AIMC Topic: Databases, Factual

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Exploration of machine algorithms based on deep learning model and feature extraction.

Mathematical biosciences and engineering : MBE
The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with ...

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Briefings in bioinformatics
Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discrimina...

Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.

Briefings in bioinformatics
Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial r...

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early c...

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops.

Briefings in bioinformatics
The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer-promoter interaction is the general feature of mammalian gene control. Recently, some comput...

Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Briefings in bioinformatics
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent ...

Predicting drug-disease associations through layer attention graph convolutional network.

Briefings in bioinformatics
BACKGROUND: Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient ...

Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients.

The journal of trauma and acute care surgery
BACKGROUND: Classic risk assessment tools often treat patients' risk factors as linear and additive. Clinical reality suggests that the presence of certain risk factors can alter the impact of other factors; in other words, risk modeling is not linea...

An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography.

Mathematical biosciences and engineering : MBE
Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its early stages. Deep Convolutional Neural Networks (Deep CNNs) and its variants have provided superior performance in glaucoma classification, segmentat...

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.

Cardiovascular research
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances ...