Machine Learning Modeling Approach Research Articles

Peptidic Compound as DNA Binding Agent: In Silico Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G-quadruplex (G4), a secondary structure in guanine-rich regions. G4 plays a crucial role in cellular processes by regulating gene expression and telomerase function. Researchers have recently identified G4-stabilizing binding agents as promising anti-cancer compounds. Additionally, peptides have emerged as effective anticancer pharmaceuticals due to their ability to form multiple hydrogen bonds, electrostatic interactions, and van der Waals forces. These properties enable peptides to bind to specific areas of DNA chains selectively. However, despite these advancements, designing G4-binding peptides remains challenging due to a lack of comprehensive information. In our present study, we employed an in silico fragment-based approach to design G4- binding peptides. This innovative method combines machine learning classification, molecular docking, and dynamics simulation. AutoDock Vina and Gromacs performed molecular docking and MD simulation, respectively. The machine learning algorithm was implemented by Scikit-learn. Peptide synthesis was performed using the SPPS method. The DNA binding affinity was measured by applying spectrophotometric titration. As a result of this approach, we identified a high-scoring peptide (p10; sequence: YWRWR). The association constant (Ka) between p10 and the ctDNA double helix chain was 4.45 × 105 M-1. Molecular modeling studies revealed that p10 could form a stable complex with the G4 surface. The obtained Ka value of 4.45 × 105 M-1 indicates favorable interactions. Our findings highlight the role of machine learning and molecular modeling approaches in designing new G4-binding peptides. Further research in this field could lead to targeted treatments that exploit the unique properties of G4 structures.

Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches

The rapid growth of e-commerce has increased the need for retailers to understand and predict customer satisfaction to support data-driven managerial decisions. This study analyzes online consumer behavior through a comparative machine learning modeling approach to forecast future customer satisfaction based on review ratings. Using a large dataset of over 100 k online orders from a major retailer, traditional machine learning models including random forest and support vector machines are benchmarked against deep learning techniques like multi-layer perceptrons. The predictive models are assessed for their ability to accurately predict customer satisfaction scores for the next orders based on key e-commerce features including delivery time, order value, and location. The findings demonstrate that the random forest model can predict future satisfaction with 92% accuracy, outperforming deep learning. The analysis further identifies core drivers of satisfaction such as delivery time and order accuracy. These insights enable retail managers to make targeted improvements, like optimizing logistics, to increase customer loyalty and revenue. This study provides a framework for leveraging predictive analytics and machine learning to unlock data-driven insights into online consumer behavior and satisfaction for superior retail decision-making. The focus on generalizable insights across a major retailer enhances the practical applicability of the machine learning approach for the retail sector.

Comparing machine learning and inverse modeling approaches for the source term estimation

Comparing machine learning and inverse modeling approaches for the source term estimation

Bond strength between recycled aggregate concrete and rebar: Interpretable machine learning modeling approach for performance estimation and engineering design

Recycled aggregate concrete has a large potential for application as a sustainable building material. To assist in guiding the engineering design of this low-carbon material, the complex relationship between its bond strength to the rebar and various design parameters was modeled. Comparative studies of multiple machine learning methods have shown that the extreme random tree model demonstrates outstanding performance with root mean square error of 1.982, mean absolute error of 1.202 and coefficient of determination of 0.954. To determine the role of design parameters in performance prediction, importance analysis and sensitivity analyses for parameters were used to explore the complex relationship in the model. The results indicated that rebar type, water-cement ratio, anchorage length, and compressive strength are crucial factors affecting the bond strength between recycled aggregate concrete and rebar. In addition, to further demonstrate the reliability and accuracy of machine learning models, explicit models in some specifications were used for comparative analysis. Due to variability in influencing factors, limited test data, and variability in material properties, the explicit models have limited accuracy for bond strength between recycled aggregate concrete and rebar. Therefore, the interpretable modeling approach can provide an accurate estimate of the bond strength between recycled concrete aggregates and rebar and guide the engineering design of this structure.

Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India

Forest fires pose significant challenges by disrupting ecological balance, impacting socio-economic harmony, and raising global concerns. North-East India (NEI) experiences high incidences of forest fires, making it crucial to implement suitable management measures considering the driving forces influencing fire likelihood. This study aims to identify forest fire susceptibility zones in NEI by using five machine-learning modeling approaches, Boosted Regression Tree (BRT), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), and Multivariate Adaptive Regression Splines (MARS), and an ensemble method. Forest fire data from the SNPP – VIIRS sensor (2018–2019) were rectified for spatial autocorrelation. Thirty-two responsive predictor variables related to topographic, climatic, biophysical, and anthropogenic factors were used as model inputs and multicollinearity analysis was performed to eliminate highly correlated predictors. Results indicate that the southern and southeastern regions of NEI, characterized by ample solar radiation, enhanced vegetation index, high human population density, and jhum cultivation, contribute significantly to higher susceptibility to forest fires. The Random Forest model performs best among the models employed, achieving an AUC value of 0.87. The ensemble susceptibility map, binarized based on AUC weighting, covers 29.54% of the total geographic area and 44.42% of the forested area of NEI. The vulnerability levels vary among states, with Mizoram showing the highest susceptibility at 89.27% and Sikkim exhibiting the lowest vulnerability at only 0.49% of their respective geographic areas. This map provides valuable insights for implementing effective forest fire management plans in the region. Moreover, the methodology utilized in this study, which incorporates satellite imagery, GIS techniques, and improved modeling techniques, can be replicated in any geographical region worldwide to facilitate effective forest fire management at a regional to large scale.

Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost

AbstractSalinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state‐of‐the‐art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere‐Wave‐Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process‐based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.

Abstract 4966: Machine learning and large language model approach to pancancer data elements

Abstract Introductory Statement: The goal is to use machine learning (ML) and large language model (LLM) to augment the manual curation of cancer data elements. Introduction: Memorial Sloan Kettering Cancer Center (MSKCC) has ~100,000 cancer patients and counting with genomic testing. Clinicians use genomic data for research but lack clinical data to analyze together. We use a vendor, VASTA Global to hire curators to manually curate cancer patient’s core clinical data elements (CCDE) within unstructured/paragraph text in electronic medical record (EMR) notes. CCDE encompasses 122 data elements that include a patient’s full cancer history that can take up to 1 working day to curate. We collaborated with the Realyze Intelligence Healthcare Solutions vendor to use their AI pipeline to generate the manual curated dataset. Realyze generated the CCDE data elements such as histology, pathology site, MMR, TNM staging, ECOG, and KPS for a pilot lung cancer cohort of 150 patients. We manually validated the generated data for 74 out of 150 patients. Methods:The Realyze platform uses a combination of LLMs, ML algorithms and standard terminologies to create a cancer patient model. These models are flexible enough to address the unique needs and challenges of a pan-cancer oncology model. By using standardized FHIR export, results were delivered to a data lake solution and written into a REDCap database to enable human review. Summary:We manually assessed 74 patients. The NLP gave concordant values for MMR, KPS and TNM staging for 100% of the instances. For MMR these were all null values with false negative (FN) of 100% accuracy. Pathology site had 92.15% accuracy while histology has 97.5% accuracy. Conclusion:Will work on refining pathology site and histology’s ICDO3 list to increase the percentage of accuracy. Once Realyze refines their model for these data elements we will re-run it on a larger cohort of cancer patients and calculate the accuracy. Accuracy Results Clinical data elements 74 patients assessed: Accuracy % ECOG 98.6 KPS 100 T (path) 100 T (clinical) 100 N (path) 100 N (clinical) 100 M (path) 100 M(clinical) 100 MMR 100 Histology (path) 97.5 Path site 92.15 Citation Format: Andrew Niederhausern, Nadia S. Bahadur, Gary Wallace, Gilan E. Saadawi, John Philip. Machine learning and large language model approach to pancancer data elements [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4966.

Performance Differences of a Touch-Based Serial Reaction Time Task in Healthy Older Participants and Older Participants With Cognitive Impairment on a Tablet: Experimental Study.

Digital neuropsychological tools for diagnosing neurodegenerative diseases in the older population are becoming more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined. The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the serial reaction time task (SRTT). This study aims to develop and empirically test a digital tablet-based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups. A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI-diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning. Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=-3.64, SE 0.86; z=-4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (β=-0.34; SE 0.12; t=-2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%). Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice.

Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models

Hydrological modelling can be complex in nonhomogeneous catchments with diverse geological, climatic, and topographic conditions. In this study, an integrated conceptual model including the snow module with machine learning modelling approaches was implemented for daily rainfall-runoff modelling in mostly karst Ljubljanica catchment, Slovenia, which has heterogeneous characteristics and is potentially exposed to extreme events that make the modelling process more challenging and crucial. In this regard, the conceptual model CemaNeige Génie Rural à 6 paramètres Journalier (CemaNeige GR6J) was combined with machine learning models, namely wavelet-based support vector regression (WSVR) and wavelet-based multivariate adaptive regression spline (WMARS) to enhance modelling performance. In this study, the performance of the models was comprehensively investigated, considering their ability to forecast daily extreme runoff. Although CemaNeige GR6J yielded a very good performance, it overestimated low flows. The WSVR and WMARS models yielded poorer performance than the conceptual and hybrid models. The hybrid model approach improved the performance of the machine learning models and the conceptual model by revealing the linkage between variables and runoff in the conceptual model, which provided more accurate results for extreme flows. Accordingly, the hybrid models improved the forecasting performance of the maximum flows up to 40 % and 61 %, and minimum flows up to 73 % and 72 % compared to the CemaNeige GR6J and stand-alone machine learning models. In this regard, the hybrid model approach can enhance the daily rainfall-runoff modelling performance in nonhomogeneous and karst catchments where the hydrological process can be more complicated.

Application of natural language processing for spill reduction in an exploration and production company

Spill reduction is a key focus for exploration and production (E&P) companies to protect people, the environment, and to maintain oil production. Learning from previous incidents enable companies to identify ways to lower the severity and frequency of spills. Root cause failure analysis (RCFA) is a key element in process safety management, and this is often focused on high severity incidents because of the time required to properly conduct the analysis. Incidents that have lower severity occur more frequently, but typically less time and resources are spent analyzing these events. Learning from lower severity, high frequency events can be useful in improving process safety programs and reduce spill frequency and impacts because the data set is more representative of the big picture. Natural language processing (NLP) is applied to identify causes and contributing factors of spills and leaks associated with E&P activity to reduce the frequency and severity of these events. Reviewing incident reports allow us to identify trends and areas of improvement, and NLP can be used to process the data quickly and improve data quality. Incident reports are typically free text that includes a description and cause of the event. One challenge with free-text descriptions is that further analysis and interpretation is needed in order to effectively analyze, trend, and learn from the available data. In addition, identifying the causes and contributing factors of a spill accurately can be time intensive. Two natural language processing methods, rule-based entity extraction and a machine learning model approach have been utilized to analyze a greater number of incident reports, which enables us to pinpoint causes of leaks, reduce the frequency of spills, and mitigate the impact of these events. This paper presents the challenges associated with learning from incident reports, particularly spills, and offers solutions that natural language processing can provide to improve data quality and contribute to spill reduction efforts in the context of an exploration and production company.

Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning

BackgroundAlthough previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk and phenotyping score for HFrEF, using Machine Learning (ML). MethodsPrior to modeling, patients in training and validation HF cohorts were assigned to 1 of 5 risk categories based on the composite endpoint of death, left ventricular assist device (LVAD) implantation or transplantation (DeLvTx), and rehospitalization in 6 months of follow-up using unsupervised clustering. The goal of our novel interpretable ML modeling approach, which is robust to missing data, was to predict this risk category (1, 2, 3, 4, or 5) using either invasive hemodynamics alone or a rich and inclusive feature set that included noninvasive hemodynamics (all features). The models were trained using the ESCAPE trial and validated using 4 advanced HF patient cohorts collected from previous trials, then compared with traditional ML models. Prediction accuracy for each of these 5 categories was determined separately for each risk category to generate 5 areas under the curve (AUCs, or C-statistics) for belonging to risk category 1, 2, 3, 4, or 5, respectively. ResultsAcross all outcomes, our models performed well for predicting the risk category for each patient. Accuracies of 5 separate models predicting a patient's risk category ranged from 0.896 +/- 0.074 to 0.969 +/- 0.081 for the invasive hemodynamics feature set and 0.858 +/- 0.067 to 0.997 +/- 0.070 for the all features feature set. ConclusionNovel interpretable ML models predicted risk categories with a high degree of accuracy. This approach offers a new paradigm for risk stratification that differs from prediction of a binary outcome. Prospective clinical evaluation of this approach is indicated to determine utility for selecting the best treatment approach for patients based on risk and prognosis.

