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The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well-established for evaluating exposure-response (E-R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E-R relationships has not been widely explored. We developed a workflow to train regularized LR/CoxPH and tree-based XGboost (XGB) models, and derive the odds ratios for best overall response and hazard ratios for overall survival, across exposure quantiles to evaluate the E-R relationship using clinical trial datasets. The E-R conclusions between LR/CoxPH and XGB models are overall consistent, and largely aligned with historical pharmacometric analyses findings. Overall, applying this interpretable ML workflow provides a promising alternative method to assess E-R relationships for impacting key dosing decisions in drug development., (© 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorithms have limited widespread adoption. In response, we have developed Pharm-AutoML, an open-source Python package that enables users to automate the construction of ML models and predict clinical outcomes, especially in the context of pharmacological interventions. In particular, our approach streamlines tedious steps within the ML workflow, including data preprocessing, model tuning, model selection, results analysis, and model interpretation. Moreover, our open-source package helps to identify the most predictive ML pipeline among defined search spaces by selecting the best data preprocessing strategy and tuning the ML model hyperparameters. The package currently supports multiclass classification tasks, and additional functions are being developed. Using a set of five publicly available biomedical datasets, we show that our Pharm-AutoML outperforms other ML frameworks, including H2O with default settings, by demonstrating improved predictive accuracy of classification. We further illustrate how model interpretation methods can be utilized to help explain the fine-tuned ML pipeline to end users. Pharm-AutoML provides both novice and expert users improved clinical predictions and scientific insights., (© 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP‐SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP‐SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM‐simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML‐based GPP‐SIF relationship. The ELM model when fed with the ML GPP‐SIF models also can well predict the spatial‐temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball‐Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote‐sensing SIF, which can be further improved in the future with more ground‐ and satellite‐based observations. Plain Language Summary: Model estimation of photosynthesis product, that is, gross primary productivity (GPP), is a challenging but vital task. One of the keys is to find better values for key parameters. This parameter searching process requires good proxies for GPP that can be widely available across space and time, good statistical methods to relate proxies to GPP and to make best estimations that reduce the gaps between modeled results and observations. Here, we designed a new method that use solar‐induced chlorophyll fluorescence (SIF, a good proxy for photosynthesis) as a key input, and employ machine learning (a robust way to relate SIF and GPP) and surrogate modeling (a good method for finding the best parameters), to improve the photosynthesis parameterization in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), a state‐of‐the‐art terrestrial biosphere model. Our results demonstrate that this new integrated approach has great potential for improving the parameterization of key photosynthesis parameters in land models. Key Points: We built a unique method to improve gross primary productivity (GPP) modeling in land modelsThis method integrates solar‐induced chlorophyll fluorescence observations, machine learning, and surrogate modelingThe method reduced posterior uncertainties in simulated GPP and improved the modeling of its spatial patterns [ABSTRACT FROM AUTHOR]

Hydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long short-term memory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most LSTM applications focus on well-monitored catchments with abundant and high-quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that 1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash–Sutcliffe efficiency above 0.8, even with only 2 years of calibration data; 2) when the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy; 3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting; and 4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. These insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue. [ABSTRACT FROM AUTHOR]

[Display omitted] • The design of novel single-phase refractory high entropy alloys via machine learning. • Extended dataset and optimal input features for machine learning algorithms. • Machine learning model validation by mechanical alloying experiment. Herein, we proposed a strategy to design single-phase refractory high entropy alloys (RHEAs) with the assistance of machine learning algorithms. Based on an extensive dataset (1807 entries) built in this work, we applied multiple machine learning algorithms to train the dataset. After the blind test, we found that the Gradient boosting (GB) model can distinguish the single-phase-solid solution and non-single-phase-solid solution alloys with a test accuracy of 96.41%. Given the GB model, we predicted over 100 equiatomic oxidation-resistance RHEAs from the composition space of eight metallic elements. After that, we synthesized ten of these predicted single-phase RHEAs by mechanical alloying. The XRD patterns show that all of them are single-phase BCC solid solution. The experimental results agree well with the prediction results, indicating the excellent performance of the machine learning model in single-phase RHEAs prediction. With the aid of the machine learning method, single-phase oxidation-resistant RHEAs were successfully designed. Our work presents a novel strategy with outstanding performance and evident effectiveness on the accelerated discovery of novel metallic materials used for extreme environments. [ABSTRACT FROM AUTHOR]

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions. • Propose an ED-LSTM network to improve multi-step reservoir inflow forecasting. • Develop an uncertainty quantification method to quantify the predictive uncertainty. • Apply SHAP to quantify the effect of hydrometeorological drivers on inflow prediction. • The proposed framework can be applied to hydrologically diverse reservoirs. [ABSTRACT FROM AUTHOR]