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# System prepended metadata

title: Chapter 2

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# Chapter 2

## [Chapter 2. Background and Related Work]{.page_break_before}{.center}


 
### 2.1  Background and Technologies

#### Background 
The problem we solved is how to connect the dots between gene-to-gene relationships by utilizing machine learning with data from biomedical research papers. Discovering and creating a relationship network between a mass amount of research papers would not only accelerate the research process but also make research more intuitive and fun.

#### Design Patterns 
For fast and intuitive implementation, we decided to design this system by implementing a market-ready machine learning model like BERT and GNormPlus for unsupervised learning with the intent to learn and encode our massive text data for vectorizing and mapping words in order to map relationships between one research document and another. For data / feature engineering pipeline, we will discuss in depth in the solution architecture section

#### Asynchronous Programming 
In order to implement our project aychoronously, we utilized the College of Engineerings HPC to run jobs using `srun` and `sbatch`. This method allowed us to run multiple programs at the same time to make significant progress without being tied up with processing power on our local machines. 

#### Project Estimation
The maximum number of data entries from the PubMed database is 34 million citations and abstracts of biomedical literature. While we did not intend to utilize this entire database, this proves that we have more than enough data points for training, validation, and testing cycles in our machine learning model. Our machine learning models are open source of BioBERT and GNormPlus for natural language processing and they were computed by the College of Engineering HPC.. Later on, we migrated the database to TSQL and utilized TigerGraph for graph representation of our data.

We estimated that the first model available would be mid-February 2023, and the production-level model would be be available by early May 2023 for showcasing and academic research purposes.

#### Scientific and Mathematical Models 
Our entire model idea is wrapped around the concept of word embedding, more specifically how to create a contextual representation of each research paper abstract and map these representations to know the nearest related paper to references and conduct research on similar topics. 
	
#### Technology

We collected data through HuggingFace Datasets such as `pubmed` and `pubmed-summarization`, use pre-trained models like BioBERT and GNormPlus for gene name recognition and relation extraction, HPC for data storage, and Tiger Graph for knowledge representation.
    
##### HuggingFace: 
Hugging Face is an open-source natural language processing library open to all ML models with support from libraries like Flair, Asteroid, ESPnet, Pyannote, and more to come. HuggingFace has a library called Datasets that allows quick loading and manipulation of large datasets as well as models available for fine-tuning.


##### BioBERT & GNormPlus: 
Bidirectional Encoder Representations (BERT) is a transformer-based machine learning technique for natural language processing pre-training developed by Google. This will be paired with a gene recognition tool and can be applied to PubMed to filter the results and apply operations to determine a weighted score for correlations between connected papers.
##### TigerGraph: 
A native parallel graph database purpose-built for loading massive amounts of data and analyzing their relationships to create connections between papers. 
##### High Performance Computation (HPC): 
Computing system available through San Jose State University with multi-core multi-socket servers, high performance in regards to storage, GPUs, and a significant amount of memory available on an interconnected network.
 
### 2.2  Literature Search

Literature searches in biological science and bioinformatics are more complicated than most fields because of the numerous and complex components like DNA, RNA, Gene, and Proteins. The article Bi-directional Methodology for Literature Extraction from PubMed Abstracts using Web Scrapper and Web Crawler applies a Bi-directional methodology to extract data that comes from the abstracts available on Pubmed obtained from a web scrapper and web crawler, then mapping the genes from that data to the Gene Card database [6]. The article also applies a second method that uses Gene Expression profiles that are analyzed with exploratory data analysis (EDA) to map the gene data extracted from Pubmed to a Gene Card database. Performing Principal Component Analysis (PCA) and clustering using k-means are used to group the differentially expressed genes. The experiment uses the web crawler called scrappy, available as a python library, to extract data from Pubmed’s abstracts. It then extracts the names of genes using regular expressions. The experiment targets lung cancer genes that are extracted from the data by comparing to a Gene Card database, then the Gene Profiles are analyzed and mapped to the Pubmed abstracts.
The article Building a PubMed knowledge graph explores how Pubmed abstracts can be used to create a knowledge graph [10]. For a Named Entity Recognition (NER) tool, researchers used a biomedical text mining tool called BioBERT based on Bidirectional Encoder Representations from Transformers (BERT) for bio-entity extraction. The data was then normalized using multi-type normalization, including GNormPlus for genes and proteins. Researchers also needed to extend multi-source information integration in order to build relationships to complete a comprehensive overview of the PubMed dataset. Data from NIH ExPORTER, ORCID, and fine-grained region and location information from the MapAffil 2016 was used to integrate data. The knowledge graph produced facilitates researcher-centric and bio-entity-centric activity.

