AIMC Topic: Machine Learning

Clear Filters Showing 15701 to 15710 of 34417 articles

BERT-Promoter: An improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection.

Computational biology and chemistry
A promoter is a sequence of DNA that initializes the process of transcription and regulates whenever and wherever genes are expressed in the organism. Because of its importance in molecular biology, identifying DNA promoters are challenging to provid...

A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects.

Journal of biomedical informatics
Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and ...

External validation of a machine learning model to predict hemodynamic instability in intensive care unit.

Critical care (London, England)
BACKGROUND: Early prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a mul...

Toward a standard formal semantic representation of the model card report.

BMC bioinformatics
BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR chal...

Does adding the drug-drug similarity to drug-target interaction prediction methods make a noticeable improvement in their efficiency?

BMC bioinformatics
Predicting drug-target interactions (DTIs) has become an important bioinformatics issue because it is one of the critical and preliminary stages of drug repositioning. Therefore, scientists are trying to develop more accurate computational methods fo...

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm.

Computational intelligence and neuroscience
Human gait recognition has emerged as a branch of biometric identification in the last decade, focusing on individuals based on several characteristics such as movement, time, and clothing. It is also great for video surveillance applications. The ma...

MetaAge: Meta-Learning Personalized Age Estimators.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods ...

Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review.

Annals of emergency medicine
STUDY OBJECTIVE: Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be diffi...

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study.

Sensors (Basel, Switzerland)
Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease...

Non-Deep Active Learning for Deep Neural Networks.

Sensors (Basel, Switzerland)
One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative u...