Top 7 Biomedical Engineering Tools & Techniques

An illustration of people working on different tools.

Biomedical engineering tools and techniques have transformed the field of biomedical research by enabling practitioners to get insights from complex medical data that are otherwise hard to attain. Through the use of these tools, biomedical researchers can model and analyze medical data with much better accuracy, ultimately leading to the development of cutting-edge treatments and technologies.

With the healthcare industry continuously changing and becoming more data-centric, biomedical engineering tools and techniques have become a significant resource for biomedical researchers eying to make new advancements in the field. In this article, we will look at the top 7 biomedical engineering tools and techniques that have become quite essential.

What Are Biomedical Engineering Tools? 

Biomedical engineering tools and techniques encompass a diverse range of applications that are used in the design and development of broad-ranging medical solutions. They can range from simulation software for developing virtual models of biological systems to image processing tools that facilitate the analysis of MRIs, and X-rays to data analysis packages for mining large medical datasets and identifying relevant trends and patterns from them. The use of these tools across different application areas helps advance biomedical engineering and enhance overall public health.

Top 7 Biomedical Engineering Tools

1. Wisecube Orpheus

Orpheus is a powerful software tool that is used to uncover novel biomedical insights through extensive data analysis and exploration. Orpheus is the largest biomedical knowledge graph of its kind, consisting of tens of billions of biomedical facts on a whopping 85 million entities. It accumulates biomedical information using cutting-edge AI techniques and expert curation to develop a state-of-the-art biomedical knowledge base. 

Orpheus is particularly useful for biomedical researchers and experts who need to access and analyze large-scale biomedical datasets. By integrating and harmonizing data from various sources, users can gain insights into complex biomedical relationships and discover new patterns and trends.

Features

  • Target identification and drug repositioning by using causal biomedical relationships
  • Inferring facts from research articles using NLP techniques
  • Identifying biomarkers using Graph AI
  • Enabling flexible querying in the knowledge graph without any query language
  • Reveal hidden biomedical relationships and identify and prioritize hypotheses

If you wish to leverage the insights from the biggest biomedical knowledge graph to fuel your AI-driven drug discovery and accelerate your biomedical research, you can sign up for a free trial with Wisecube.

2. Hetionet

Two hetnet visualizations with nodes and edges

Hetionet is a heterogeneous information network that contains multiple node and edge relationship types, allowing it to accurately describe more complex biomedical data. In biomedicine, Hetionet is useful for researchers in identifying the differences between medical components such as genes and diseases. It combines information from a collection of publicly available 29 databases and consists of 47,031 nodes and 2,250,197 edges, in total. 

Features

  • Enabling biomedical experts to formulate a hypothesis and make accurate predictions
  • Encodes relationships from millions of medical studies conducted over the last 50 years
  • Capturing conceptual differences between biomedical entities like drugs and diseases
  • Used in drug repurposing and for prioritizing disease-associated genes

Hetionet is available in four formats (JSON, Neo4j, TSV, matrix) and can be downloaded from the GitHub repository.

3. Biolink Model

Biolink Model is a data model that represents biological entities (genes, diseases, pathways, etc), their characteristics, and their underlying associations. It does not depend on a specific graph representation or a storage technology to represent this biological knowledge such as Neo4j or RDF etc. The data model was developed to standardize the way biological information is represented, utilizing precisely defined semantics and linking the model to external sources such as ontologies and research articles. 

Features

  • Provides a set of qualifiers, such as “has_evidence”, “is_defined_by”, and “provided_by”, for adding additional context to entity and relationship statements
  • Designed to be extensible and allows for the addition of new entity types, relationships, and attributes as and when needed

Biomedical researchers can use Biolink Model’s GitHub repository to understand, install, curate, and work with the data model.

4. KnowEnG

Knowledge Engine for Genomics (KnowEnG) is an educational web resource and a software environment that aims to transform the way biomedical researchers process and analyze genome-related data. The Knowledge Engine platform attempts to integrate multiple bioinformatics knowledge bases and then apply advanced analytical and data mining methods to it. 

Features

  • Providing computational methods for analyzing genome-related datasets
  • Offering analytical techniques that are scalable and can be deployed on a cloud environment
  • Offering a visualization platform that illustrates interactive data analysis of user-provided datasets 
  • Creating interactive educational modules and tutorials on key biomedical concepts to train students and researchers 

You can also have a look at their GitHub profile where they have shared repositories to enable genomic analysis and visualization.  

5. BioBERT

An overview of BioBERT framework

BioBERT is a biomedical language model that has been pre-trained on a large collection of biomedical text. The model provides a range of capabilities for natural language processing and understanding in the biomedical domain. With minimal modifications required for specific tasks, BioBERT has demonstrated remarkable performance in various biomedical text mining activities, such as named entity recognition and relation extraction. As a result, it has proven to be highly effective for medical professionals seeking to extract meaningful information from biomedical texts. 

Features

  • Fine-tuning capabilities for customized tasks and applications
  • Support for a range of downstream tasks such as named entity recognition, question answering, and relation extraction
  • Available as a pre-trained model or for customized training on specific datasets

Biomedicine providers and scientists can use BioBERT’s GitHub repository to install and use the language representation model.

6. PubTator

PubTator is a web-based tool for annotating and exploring biomedical literature. The tool provides a range of features and capabilities to explore biomedical concepts in more than 30 million biomedical publications on PubMed and PMC. The following bio concepts are supported by PubTator: genes, diseases, chemicals, mutations, species, and cell lines. Biomedical experts can export pre-annotated bio concepts in different formats and can also get access to annotated data through automated computer programming scripts.

Features

  • Leveraging PubTator annotations to perform semantic search
  • Using deep learning techniques for concept disambiguation
  • Easy navigation to entities of interest through an in-document search
  • Publications can be organized into collections, and then be viewed/downloaded together
  • Can be accessed for free through a web browser or an API

PubTator can be accessed by biomedical researchers from its official website.

7. OpenBEL

OpenBEL is an open-source technology that uses Biological Expression Language (BEL) to collect and store biomedical knowledge. The OpenBEL framework is designed to enable the integration of information from various biomedical vocabularies and ontologies, allowing organizations to integrate information from multiple sources into a single knowledge repository. This combined knowledge can, in turn, be used by biomedical experts in decision support systems or analytical applications. 

Features

  • Merging biomedical knowledge from multiple independent sources
  • Forming custom biological networks from the centralized knowledge repository
  • Enabling applications to query biomedical knowledge using a web browser and APIs

The OpenBEL framework is open-platform and can be accessed by biomedical experts from OpenBEL’s GitHub repository.

We hope that this list helps biomedical researchers understand the overall ecosystem of biomedical engineering tools. If you want to know more about Wisecube’s biomedical knowledge graph (Orpheus), you can schedule a demo to understand how Orpheus can facilitate scientific discovery and power R&D in your particular use case.

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