AIMC Topic: Machine Learning

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Identification of 2'-O-methylation Site by Investigating Multi-feature Extracting Techniques.

Combinatorial chemistry & high throughput screening
BACKGROUND: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'-O-methylation plays an important role in immune rec...

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach.

Journal of Alzheimer's disease : JAD
BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking...

Detection of Disease-Specific Parent Cells Via Distinct Population of Nano-Vesicles by Machine Learning.

Current pharmaceutical design
BACKGROUND: The diagnosis and prognosis of pathological conditions, such as age-related macular degeneration (AMD) and cancer still need improvement. AMD is primarily caused due to the dysfunction of retinal pigment epithelium (RPE), whereas endothel...

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data.

Journal of Alzheimer's disease : JAD
BACKGROUND: Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used ...

A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer's Disease: Data Preprocessing and Analysis.

The journal of prevention of Alzheimer's disease
The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer's Disease. Further, there are standardized dataset available today th...

Recognizing Proteins with Binding Function in Elymus nutans Based on Machine Learning Methods.

Combinatorial chemistry & high throughput screening
BACKGROUND: We research the binding function proteins in Elymus nutans. Recognition for proteins is essential for study of biology. Machine learning methods have been widely used for the prediction of proteins.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.

Database : the journal of biological databases and curation
Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better ...

General principles of machine learning for brain-computer interfacing.

Handbook of clinical neurology
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair,...

Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning.

Technology in cancer research & treatment
BACKGROUND AND AIM: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to eval...

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.

Methods in molecular biology (Clifton, N.J.)
A standard strategy to discover somatic mutations in a cancer genome is to use next-generation sequencing (NGS) technologies to sequence the tumor tissue and its matched normal (commonly blood or adjacent normal tissue) for side-by-side comparison. H...