Bi-directional prediction of hydrothermal carbonization characteristics of agroforestry and livestock wastes with variable components: Graph learning model-aided waste recycle

Agricultural resource management is essential in present-day society. The hydrothermal carbonization (HTC) is a promising technology for recovering valuable resources from wastes. However, there is a paucity of efficient approaches to predict and comprehend the agricultural waste HTC process. Machine learning modeling approaches have emerged as an important tool for simulating thermochemical reaction processes such as HTC. However, previous studies all limit to predicting products properties of single-component HTC, which are contrary to the complex composition of raw materials, and demonstrate poor generalization performance. Herein, the homogeneity graph data is employed to represent the HTC data with variable components. Then, based on graph encoder-decoder transformer method, a bi-directional prediction model is established to model the HTC process of agroforestry and livestock wastes. In the case of the forward prediction, the model can predict the composition and yield of hydrochar according to the given HTC conditions. The comparisons with the experimental data other than the training and validation datasets show that the prediction accuracy (R2) of ash, carbon and nitrogen contents of the hydrochar are 0.895, 0.908 and 0.840, respectively, which proves the good generalizability of the model. Moreover, owing to the embedding vector, which connects the reactant data with the product data in HTC, the model can also achieve backward prediction of HTC reaction conditions such as raw material composition and reaction temperature. Furthermore, by dimensionality reduction analysis of the embedding vector in the model, the similarity of any two HTC reactions can be quantified. The established HTC-graph neural network (GNN) model shows both high accuracy and good generalization ability in bi-directional prediction of HTC process. Based on the effectiveness of the proposed model and the given evaluation indexes, the corresponding HTC parameters can be determined, which greatly benefits to the resources saving and energy consumption reduction.

A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries

In this paper, machine learning (ML)/deep learning (DL) has been employed to predict the thermal runaway (TR) occurrence in Li-ion batteries (LIBs). State-of-the-art convolutional neural networks (CNNs) are adopted to forecast the evolution of the TR phenomenon with a classification approach of three distinct stages, namely, safe operation, critical condition of triggered the TR, and the actual TR occurrence. In addition, the “you look only once” (YOLO) object detection ML model is used to identify the location of the TR heat sources within the battery. The neural networks are trained on simulated thermal images, computed with the multiphysics modeling of LIBs. The multiphysics modeling approach comprises a coupled thermal, electrochemical (P2D model), and degradation sub-models. The degradation phenomenon leading to the TR considered in this paper is the solid electrolyte interface (SEI) formation/decomposition on the negative electrode. The proposed ML model exhibits high accuracy in predicting the TR evolution stages and heat source locations. The combined multiphysics and ML modeling approach developed in this work provides qualitative insights for ‘on-the-fly’ prediction of the TR, as well as a framework for extending data-driven methodologies to broad applications in electrochemistry.

Computational Design of Low Melting Eutectics of Molten Salts: A Combined Machine Learning and Thermodynamic Modeling Approach.

We develop a computational framework combining thermodynamic and machine learning models to predict the melting temperatures of molten salt eutectic mixtures (Teut). The model shows an accuracy of ∼6% (mean absolute percentage error) over the entire data set. Using this approach, we screen millions of combinatorial eutectics ranging from binary to hexanary, predict new mixtures, and propose design rules that lead to low Teut. We show that heterogeneity in molecular sizes, quantified by the molecular volume of the components, and mixture configurational entropy, quantified by the number of mixture components, are important factors that can be exploited to design low Teut mixtures. While predicting eutectic composition with existing techniques had proved challenging, we provide some preliminary models for estimating the compositions. The high-throughput screening technique presented here is essential to design novel mixtures for target applications and efficiently navigate the vast design space of the eutectic mixtures.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review.

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

Computational modelling for predicting rheological properties of composite modified asphalt binders

The complicated viscoelastic characteristics of asphalt binders make it a challenging task to precisely predict their rheological behavior. This study aims to investigate and compare the suitability of response surface methodology (RSM) and machine learning (ML) modeling approaches in predicting the complex modulus (G*), phase angle (δ), and rutting parameter (G*/sinδ) of Nano Silica (NS) and/or waste denim fiber (WDF) modified asphalt binders before and after short-term aging. To achieve this, an experimental scheme was designed for RSM and ML modeling with three variables including NS contents (0–6%), WDF contents (0–6%), and testing temperature (40–76 °C) as the inputs, and provided the G*, δ and G*/sinδ before and after short-term aging as the outputs. A wide range of ML algorithms was evaluated to determine the optimum ML model that can be used to accurately predict the rheological properties of NS/WDF-modified asphalt binders. RSM analysis results indicated that the G*, δ, and G*/sinδ of NS/WDF composite asphalt are significantly affected by the %NS, %WDF, and test temperatures. The RSM-developed models showed coefficient of determination (R2) values exceeding 0.97 for all responses, indicating adequate agreement between experimental results and models developed by RSM. From ML algorithms optimization and among all evaluated ML models, it was found that Gaussian process regression (GPR) exhibited the highest R2 with a value of (0.99) and the lowest Root Mean Square Error (RMSE) with a value of approximately 1%. The performance evaluation of the GPR model for predicting all responses showed a very small difference between the predicted and experimental results, highlighting the prediction accuracy of the developed ML models.

