Large Language Model and Large Vision Model for Life Sciences

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: 28 June 2024 | Viewed by 489

Special Issue Editor

Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
Interests: bioinformatics; genetics; genomics; machine learning; ceRNA network; predictive modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has profoundly changed the research paradigm of life sciences. Many Large Language Models (LLMs) inspired by ChatGPT have been built to accelerate biomedical studies. For example, GeneGPT can answer biomedical questions uses NCBI Web APIs and CancerGPT can predict drug pair synergy using large pre-trained language models.

Beside text data, there are many images and videos in biomedical researches, such as pathology images, light or electron microscopy cell images and videos. It is estimated that over one million digital pathology images are collected per day all over the USA. Many hospitals are sharing their large repositories. Large Vision Model (LVM) can be used to analyze such large volume image and video data.

Since LLM and LVM are trained on large volume data with large diversity, they are general models and can do a lot of tasks without too much sample and feature engineering. For language problems, we can build an NLP (Natural Language Processing) model to do translation and build another NLP model to fix grammar errors. But for LLM like ChatGPT which are trained on almost the entire Internet data, one model can do all these tasks. Similarly, we can train a traditional CV (Computer Vision) model between the culture plate images and the number of cells. But for another cell type with different shapes, we need to train another model. Now, we can train one LVM and give a prompt of a series of cell A growth images and few cell B growth images and ask the LVM model to predict in next image how many B cells will be.

Although it is still challenging to build LVM, several new approaches such as visual sentences have been proposed. The visual data can be represented as sequences, the LVM can be trained by minimizing the cross-entropy loss for next token prediction. Different vision tasks can be solved with suitable visual prompts.

The fusion of LLM and LVM makes the Large Multimodal Model (LMM) possible and eventually the Artificial General Intelligence (AGI) possible. We will understand its underlying meaning no matter we see a sentence or an image or a video.

Therefore, we invite multidisciplinary researchers to submit original articles, as well as review articles on this special issue. Potential topics of interest include, but are not limited to, the following:

  1. Large language model for single cell studies
  2. Large language model in protein design
  3. Large language model in High Throughput Drug screening/ discovery
  4. Large vision model for pathology images
  5. Large vision model for spatial omics images
  6. Large vision model for cancer cell growth and migration
  7. Large multimodal model for building digital twins of cancer progression
  8. Large vision model in High Throughput Drug screening/ discovery

Dr. Tao Huang
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • large language model
  • large vision model
  • images
  • videos
  • single cell
  • protein design
  • spatial omics
  • drug discovery
  • digital twin

Published Papers

This special issue is now open for submission.
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