AIMC Topic: Artificial Intelligence

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Distributed Raman Spectrum Data Augmentation System Using Federated Learning with Deep Generative Models.

Sensors (Basel, Switzerland)
Chemical agents are one of the major threats to soldiers in modern warfare, so it is so important to detect chemical agents rapidly and accurately on battlefields. Raman spectroscopy-based detectors are widely used but have many limitations. The Rama...

Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells.

International journal of molecular sciences
Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer in women. It has the poorest prognosis along with limited therapeutic options. Smart nano-based carriers are emerging as promising approaches in treating TNBC due to...

Substrate Recognition Properties from an Intermediate Structural State of the UreA Transporter.

International journal of molecular sciences
Through a combination of comparative modeling, site-directed and classical random mutagenesis approaches, we previously identified critical residues for binding, recognition, and translocation of urea, and its inhibition by 2-thiourea and acetamide i...

Population codes enable learning from few examples by shaping inductive bias.

eLife
Learning from a limited number of experiences requires suitable inductive biases. To identify how inductive biases are implemented in and shaped by neural codes, we analyze sample-efficient learning of arbitrary stimulus-response maps from arbitrary ...

Artificial Intelligence Applications in Clinical Chemistry.

Clinics in laboratory medicine
Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this...

Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis.

Lung cancer (Amsterdam, Netherlands)
BACKGROUND: Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in hea...

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We...

Mass-Spec, Cryo-EM and AI join forces for a close look at the transporter complex in cilia.

The EMBO journal
The intraflagellar transport (IFT) complex transports components between the cytoplasm and the ciliary tip. Two studies now report on the atomic structure of IFT-B, the core of IFT, using cutting-edge technology, such as cross-linking mass spectromet...

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity.

Journal of chemical information and modeling
Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method i...

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients.

IEEE journal of translational engineering in health and medicine
BACKGROUND: Over a decade, tissues dissected adjacent to primary tumors have been considered "normal" or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict ...