OncoMatch

-- Find the expert oncologist for personalized cancer therapies http://52.90.79.42

This project has 8 sections with code and detailed explanation in 8 jupyter notebooks.

• Part I: Web_scraping
  Obtaining data from Cancer.Net, Pubmed, scopus, clinicaltrials.gov, with methods including web scraping, API and Biopython.
• Part II: Abstract_preprocessing
  Obtaining cancer type information and gene information from abstracts. The aim is to annotate each abstract by cancer type and gene which are two critical features for the doctor recommendation system.
• Part III: Abstract_cancertype_annotation_LSTM
  Building LSTM models and training on the abstracts labeled with cancer type information. The aim is to predict cancer type for the abstracts with no cancer type information.
• Part IV: Abstract_keywords
  This notebook first generates dataframes and dictionaries to set up connections among oncologists, cancer types and abstract ids (pmid). Secondly, it visualizes keywords in abstracts for each cancer type.
• Part V: Word2Vec
  Training Word2Vec model to embed abstracts
• Part VI: LDA
  Training Latent Dirichlet Allocation (LDA) to embed abstracts.
• Part VII: BioBERT
  Using pre-trained weights from BioBERT to embed abstracts.
• Part VIII: Webapp
  The functions and steps used in the final webapp including both BioBERT and LDA methods.

Project Aim:

Match the needs of patients to abstracts published by oncologists based on similarity in semantic meanings which are encoded by word embedding.

Data Source:

1. Cancer.Net: https://www.cancer.net/
2. Pubmed: https://www.ncbi.nlm.nih.gov/pubmed/
3. ClinicalTrial: https://clinicaltrials.gov/

Here is a summary of some statistics of the data for this project:

Data type	Number of data
Abstracts	50930
Oncologists	1470
Cancer types	55
Genes	206
Cancer Centers	48

Approach:

Step 1 -- Annotate abstracts by cancer type

Around 83.4% of abstracts are annotated with cancer type by searching for cancer-related information and the remaining 16.6% are unlabeled.

Model -- LSTM

To annotate the unlabeled 16.6% abstracts, an LSTM model is built and trained on the labeled abstracts.

For each cancer type, an LSTM model is trained separately as a binary classification problem. Class_0 suggests the abstract doesn't contain information about the specific cancer type and Class_1 suggests that the abstract contains information about this cancer type.

Classification results

Here is the overall training performance for the top 29 most frequent cancer types

Since for each cancer type, the dataset is imbalanced (more Class_0 than Class_1), F1 score is used as the evaluation metric and the model with the highest F1 score is selected.

Here is the detailed training and prediction results for Breast Cancer:

After annotating the unlabeled abstracts, here is the overall cancer type information for the abstracts:

The keywords for each cancer type are visualized by word frequency and WordCloud.

Step 2 -- Word Embedding

Each abstract will be converted to a numeric vector that represents its semantic meanings.

Validation

To evaluate the word embedding performance, article titles are also converted to numeric vectors. The consine similarity scores between each pair of abstract and title are calculated and ranked. The better embedding performance suggests that the consine similarity score between each abstract and its corresponding title should have the highest ranking (percentile close to 0).

Embedding results

Here I tested Word2Vec, Latent Dirichlet Allocation (LDA) and BioBERT three models. The embedding performance is plotted as the distribution of cosine similarity score rankings between each abstract and its corresponding title (better performance corresponds to higher ranking and percentile closer to 0).

To better visualize the embedding results from BioBERT, here I generated TSNE plots of the embedded vectors of abstracts converted by BioBERT colored based on cancer types or gene mutation information.

Conclusion

BioBERT significantly outperforms Word2Vec and LDA models and I chose BioBERT to embed abstracts as well as the free-form text input from users.

Application

OncoMatch http://52.90.79.42

Users will provide information about Cancer type, Gene mutation (optional), Clinical Trials and a detailed description about the disease including medical records, family history and specific therapies, etc.

For example, a user is looking for oncologists specialized in melanoma treatment. Here if we entered the biography from a famous melanoma oncologist Dr. Jedd D. Wolchok. The number one ranked oncologist from OncoMatch is Dr. Jedd D. Wolchok.

OncoMatch will provide information about the oncologist's affiliation, location, a list of clinical trials and a list of most relevant publications based on the search record. OncoMatch will also give a list of top ranked oncoligsts that users can take a lookt at.

For visualization, the localization of abstracts about melanoma, abstracts published by Dr. Jedd D. Wolchok about melanoma and the text input are plotted in TSNE plot.