AIMC Topic: Machine Learning

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Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association.

Circulation
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imagin...

DiCleave: a deep learning model for predicting human Dicer cleavage sites.

BMC bioinformatics
BACKGROUND: MicroRNAs (miRNAs) are a class of non-coding RNAs that play a pivotal role as gene expression regulators. These miRNAs are typically approximately 20 to 25 nucleotides long. The maturation of miRNAs requires Dicer cleavage at specific sit...

Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning.

Frontiers in immunology
BACKGROUND: Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have cruc...

From Data to Decisions: How Artificial Intelligence Is Revolutionizing Clinical Prediction Models in Plastic Surgery.

Plastic and reconstructive surgery
The impact of clinical prediction models within artificial intelligence (AI) and machine learning is significant. With its ability to analyze vast amounts of data and identify complex patterns, machine learning has the potential to improve and implem...

Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
BACKGROUND: Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic splenic injuries, leading to improved patient management. Their effective use requires rapid study inter...

Contrastive and adversarial regularized multi-level representation learning for incomplete multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
Incomplete multi-view clustering is a significant task in machine learning, given that complex systems in nature and society cannot be fully observed; it provides an opportunity to exploit the structure and functions of underlying systems. Current al...

A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification.

Artificial intelligence in medicine
The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, the u...

Machine learning interpretable models of cell mechanics from protein images.

Cell
Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understandi...

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is ...

MAGPIE: accurate pathogenic prediction for multiple variant types using machine learning approach.

Genome medicine
Identifying pathogenic variants from the vast majority of nucleotide variation remains a challenge. We present a method named Multimodal Annotation Generated Pathogenic Impact Evaluator (MAGPIE) that predicts the pathogenicity of multi-type variants....