AIMC Topic: Machine Learning

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iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.

International journal of molecular sciences
Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identify...

A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System.

Scientific reports
While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as...

An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Scientific reports
Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely d...

A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer.

Scientific reports
Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplat...

Easy-Prime: a machine learning-based prime editor design tool.

Genome biology
Prime editing is a revolutionary genome-editing technology that can make a wide range of precise edits in DNA. However, designing highly efficient prime editors (PEs) remains challenging. We develop Easy-Prime, a machine learning-based program traine...

MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning.

Journal of neural engineering
This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque.The regression models, collectively known as MuscleN...

A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data.

PloS one
Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients with obvious EI, the identification of a such behavior during a real-life consultation is unreliable...

Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review.

International journal of medical informatics
OBJECTIVES: Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision...

Piezoelectric Smart Patch Operated with Machine-Learning Algorithms for Effective Detection and Elimination of Condensation.

ACS sensors
Timely detection and elimination of surface condensation is crucial for diverse applications in agriculture, automotive, oil and gas industries, and respiratory monitoring. In this paper, a smart patch based on a ZnO/aluminum (∼5 μm/50 μm thick) flex...

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.

Sensors (Basel, Switzerland)
In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not per...