Accurate identification of gene and protein names is an essential step in many commercially highly relevant applications, such as patent retrieval, prior art search, or patent classification [3]. Testing was run on the precision of tools in identifying correctly recognized gene names or identifiers by the total number of annotated gene entities or identifiers of a document averaged over a collection of documents. Based on their execution times, the ranking order from slow to fast is Gimli, GNormPlus, GeneTuKit, and GNAT. Results show that in a performance comparison between GNAT, Gimli, and GNormPlus, GNormPlus performs the best out of the rest when taking the recall and F-measure results (based on test accuracy), while GNAT performs better on precision values and runs faster.

The article Drug Repurposing Using TigerGraph & Graph Machine Learning uses the latest graph database and machine learning tools to create a Drug Repurposing Knowledge Graph (DRKG) by using repositioning, reprofiling redirection, and rediscovery in the investigation of existing drugs for new applications in medicine [8]. DRKG is a knowledge graph that links related genes, compounds, diseases, biological processes, side effects, and symptoms. The researchers used machine learning and a graph database TigerGraph to gain knowledge about drug repurposing, specifically targeting genes that are linked to a particular disease that interact within cells that are affected by genes that are targeted by other drugs with a focus on the biological components specific to Covid-19. The DRKG process consists of creating a TigerGraph schema, importing data in TSV format, querying the data, and then visualizing the graph and fine-tuning the model and predictions. The machine learning framework PyTorch was used for link prediction since researchers wanted to utilize six built-in datasets containing gene knowledge: DrugBank, Hetionet, GNBR, String, IntAct and DGIdb. The biggest challenge in the implementation of DRKG was time and resources since it was computationally heavy. In an effort to reduce time, researchers used pre-trained models and fine-tuned them to fit the specific purpose of discovering edges between compounds, disease, and gene-to-gene interactions that previously were not recognized. The graph database was also deployed on TigerGraph cloud and used REST interface on the large volume of data. Requirements included the integration of Nvidia for embedded graph machine learning, using Spark or Dask for processing to complete jobs, allowing for graph transformations and projections using tools such as Neo4j, and implementing the business requirements of the machine learning algorithms. 

### State-of-the-art
The USA National Center for Biotechnology Information (NCBI) contains information on 27.5 million journal articles. “The NCBI Nucleotide Database (including GenBank) had data for 243.3 million different sequences and dbSNP described 997.3 million different genetic variants” [11]. Pubmed entries can be searched using up to 272 thousand unique terms. The NCBI also provides access to a total of 50 databases through a web interface and online API. The eUtils API provides endpoints for searching each of the databases it covers and can find cross-references among those records and fetch particular records. This is just one of many reliable sources for biotechnology information and is the largest database covering this topic. 

GNAT and Gimli have been known as the baseline tools with the state-of-the-art performance on scientific articles [4]. GeneTuKit works using four main node modules. The first module is for gene mention recognition, the second is for gene ID candidate generation, the third is for gene ID disambiguation to distinguish between genes, and the fourth module is for generating a confidence score for each predicted gene ID. GNAT and GeneTuKit have reasonable execution times, when scaled up, GeneTuKit takes 9 times as long as GNAT. However, GNormPlus and Gimli take an impractical amount of time when scaled up. At the document level, GnormPlus performs the best; however, when scaling up then GNAT gives the most precision within a reasonable amount of time. 

To visually represent a knowledge graph, applications such as TigerGraph are used as a backend graph database to store the knowledge graph and the newly discovered relationships together with some graph machine learning techniques.TigerGraph has recently been used in a Drug Repurposing Knowledge Graph (DRKG) to generate visual representations for newly found gene relations. Drug Repurposing Knowledge Graph (DRKG) is a comprehensive biological knowledge graph relating genes, compounds, diseases, biological processes, side effects and symptoms.[10].  DRKG is freely available to import into our TigerGraph database for the general purpose of recreating new graphs for the gene of interest. 
