AIMC Topic: Machine Learning

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Multi-label zero-shot learning with graph convolutional networks.

Neural networks : the official journal of the International Neural Network Society
The goal of zero-shot learning (ZSL) is to build a classifier that recognizes novel categories with no corresponding annotated training data. The typical routine is to transfer knowledge from seen classes to unseen ones by learning a visual-semantic ...

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

Interdisciplinary sciences, computational life sciences
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce ...

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.

Medical & biological engineering & computing
Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details co...

Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein-Ligand Predictions.

Journal of chemical information and modeling
In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into a probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance...

Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.

Proceedings of the National Academy of Sciences of the United States of America
Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection...

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model.

International journal of molecular sciences
Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aim...

Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model.

BMC medical informatics and decision making
BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients' outcomes and data mining is such a powerf...

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.

BMC medical genomics
BACKGROUND: Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from path...

Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life.

BMC bioinformatics
BACKGROUND: It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new ref...

Biomechanical monitoring and machine learning for the detection of lying postures.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Pressure mapping technology has been adapted to monitor over prolonged periods to evaluate pressure ulcer risk in individuals during extended lying postures. However, temporal pressure distribution signals are not currently used to identi...