A Review of Artificial Intelligence based Biological-Tree Construction: Priorities, Methods, Applications and Trends

• URL: http://arxiv.org/abs/2410.04815v1
• Date: Mon, 7 Oct 2024 08:00:41 GMT
• Title: A Review of Artificial Intelligence based Biological-Tree Construction: Priorities, Methods, Applications and Trends
• Authors: Zelin Zang, Yongjie Xu, Chenrui Duan, Jinlin Wu, Stan Z. Li, Zhen Lei,
• Abstract summary: Biological tree analysis serves as a pivotal tool in uncovering the evolutionary and differentiation relationships among organisms, genes, and cells. Traditional tree inference methods, while foundational in early studies, face increasing limitations in processing the large-scale, complex datasets. Recent advances in deep learning offer promising solutions, providing enhanced data processing and pattern recognition capabilities.
• Score: 43.12448177569722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biological tree analysis serves as a pivotal tool in uncovering the evolutionary and differentiation relationships among organisms, genes, and cells. Its applications span diverse fields including phylogenetics, developmental biology, ecology, and medicine. Traditional tree inference methods, while foundational in early studies, face increasing limitations in processing the large-scale, complex datasets generated by modern high-throughput technologies. Recent advances in deep learning offer promising solutions, providing enhanced data processing and pattern recognition capabilities. However, challenges remain, particularly in accurately representing the inherently discrete and non-Euclidean nature of biological trees. In this review, we first outline the key biological priors fundamental to phylogenetic and differentiation tree analyses, facilitating a deeper interdisciplinary understanding between deep learning researchers and biologists. We then systematically examine the commonly used data formats and databases, serving as a comprehensive resource for model testing and development. We provide a critical analysis of traditional tree generation methods, exploring their underlying biological assumptions, technical characteristics, and limitations. Current developments in deep learning-based tree generation are reviewed, highlighting both recent advancements and existing challenges. Furthermore, we discuss the diverse applications of biological trees across various biological domains. Finally, we propose potential future directions and trends in leveraging deep learning for biological tree research, aiming to guide further exploration and innovation in this field.

Related papers

• BioMaze: Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoning [49.487327661584686]
  We introduce BioMaze, a dataset with 5.1K complex pathway problems from real research. Our evaluation of methods such as CoT and graph-augmented reasoning, shows that LLMs struggle with pathway reasoning. To address this, we propose PathSeeker, an LLM agent that enhances reasoning through interactive subgraph-based navigation.
  arXiv  Detail & Related papers  (2025-02-23T17:38:10Z)
• Large Language Models for Bioinformatics [58.892165394487414]
  This survey focuses on the evolution, classification, and distinguishing features of bioinformatics-specific language models (BioLMs) We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities.
  arXiv  Detail & Related papers  (2025-01-10T01:43:05Z)
• Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models [51.316001071698224]
  We introduce Biology-Instructions, the first large-scale multi-omics biological sequences-related instruction-tuning dataset. This dataset can bridge the gap between large language models (LLMs) and complex biological sequences-related tasks. We also develop a strong baseline called ChatMultiOmics with a novel three-stage training pipeline.
  arXiv  Detail & Related papers  (2024-12-26T12:12:23Z)
• Causal Representation Learning from Multimodal Biological Observations [57.00712157758845]
  We aim to develop flexible identification conditions for multimodal data. We establish identifiability guarantees for each latent component, extending the subspace identification results from prior work. Our key theoretical ingredient is the structural sparsity of the causal connections among distinct modalities.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning [45.6771125432388]
  We introduce GENomic REpresentation with Language Model (GENEREL) GENEREL is a framework designed to bridge genetic and biomedical knowledge bases. Our experiments demonstrate GENEREL's ability to effectively capture the nuanced relationships between SNPs and clinical concepts.
  arXiv  Detail & Related papers  (2024-10-14T04:19:52Z)
• Generalized knowledge-enhanced framework for biomedical entity and relation extraction [0.6856896119187885]
  We develop a novel framework to construct a task-independent and reusable background knowledge graph for biomedical entity and relation extraction. The design of our model is inspired by how humans learn domain-specific topics. Our framework employs such common-knowledge-sharing mechanism to build a general neural-network knowledge graph that is learning transferable to different domain-specific biomedical texts effectively.
  arXiv  Detail & Related papers  (2024-08-13T04:06:45Z)
• Progress and Opportunities of Foundation Models in Bioinformatics [77.74411726471439]
  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.
  arXiv  Detail & Related papers  (2024-02-06T02:29:17Z)
• 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)
• PhyloGFN: Phylogenetic inference with generative flow networks [57.104166650526416]
  We introduce the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and phylogenetic inference. Because GFlowNets are well-suited for sampling complex structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-12T23:46:08Z)
• Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook [0.0]
  We examine the history, advantages, and challenges of deep learning in computational biology. Our focus is on two primary applications: DNA sequence classification and prediction, as well as protein structure prediction from sequence data.
  arXiv  Detail & Related papers  (2023-10-02T07:53:05Z)
• Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey [9.284385189718236]
  Bio-Inspired Deep Learning approaches towards advancing the computational capabilities and biological plausibility of current models. Recent bio-inspired training methods pose themselves as alternatives to backprop, both for traditional and spiking networks.
  arXiv  Detail & Related papers  (2023-07-30T13:57:25Z)
• GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies [0.3263412255491401]
  We introduce a novel, fully differentiable formulation of phylogenetic inference that leverages a unique representation of topological distributions in continuous geometric spaces. In experiments using real benchmark datasets, GeoPhy significantly outperformed other approximate Bayesian methods that considered whole topologies.
  arXiv  Detail & Related papers  (2023-07-07T15:45:05Z)
• Biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data [0.0]
  We provide a structured literature analysis focused on Deep Learning (DL) models used to support inference in cancer biology. The work focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability.
  arXiv  Detail & Related papers  (2022-07-02T12:11:35Z)
• Active Inference Tree Search in Large POMDPs [0.0]
  We introduce a novel method to plan in POMDPs--Active Inference Tree Search (AcT) AcT combines the normative character and biological realism of a leading planning theory in neuroscience (Active Inference) and the scalability of tree search methods in AI. Our simulations show that AcT successfully navigates binary trees that are challenging for sampling-based methods, problems that require adaptive exploration, and the large POMDP problem 'RockSample'--in which AcT reproduces state-of-the-art POMDP solutions.
  arXiv  Detail & Related papers  (2021-03-25T14:17:09Z)

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.