AIMC Topic: Machine Learning

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Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks.

Scientific reports
Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and ...

Identification of infectious disease-associated host genes using machine learning techniques.

BMC bioinformatics
BACKGROUND: With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our underst...

Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM.

Biomedical engineering online
BACKGROUND: Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hypertension stage 1 and hypertension stage 2. The quantitative analysis of hypertension helps determine dis...

Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces.

BMC bioinformatics
BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interacti...

Predicting breast cancer risk using personal health data and machine learning models.

PloS one
Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer ...

Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review.

Journal of medical imaging and radiation sciences
Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the medical domain. In this article, we investigate the impact of ML for the diagnosis of breast cancer ...

Discovering nuclear targeting signal sequence through protein language learning and multivariate analysis.

Analytical biochemistry
Nuclear localization signals (NLSs) are peptides that target proteins to the nucleus by binding to carrier proteins in the cytoplasm that transport their cargo across the nuclear membrane. Accurate identification of NLSs can help elucidate the functi...

A novel machine learning based approach for iPS progenitor cell identification.

PLoS computational biology
Identification of induced pluripotent stem (iPS) progenitor cells, the iPS forming cells in early stage of reprogramming, could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult ...

Association extraction from biomedical literature based on representation and transfer learning.

Journal of theoretical biology
Extracting biological relations from biomedical literature can deliver personalized treatment to individual patients based on their genomic profiles. In this paper, we present a novel sentence-level attention-based deep neural network to predict the ...

Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
The Malaria burden was an escalating global encumbrance and need to be addressed with critical care. Anti-malarial drug discovery was integrated with supervised machine learning (ML) models to identify potent thiazolyl-traizine derivatives. This assi...