When we are using machine learning models, we typically don’t make any substantial/particular assumptions like non-collinearity, normally distributed residuals, etc. The absolute predictive performance of ML models is usually better than for statistical models (although, they often don’t have the s...
Bad in the sense that it might be hard for us to put intuition behind the resulting market conditions, and good in the sense that it will hopefully tell us something that we wouldn’t have known without employing a technique like machine learning. After all, if we perfectly knew the market...
Fig. 8: BaM3 effectively couples predictions from machine learning and mathematical modeling in ovarian cancer patients. a Predicted vs real TtR calculated from the mode of the probability distribution obtained from the mathematical model (Model), density estimation (Data), and the BaM3 method. As...
2). This version of the model is used to provide outputs to the machine learning. Figure 2 Measured (USDA-NASS) corn yields vs. simulated corn yields at the state level from 1984 to 2019 using the pSIMS-APSIM framework. Full size image APSIM output variables used as inputs to ML ...
Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotec...
Supervised models use newer machine learning techniques such as neural networks to identify patterns buried in data that has already been labeled. The biggest difference between these approaches is that with supervised models more care must be taken to properly label data sets upfront. ...
For the regulation at the exonic context, we further developed a machine learning approach to extract key predictive features for splicing activators and inhibitors, which can be used in designing peptides with a customized function in regulating splicing. To our knowledge, this work presents the ...
In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD). Methods In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with ...
We also applied machine learning method (random forest) to better understand the off-target viability effect, by comparing guides that have strong vs. weak effect on non-essential genes. We found features identified from this analysis are consistent with features for on-target efficiency, implying ...
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning cl