Emotional Responses to Religious Conversion: Insights from Machine Learning

This study aims to understand the feelings of newly converted Muslims when they narrated their pre- and post-conversion using the Machine Learning model and qualitative approach. The data set analyzed in this paper comes from in-depth interviews with 12 mualaf/ newly converted Muslims from various backgrounds. All recorded interviews were transcribed and filtered to remove any unnecessary or misaligned data to ensure that the data was fully aligned with the interview questions. To analyze emotional changes, we utilize natural language processing (NLP) algorithms, which enable us to extract and interpret emotional content from textual data sources, such as personal narratives. The analysis was performed in Google Colab and utilizing XLM-EMO, a fine-tuned multilingual emotion detection model that detects joy, anger, fear, and sadness emotions from text. The model was chosen because it supports Bahasa, as our interview was conducted in Bahasa. Furthermore, the model also has the best accuracy amongst its competitors, namely LS-EMO and UJ-Combi. The model also has great performance, with the overall average Macro-F1s for XLM-RoBERTa-large, XLM-RoBERTa-base, and XLM-Twitter-base are .86, .81, and .84. Furthermore, two psychologists compared emotion detection results from the XLM-EMO model to the raw input data, and an inductive content analysis was performed. This approach allowed us to identify the reasoning behind the emotions deemed pertinent and intriguing for our investigation. This study showed that Sadness is the most dominant emotion, constituting 46.67% of the total emotions in the pre-conversion context. On the other hand, joy emerges as the most dominant, constituting a substantial proportion of 57.73% among the emotions analyzed from post-conversion emotions data. Understanding the positive impact of religious conversion on emotions may inform mental health interventions and incorporate spiritual or religious elements into therapeutic approaches for individuals struggling with emotional issues, guiding individuals undergoing religious conversion and emphasizing the potential emotional benefits.

A chemistry-based explainable machine learning model based on NIR spectra for predicting wood properties and understanding wavelength selection

ABSTRACT A chemistry-based explainable machine learning (ML) approach was used to predict wood properties using near infrared (NIR) spectral data collected from rough and smooth surfaces, and to provide better understanding of the role of important NIR wavelengths (features) in the performance of ML models. NIR spectra collected from western hemlock (Tsuga heterophylla) and coastal Douglas-fir (Pseudotsuga menziesii) boards with rough and smooth surfaces were fed into random forest and TreeNet; a gradient boosting machine algorithm, for predicting wood density, modulus of elasticity (MOE) and modulus of rupture (MOR). The TreeNet model could predict the MOE, MOR, and density with R2 of 0.66, 0.64, and 0.64 using spectra collected from rough surface and R2 of 0.54, 0.46, and 0.46 using spectra collected from smooth surface. TreeNet outperformed the random forest, and for both ML algorithms higher R2 and lower error were obtained using NIR data collected from rough surfaces. This suggested that for Douglass fir and western hemlock, NIR spectra could be collected on a sawn surface prior to surface planing. However, it is difficult to generalize the impact of surface roughness on the performance of predictive model as different factors (e.g. what constitutes a smooth or rough surface, variability of data set in terms of wood properties) impact the success of predictive models. NIR features having the greatest influence on TreeNet models were examined and consistently had wood chemistry specific band assignments. The most important features occurred in the O-H first overtone, and C–H second overtone regions and a narrow zone (approximately 2400–2500 nm) of the C–H stretch C–C stretch combination region. Important features also differed by property and with surface roughness. Explaining ML model performance using the relative importance of the NIR features showed the importance of wood chemistry related information when developing models, however MOE and MOR TreeNet models based on smooth surface NIR spectra showed an increased importance of water related features. Overall, the chemistry-based explainable machine learning model approach allows for identification of important NIR features, and regions, and aids in understanding how they contribute to the performance of NIR-based wood property predictive models.

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.