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Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples., (© 2021. The Author(s).)

This systematic review examines prediction models for adverse drug events (ADEs) related to antineoplastic drugs, which are used in cancer therapy. The review analyzes 55 studies and evaluates the quality and reporting of these prediction models. The majority of models focused on ADEs related to conventional chemotherapy drugs, with fewer models addressing novel anticancer drugs and additional toxicities. The study finds that machine learning algorithms generally had better methodological quality but weaker reporting quality compared to traditional statistical models. The authors recommend expanding research focus, optimizing model presentation, and improving the design, conduct, validation, and reporting of prediction models to enhance their application in clinical practice. [Extracted from the article]

• Characterized solar irradiance variability relationship with cloud properties. • Observations taken from DOE Atmospheric Radiation Measurement site in Oklahoma. • Seasonal analysis suggests relationship is site independent. • Machine learning model developed to describe observations. • Step towards Numerical Weather Model day ahead variability forecast. A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience. [ABSTRACT FROM AUTHOR]

Machine learning classification and regression algorithms were applied to calibrate the localization errors of a time-difference-of-arrival (TDOA)-based acoustic sensor array used for tracking salmon passage through a hydroelectric dam on the Snake River, Washington, USA. The locations of stationary and mobile acoustic tags were first tracked using the approximate maximum likelihood algorithm. Next, ensembles of classification trees successfully identified and filtered data points with large localization errors. This prefiltering step allowed the creation of a machine-learned regression model function, which decreased the median distance error by 50% for the stationary tracks and by 34% for the mobile tracks. It also extended the previous range of sub-meter localization accuracy from 100 m to 250 m horizontal distance from the dam face (the receivers). Median distance errors in the depth direction were especially decreased, falling from 0.49 m to 0.04 m in the stationary tracks and from 0.38 m to 0.07 m in the mobile tracks. These methods would have application to the calibration of error in any TDOA-based sensor network with a steady environment and array configuration. [ABSTRACT FROM AUTHOR]

One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. In this study, data-driven feature analysis is performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross correlation, multinomial logistical regression, and classification and regression trees, are integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrates the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design. [ABSTRACT FROM AUTHOR]