AIMC Topic: Machine Learning

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Learning meaningful representations of protein sequences.

Nature communications
How we choose to represent our data has a fundamental impact on our ability to subsequently extract information from them. Machine learning promises to automatically determine efficient representations from large unstructured datasets, such as those ...

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.

BMC medical research methodology
BACKGROUND: Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction.

BMC plant biology
Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few app...

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.

BMC bioinformatics
OBJECTIVES: Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immun...

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis.

Progress in molecular biology and translational science
The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that...

A general framework of nonparametric feature selection in high-dimensional data.

Biometrics
Nonparametric feature selection for high-dimensional data is an important and challenging problem in the fields of statistics and machine learning. Most of the existing methods for feature selection focus on parametric or additive models which may su...

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization.

The Journal of asthma : official journal of the Association for the Care of Asthma
OBJECTIVE: Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective on...

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop practical machine learning models based on clinical quantitative computed tomography (QCT) images for...

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm.

The Science of the total environment
A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is ch...

Machine learning to design antimicrobial combination therapies: Promises and pitfalls.

Drug discovery today
Combination therapies can overcome antimicrobial resistance (AMR) and repurpose existing drugs. However, the large combinatorial space to explore presents a daunting challenge. In response, machine learning (ML) algorithms are being applied to identi...