Progress and Opportunities of Foundation Models in Bioinformatics

• URL: http://arxiv.org/abs/2402.04286v1
• Date: Tue, 6 Feb 2024 02:29:17 GMT
• Title: Progress and Opportunities of Foundation Models in Bioinformatics
• Authors: Qing Li, Zhihang Hu, Yixuan Wang, Lei Li, Yimin Fan, Irwin King, Le Song, Yu Li
• Abstract summary: Foundations models (FMs) have ushered in a new era in computational biology, especially in the realm of deep learning. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. Review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases.
• Score: 77.74411726471439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI), particularly through the adoption of foundation models (FMs). These AI techniques have rapidly advanced, addressing historical challenges in bioinformatics such as the scarcity of annotated data and the presence of data noise. FMs are particularly adept at handling large-scale, unlabeled data, a common scenario in biological contexts due to the time-consuming and costly nature of experimentally determining labeled data. This characteristic has allowed FMs to excel and achieve notable results in various downstream validation tasks, demonstrating their ability to represent diverse biological entities effectively. Undoubtedly, FMs have ushered in a new era in computational biology, especially in the realm of deep learning. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. We delve into the specifics of the problem at hand including sequence analysis, structure prediction, function annotation, and multimodal integration, comparing the structures and advancements against traditional methods. Furthermore, the review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases. Finally, we outline potential development paths and strategies for FMs in future biological research, setting the stage for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also as a roadmap for future explorations and applications of FMs in biology.

Related papers

• A Comprehensive Survey of Foundation Models in Medicine [8.879092631568263]
  Foundation models (FMs) are large-scale deep-learning models trained on extensive datasets using self-supervised techniques. We focus on the history, learning strategies, flagship models, applications, and challenges of FMs in healthcare.
  arXiv  Detail & Related papers  (2024-06-15T20:04:06Z)
• Simplicity within biological complexity [0.0]
  We survey the literature and argue for the development of a comprehensive framework for embedding of multi-scale molecular network data. Network embedding methods map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation.
  arXiv  Detail & Related papers  (2024-05-15T13:32:45Z)
• Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare [14.399086205317358]
  Foundation models (FMs) are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback. These models are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data.
  arXiv  Detail & Related papers  (2024-05-10T19:22:24Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs [27.32543389443672]
  We present BioBridge, a novel parameter-efficient learning framework to bridge independently trained unimodal FMs to establish multimodal behavior. Our empirical results demonstrate that BioBridge can beat the best baseline KG embedding methods. We also identify BioBridge demonstrates out-of-domain generalization ability by extrapolating to unseen modalities or relations.
  arXiv  Detail & Related papers  (2023-10-05T05:30:42Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.