FDA General

A practical guide to FAIR data management in the age of multi-OMICS and AI.

Multi-cellular biological systems, including the immune system, are highly complex, dynamic, and adaptable. Systems biologists aim to understand such complexity at a quantitative level. However, these ambitious efforts are often limited by access to a variety of high-density intra-, extra- and multi-cellular measurements resolved in time and space and across a variety of perturbations. The advent ...

Reducing bias in source-free unsupervised domain adaptation for regression.

Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to train target models by mitigating biases induced by noisy pseudo labels, they often lack theoretical guarantees for fully reducing biases and have predo...

Multidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points.

Biological neural systems seamlessly integrate perception and action, a feat not efficiently replicated in current physically separated designs of neu...

Reimbursement and Regulatory Landscape for Artificial Intelligence in Medical Technology.

Integration of artificial intelligence (AI) into medical devices and services promises significant improvements in the diagnosis and treatment of dise...

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects.

The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into i...

Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.

The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While ...

The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.

The 5' UTRs of mRNAs are critical for translation regulation during development, but their in vivo regulatory features are poorly characterized. Here,...

Artificial intelligence-based biomarkers for treatment decisions in oncology.

The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the t...

Feedback regulation of mA modification creates local auxin maxima essential for rice microsporogenesis.

N-methyladenosine (mA) RNA modification and its effectors control various plant developmental processes, yet whether and how these effectors are trans...

GRL-PUL: predicting microbe-drug association based on graph representation learning and positive unlabeled learning.

Extensive research has confirmed the widespread presence of microorganisms in the human body and their crucial impact on human health, with drugs bein...

Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.

Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations...

Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction.

Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials ne...

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and com...

Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making.

Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a fro...

Adaptive Gait Feature Learning Using Mixed Gait Sequence.

Gait recognition has become a mainstream technology for identification, as it can recognize the identity of subjects from a distance without any coope...

Hierarchical Graph Transformer With Contrastive Learning for Gene Regulatory Network Inference.

Gene regulatory networks (GRNs) are crucial for understanding gene regulation and cellular processes. Inferring GRNs helps uncover regulatory pathways...

Countering AI-powered disinformation through national regulation: learning from the case of Ukraine.

Advances in the use of AI have led to the emergence of a greater variety of forms disinformation can take and channels for its proliferation. In this ...

Plant and marine-derived natural products: sustainable pathways for future drug discovery and therapeutic development.

Plant- and marine-derived natural products are rich sources of bioactive compounds essential for drug discovery. These compounds contain complex mixtu...

A deep learning approach to perform defect classification of freeze-dried product.

Cosmetic inspection of freeze-dried products is an important part of the post-manufacturing quality control process. Traditionally done by human visua...

A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing.

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkp...