Over the last few decades, the publication rate has been massively increasing, resulting in a huge
number of available scientific documents, which consequently makes the search of relevant
information for a certain topic a heavy and time-consuming task. Biomedical Text Mining has
been addressing this problem for a while, but there is still space for improvements. For instance,
PubMed, which contains now more than 32 million citations, has only recently implemented a
machine learning model to improve document ranking, and is still trying to improve their system
by implementing a Deep Learning model, needing for now further studies.
Following this line of thought, a deep learning-based tool named BioTMPy was developed to
facilitate the search of relevant documents. BioTMPy is divided into separate modules that ease
distinct processes of a document relevance pipeline. More precisely, modules to load datasets in
different formats, convert them into distinct data structures, perform data analysis, and
implement several deep learning models with their associated methods to perform
hyperparameter optimization, cross validation, etc. Additionally, the package provides some
examples on how to integrate all the modules together to perform a complete pipeline for
document relevance.
To validate the developed pipelines, BioTMPy was later applied on a BioCreatives challenge from
2019. This challenge addressed the search of relevant documents for the topic of mining protein
interactions and mutations for precision medicine. With a comparison between different pretrained
embeddings, BioWordVec seemed to show on this data a slightly better performance over
GloVe, pubmed_pmc and pubmed_ncbi. Additionally, a model with a pre-trained BERT model
(BioBERT) and a Bi-LSTM managed to surpass the best challenges submission with a difference
of 7.25% for average precision and 3.15% for the f1-score.
In addition, a web service was implemented to provide an effortless use of the developed model,
allowing a user to order documents by relevance regarding the topic mentioned above. This
means that, after gathering a corpus, one can use BioTMPy to develop a pipeline to retrieve the
most relevant documents for a certain topic and consequently make it available to the public.info:eu-repo/semantics/publishedVersio
