AIMC Topic: Machine Learning

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AcidProNet: Acidophilic Protein Classification via DCGAN-GP-Based Data Augmentation and Parameter-Shared Mixture-of-Experts Transformer.

Journal of chemical information and modeling
With the continued exploration of biological resources in extreme environments, functional proteins such as acidophilic proteins have attracted increasing attention. These proteins can maintain structural stability and biological functionality under ...

Construction and validation of the prediction model for kinesiophobia in older adults with chronic low back pain.

BMC geriatrics
BACKGROUND: Low back pain imposes a substantial burden on global healthcare systems. Kinesiophobia is highly prevalent among older adults with chronic low back pain, severely hindering effective intervention and treatment. However, current assessment...

Precise diagnosis of small invasive pulmonary nodules driven by single-cell immune signatures in peripheral blood.

Nature communications
Early detection of lung cancer is crucial for improving patient outcomes. However, accurately diagnosing invasive pulmonary nodules and predicting tumor invasiveness remain major clinical challenges. Given the established role of immune dysfunction i...

Assessment of the risk of osteoporotic bone fracture in postmenopausal women using machine learning methods.

Scientific reports
The main objective of osteoporosis management is to prevent osteoporotic fractures. Using machine learning methods, new risk variables can be identified to enhance the ability to identify women with osteoporosis who are at an increased risk of bone f...

COVID-19 severity analysis for clinical decision support based on machine learning approach.

Scientific reports
The COVID-19 pandemic has placed immense pressure on global healthcare systems, underscoring the urgent need for early and accurate prediction of disease severity to improve patient care and optimize resource allocation. Failure in ward allocation ca...

Classifying human vs. AI text with machine learning and explainable transformer models.

Scientific reports
The rapid proliferation of AI-generated text from models such as ChatGPT-3.5 and ChatGPT-4 has raised critical challenges in verifying content authenticity and ensuring ethical use of language technologies. This study presents a comprehensive framewo...

Coagulation Risk Prediction in Patients With Liver Failure: Integrated Meta-Analysis and Machine Learning Model Study.

JMIR medical informatics
BACKGROUND: Liver failure often results in significant coagulation dysfunction, which is a major complication. Artificial liver support systems (ALSS) have been used to ameliorate coagulation parameters, but the dynamic nature of these improvements a...

Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin's lymphoma in ICU: a retrospective study based on MIMIC-IV database.

Clinical and experimental medicine
Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV da...

Stress detection using time-frequency analysis and machine learning framework.

Biomedical physics & engineering express
Stress is a prevalent and inherent phenomenon in people. It triggers the production of hormones that assist in managing the scenarios; nevertheless, chronic stress adversely impacts physical and mental health, which may result in detrimental effects ...

Learning structured population models from data with WSINDy.

PLoS computational biology
Characteristics of individuals in a population, such as age and size, play a key role in determining how populations change over time. In contexts of population dynamics, identifying effective model features, such as fecundity and mortality rates, is...