AIMC Topic: Humans

Clear Filters Showing 2191 to 2200 of 95995 articles

Predicting protein-protein interactions in the human proteome.

Science (New York, N.Y.)
Protein-protein interactions (PPIs) are essential for biological function. Coevolutionary analysis and deep-learning (DL)-based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches hav...

Single-cell analysis of oxidative phosphorylation protein expression in pancreatic islets in type 2 diabetes.

The Journal of endocrinology
Mitochondrial dysfunction is a key feature of type 2 diabetes and is closely linked to ageing, a major risk factor for the disease. This study investigated islet cell composition and mitochondrial oxidative phosphorylation protein expression in pancr...

Neural subgraph counting on stream graphs via localized updates and monotonic learning.

PloS one
Graphs are a representative type of fundamental data structures. They are capable of representing complex association relationships in diverse domains. For large-scale graph processing, the stream graphs have become efficient tools to process dynamic...

Contrastive learning-enhanced personalized interaction dual tower network for recommendation.

PloS one
Dual-tower retrieval models have become a prevalent solution in large-scale recommendation systems due to their scalability and deployment efficiency. However, they face critical limitations including insufficient modeling of user behavior sequences,...

Evaluating Uighur literary translation: A comparative study of ChatGPT, Google Translate, and Bing Translator.

PloS one
This study compares generative artificial intelligence (GenAI) and neural machine translation (NMT) systems in translating Uighur literary text (قۇتادغۇ بىلىك)into English. Two NMT systems, Google Translate and Bing Translator, were evaluated alongsi...

An exploratory study on predicting HER2-positive expression status of breast cancer using ultrasound radiomics combined with machine learning models.

PloS one
OBJECTIVE: This study aimed to investigate the feasibility and potential value of predictive models for human epidermal growth factor receptor 2 (HER2)-positive status in breast cancer (BC) based on radiomics features from conventional ultrasound ima...

Intelligent sensory technologies, NIR spectroscopy and chemometrics combined with machine learning based on multi-source data fusion for comprehensive evaluation of Sinapis Semen in different processing degrees.

Journal of pharmaceutical and biomedical analysis
Sinapis Semen, as a traditional Chinese medicine, has an unclear relationship between its stir-frying degrees and sensory characteristics. Therefore, it is essential to develop a multi-index evaluation method to classify the processing degree of Sina...

Advancement of machine learning algorithms in biosensors.

Clinica chimica acta; international journal of clinical chemistry
Biosensors have emerged as transformative tools in modern diagnostics, enabling rapid, accurate, and sensitive detection of biological markers for disease diagnosis, real-time monitoring, and personalized healthcare. However, current biosensors still...

Ultrasound-responsive nanocarriers for cancer therapy: Physiochemical features-directed design.

Journal of controlled release : official journal of the Controlled Release Society
The design of ultrasound (US)-responsive nanocarriers (URNs) guided by their physicochemical characteristics represents a pivotal strategy for advancing cancer therapy. By exploiting the intrinsic physicochemical properties of diverse nanocarriers, i...

Personalized real-time inference of momentary excitability from human EEG.

NeuroImage
The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentially constraining therapeutic outcomes. Current EEG-guided approaches are hindered by their reliance ...