Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances | Paper Citation Record

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Unresolved cited work

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Ma- chine learning applications in genetics and genomics

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Using rule-based machine learn- ing for candidate disease gene prioritization and sample classification of cancer gene expression data.PloS one, 7(7):e39932, 2012

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Deep learning for computational biology.Molecular systems biology, 12(7):878, 2016

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Applications of transformer- based language models in bioinformatics: a survey

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Foundation models in bioinformatics.National science review, 12(4):nwaf028, 2025

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Large language models for bioinformatics

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Generative models for protein se- quence modeling: recent advances and future directions

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Language models enable zero-shot prediction of the effects of mutations on pro- tein function.Advances in neural information process- ing systems, 34:29287–29303, 2021

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Gen- erative design of de novo proteins based on secondary- structure constraints using an attention-based diffusion model.Chem, 9(7):1828–1849, 2023

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Generative ai for synthetic data across multiple medical modalities: A systematic review of recent developments and chal- lenges.Computers in biology and medicine, 189:109834, 2025

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Progress and opportunities of foundation models in bioinformatics.Briefings in Bioinformatics, 25(6):bbae548, 2024

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Protein sequence analysis land- scape: A systematic review of task types, databases, datasets, word embeddings methods, and language models.Database, 2025:baaf027, 2025

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Dnabert: pre-trained bidirectional en- coder representations from transformers model for dna- language in genome.Bioinformatics, 37(15):2112–2120, 2021

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Dna language model grover learns se- quence context in the human genome.Nature Machine Intelligence, 6(8):911–923, 2024

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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances ‘bingo’—a large language model- and graph neural network-based workflow for the pre- diction of essential genes from protein data.Briefings in Bioinformatics, 25(1):bbad472, 2024

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Observation d646c9db-f67b-4b78-8e8d-ce0ee65da55e · outbound

This paper cites Clustering huge protein sequence sets in linear time.Nature com- munications, 9(1):2542, 2018.

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Clustering huge protein sequence sets in linear time.Nature com- munications, 9(1):2542, 2018

Reference 98

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Observation cd6b9eea-41d6-4cd7-86ea-7b1b8d01049a · outbound

This paper cites Gene3d: extensive prediction of globular domains in proteins.Nucleic acids research, 46(D1):D435–D439, 2018.

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Gene3d: extensive prediction of globular domains in proteins.Nucleic acids research, 46(D1):D435–D439, 2018

Reference 99

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Observation 7a683a40-18fc-4ec6-ad5b-7848f678ab88 · outbound

This paper cites Proteinnet: a standardized data set for machine learning of protein structure.BMC bioinformatics, 20(1):311, 2019.

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances Proteinnet: a standardized data set for machine learning of protein structure.BMC bioinformatics, 20(1):311, 2019

Reference 100

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