AIMC Topic: Models, Statistical

Clear Filters Showing 1211 to 1220 of 1301 articles

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations.

Carcinogenesis
Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the under...

Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network.

Water science and technology : a journal of the International Association on Water Pollution Research
Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount of incoming wastewater in WWTPs, wastewater engineers face challenges arising from numerous complexities and uncertainties...

Borderline Personality Features in Inpatients with Bipolar Disorder: Impact on Course and Machine Learning Model Use to Predict Rapid Readmission.

Journal of psychiatric practice
BACKGROUND: Earlier research indicated that nearly 20% of patients diagnosed with either bipolar disorder (BD) or borderline personality disorder (BPD) also met criteria for the other diagnosis. Yet limited data are available concerning the potential...

HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction.

IEEE journal of biomedical and health informatics
The discovery of disease-causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making ...

Machine Learning for Health Services Researchers.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
BACKGROUND: Machine learning is increasingly used to predict healthcare outcomes, including cost, utilization, and quality.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis.

Bioinformatics (Oxford, England)
MOTIVATION: Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised man...

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.

American journal of epidemiology
Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of...

STATISTICAL APPROACH FOR HUMAN ELECTROMAGNETIC EXPOSURE ASSESSMENT IN FUTURE WIRELESS ATTO-CELL NETWORKS.

Radiation protection dosimetry
In this article, we study human electromagnetic exposure to the radiation of an ultra dense network of nodes integrated in a floor denoted as ATTO-cell floor, or ATTO-floor. ATTO-cells are a prospective 5 G wireless networking technology, in which hu...