AIMC Topic: Machine Learning

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Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.

Scientific reports
Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to its diverse clinical manifestations and underlying biological complexity. This study explored machine learning approaches to enhance ASD screening acc...

Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.

Scientific reports
Parkinson's disease (PD) is a neurodegenerative disorder affecting motor control, leading to symptoms such as tremors and stiffness. Early diagnosis is essential for effective treatment, but traditional methods are often time-consuming and expensive....

Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.

Scientific reports
Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual ce...

ML techniques increasing the power factor of a compression ignition engine that is powered by Annona biodiesel using SATACOM.

Scientific reports
The global production of biodiesel in 2023 amounted to 34 billion liters because compression ignition engines need environmentally friendly fuel alternatives. The research investigates Annona biodiesel in combination with machine learning (ML) and ST...

Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms.

Scientific reports
Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter sur...

Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.

Cell communication and signaling : CCS
BACKGROUND: Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensi...

Identification of thyroid cancer biomarkers using WGCNA and machine learning.

European journal of medical research
OBJECTIVE: The incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis from widespread screening and improved ultrasound technology. Identifying precise TC biomarkers is crucial for accurate diagnosis and effective treat...

Machine Learning Feasibility in Cochlear Implant Speech Perception Outcomes-Moving Beyond Single Biomarkers for Cochlear Implant Performance Prediction.

Ear and hearing
OBJECTIVES: Machine learning (ML) is an emerging discipline centered around complex pattern matching and large data-based prediction modeling and can improve precision medicine healthcare. Cochlear implants (CI) are highly effective, however, outcome...

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians.

Cancer letters
In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building on this insight, we develop...

FreqYOLO: A uterine disease detection network based on local and global frequency feature learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Leiomyomas (LM) and adenomyosis (AM) are common gynecological diseases with high incidence rates and an increasing trend of affecting younger women. Accurate detection and differentiation of LM and AM in ultrasound images are crucial for selecting ap...