AIMC Topic: Proteomics

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Personal Dense Dynamic Data Clouds Connect Systems Biomedicine to Scientific Wellness.

Methods in molecular biology (Clifton, N.J.)
The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holisti...

Graph representation learning for structural proteomics.

Emerging topics in life sciences
The field of structural proteomics, which is focused on studying the structure-function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storin...

Parameters to identify good quality oocytes and embryos in cattle.

Reproduction, fertility, and development
Oocyte/embryo selection methodologies are either invasive or noninvasive and can be applied at various stages of development from the oocyte to cleaved embryos and up to the blastocyst stage. Morphology and the proportion of embryos developing to the...

Deep Learning Proteins using a Triplet-BERT network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Modern sequencing technology has produced a vast quantity of proteomic data, which has been key to the development of various deep learning models within the field. However, there are still challenges to overcome with regards to modelling the propert...

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

Briefings in bioinformatics
This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusabl...

Artificial intelligence for proteomics and biomarker discovery.

Cell systems
There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have no...

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

On the feasibility of deep learning applications using raw mass spectrometry data.

Bioinformatics (Oxford, England)
SUMMARY: In recent years, SWATH-MS has become the proteomic method of choice for data-independent-acquisition, as it enables high proteome coverage, accuracy and reproducibility. However, data analysis is convoluted and requires prior information and...