clinical trial embedding tutorial, this project seeks to predict medical-conditions related outcomes for health-and-wellness, utilizing advanced data embedding techniques and machine learning models, particularly focusing on the predictive analysis of clinical trials.
- Following the completion of preprocessing clinical trial records from ClinicalTrials.gov, the project aims to leverage this integrated data in the realm of science, assembling a binary outcome prediction model for success or failure, utilizing a combination of semantic embeddings from BioBERT/SBERT, molecular embeddings from DeepPurpose, and the XGBoost classifier, thereby aiming to revolutionize the understanding and development of healthcare and drug development.
