AIMC Topic: Datasets as Topic

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Identifying N-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine.

Scientific reports
N6-methyladenosine (mA) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear transl...

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

NeuroImage
This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We ad...

Multi-center machine learning in imaging psychiatry: A meta-model approach.

NeuroImage
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizoph...

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

Journal of theoretical biology
Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid ...

An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions.

BMC genomics
BACKGROUND: Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the...

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy.

eNeuro
Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How do...

Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: To safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an e...

Resource Classification for Medical Questions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We present an approach for manually and automatically classifying the resource type of medical questions. Three types of resources are considered: patient-specific, general knowledge, and research. Using this approach, an automatic question answering...

Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Determining the main topics in consumer health questions is a crucial step in their processing as it allows narrowing the search space to a specific semantic context. In this paper we propose a topic recognition approach based on biomedical and open-...

Dermatologist-level classification of skin cancer with deep neural networks.

Nature
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of ski...