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Artificial Intelligence for Molecular Biology: Advanced Methods and Applications
Hardcover
Medical ReferenceBiologyGeneral Computers
ISBN13: 9783031904530
Publisher: Springer
Published: Oct 1 2025
Pages: 609
Weight: 2.31
Height: 1.31 Width: 6.14 Depth: 9.21
Language: English
- Word Embedding Methods: This chapter explores the evolution of word embedding techniques, including foundational models like Word2Vec, FastText, and GloVe, as well as advanced graph-based embeddings such as DeepWalk, Node2Vec, and Struc2Vec. These embeddings have revolutionized sequence representation, providing powerful tools for analyzing biological data. Large Language Models: Language models have reshaped the landscape of computational biology. This chapter examines models like ULMFiT, BERT, and cutting-edge tools like AlphaFold and RNAFormer, which have set new benchmarks in structure prediction and sequence analysis. AI-Driven Insights into DNA Sequence Analysis Landscape: AI has unlocked new possibilities in DNA analysis. This chapter reviews methodologies, datasets, and predictive pipelines, offering insights into the performance and distribution of research across various benchmarks. AI-Driven Insights into RNA Sequence Analysis Landscape: RNA, with its unique roles and complexities, benefits significantly from AI approaches. This chapter investigates datasets, predictive pipelines, and performance metrics specific to RNA analysis. AI-Driven Insights into Protein Sequence Analysis Landscape: Proteins, central to numerous biological processes, are analyzed using AI-driven techniques. This chapter discusses embedding-based and language model-based methods, as well as the resources and benchmarks available for protein analysis. AI-Driven Revolution in Peptide Classification Landscape: Peptides, due to their diverse biological roles, pose unique challenges. This chapter provides a thorough examination of peptide classification, exploring AI methodologies, datasets, evaluation strategies, and the state-of-the-art performance of predictive models.
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