Toward explainable artificial intelligence for precision pathology
The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …
diagnostic pathology with respect to its ability to analyze histological images and …
Next-generation TB vaccines: progress, challenges, and prospects
L Zhuang, Z Ye, L Li, L Yang, W Gong - Vaccines, 2023 - mdpi.com
Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global
infectious disease and a leading cause of mortality worldwide. Currently, the only available …
infectious disease and a leading cause of mortality worldwide. Currently, the only available …
Towards generalist biomedical AI
Background Medicine is inherently multimodal, requiring the simultaneous interpretation
and integration of insights between many data modalities spanning text, imaging, genomics …
and integration of insights between many data modalities spanning text, imaging, genomics …
Applications of artificial intelligence in clinical laboratory genomics
The transition from analog to digital technologies in clinical laboratory genomics is ushering
in an era of “big data” in ways that will exceed human capacity to rapidly and reproducibly …
in an era of “big data” in ways that will exceed human capacity to rapidly and reproducibly …
Effect of tokenization on transformers for biological sequences
E Dotan, G Jaschek, T Pupko, Y Belinkov - Bioinformatics, 2024 - academic.oup.com
Motivation Deep-learning models are transforming biological research, including many
bioinformatics and comparative genomics algorithms, such as sequence alignments …
bioinformatics and comparative genomics algorithms, such as sequence alignments …
Leveraging deep learning to improve vaccine design
AP Hederman, ME Ackerman - Trends in immunology, 2023 - cell.com
Deep learning has led to incredible breakthroughs in areas of research, from self-driving
vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however …
vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however …
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Background There is an increasing interest in the use of Deep Learning (DL) based
methods as a supporting analytical framework in oncology. However, most direct …
methods as a supporting analytical framework in oncology. However, most direct …
[HTML][HTML] A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems
Load forecasting is a crucial task, which is carried out by utility companies for sake of power
grids' successful planning, optimized operation and control, enhanced performance, and …
grids' successful planning, optimized operation and control, enhanced performance, and …
Application of deep learning technique in next generation sequence experiments
S Özgür, M Orman - Journal of Big Data, 2023 - Springer
In recent years, the widespread utilization of biological data processing technology has
been driven by its cost-effectiveness. Consequently, next-generation sequencing (NGS) has …
been driven by its cost-effectiveness. Consequently, next-generation sequencing (NGS) has …
An overview of artificial intelligence in the field of genomics
Artificial intelligence (AI) is revolutionizing many real-world applications in various domains.
In the field of genomics, multiple traditional machine-learning approaches have been used …
In the field of genomics, multiple traditional machine-learning approaches have been used …