AIMC Topic: Area Under Curve

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A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms.

BMC medical informatics and decision making
BACKGROUND: In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negative...

A practical model for the identification of congenital cataracts using machine learning.

EBioMedicine
BACKGROUND: Approximately 1 in 33 newborns is affected by congenital anomalies worldwide. We aimed to develop a practical model for identifying infants with a high risk of congenital cataracts (CCs), which is the leading cause of avoidable childhood ...

Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Detecting blood content in the gastrointestinal tract is one of the crucial applications of capsule endoscopy (CE). The suspected blood indicator (SBI) is a conventional tool used to automatically tag images depicting possible ble...

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.

BMC genomics
BACKGROUND: Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical protein...

A computational method to predict topologically associating domain boundaries combining histone Marks and sequence information.

BMC genomics
BACKGROUND: The three-dimensional (3D) structure of chromatins plays significant roles during cell differentiation and development. Hi-C and other 3C-based technologies allow us to look deep into the chromatin architectures. Many studies have suggest...

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 drug-target interactions from drug structure and protein sequence using novel convolutional neural networks.

BMC bioinformatics
BACKGROUND: Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target i...

Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.

BMC bioinformatics
BACKGROUND: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, m...

Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.

Scientific data
The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underway and the early drivers of clinical outcome cannot be readily determined. Thus, there is a need to ...