
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Июнь 19, 2024
Язык: Английский
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Июнь 19, 2024
Язык: Английский
Biomolecules, Год журнала: 2024, Номер 14(4), С. 409 - 409
Опубликована: Март 27, 2024
Proteins need to be located in appropriate spatiotemporal contexts carry out their diverse biological functions. Mislocalized proteins may lead a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where target protein resides within cell will give insights into tailored drug design for As the gold validation standard, conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, biomarker tags subcellular location identification. However, booming era proteomics high-throughput sequencing generates tons newly discovered proteins, making localization by wet-lab experiments mission impossible. To tackle this concern, past decades, artificial intelligence (AI) machine learning (ML), especially deep methods, have made significant progress research area. In article, we review latest advances AI-based method development three typical types approaches, including sequence-based, knowledge-based, image-based methods. We also elaborately discuss existing challenges future directions field.
Язык: Английский
Процитировано
4Опубликована: Март 4, 2024
Proteins need to be located in appropriate spatiotemporal contexts carry out their diverse biological functions. Mislocalized proteins may lead a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where target protein resides within cell will give insights into tailored drug design for As the gold validation standard, conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, biomarker tags subcellular location identification. However, booming era proteomics high-throughput sequencing generates tons newly discovered proteins, making subcel-lular localization by wet-lab experiments mission impossible. To tackle this concern, past decades, artificial intelligence (AI) machine learning (ML), especially deep methods, have made significant progress research area. In article, we review latest advances AI-based method development three typical types approaches, including sequence-based, knowledge-based, image-based methods. We also elaborately discuss existing challenges future directions field.
Язык: Английский
Процитировано
2International Immunopharmacology, Год журнала: 2024, Номер 140, С. 112827 - 112827
Опубликована: Авг. 8, 2024
Язык: Английский
Процитировано
1Ophthalmic Genetics, Год журнала: 2024, Номер unknown, С. 1 - 8
Опубликована: Ноя. 28, 2024
Introduction Classically, Usher syndrome is characterized by the association of sensorineural hearing loss (SNHL), retinitis pigmentosa (RP) and possible vestibular dysfunction. Pathogenic bi-allelic variants in CEP250 cause atypical autosomal recessive syndrome, which associated with SNHL photoreceptors 20 dysfunction without signs. To date, only 19 scattered descriptions have been reported. In this study, we present detailed clinical genetic description 7 unrelated individuals related disease, along a literature review to provide new insight on severity course disease.
Язык: Английский
Процитировано
1Biochemical Pharmacology, Год журнала: 2024, Номер 232, С. 116691 - 116691
Опубликована: Дек. 3, 2024
Язык: Английский
Процитировано
1Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Июнь 19, 2024
Язык: Английский
Процитировано
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