AIMC Topic: Neural Networks, Computer

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Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modelin...

An extensive experimental survey of regression methods.

Neural networks : the official journal of the International Neural Network Society
Regression is a very relevant problem in machine learning, with many different available approaches. The current work presents a comparison of a large collection composed by 77 popular regression models which belong to 19 families: linear and general...

Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study.

The Lancet. Oncology
BACKGROUND: The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagn...

Prediction of protein self-interactions using stacked long short-term memory from protein sequences information.

BMC systems biology
BACKGROUND: Self-interacting Proteins (SIPs) plays a critical role in a series of life function in most living cells. Researches on SIPs are important part of molecular biology. Although numerous SIPs data be provided, traditional experimental method...

Predicting tumor cell line response to drug pairs with deep learning.

BMC bioinformatics
BACKGROUND: The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.

Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks.

BMC bioinformatics
BACKGROUND: Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are requir...

Sparse coding of pathology slides compared to transfer learning with deep neural networks.

BMC bioinformatics
BACKGROUND: Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non-uniform dimensions, and ofte...

Biomedical semantic indexing by deep neural network with multi-task learning.

BMC bioinformatics
BACKGROUND: Biomedical semantic indexing is important for information retrieval and many other research fields in bioinformatics. It annotates biomedical citations with Medical Subject Headings. In face of unbalanced category distribution in the trai...

Scalable deep text comprehension for Cancer surveillance on high-performance computing.

BMC bioinformatics
BACKGROUND: Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This ...

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.

BMC bioinformatics
BACKGROUND: Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require...