Advanced Computational Methods Research Articles

Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent

Understanding the solubility of drug particles in solvents has remained a big challenge in different fields. Development of advanced computational methods to predict the solubility of drugs is an important necessity due to the difficulty and time-consuming characteristics of experimental measurements. This study used hybrid machine learning (ML) models utilizing two inputs, including Pressure (X1) and Temperature (X2), to investigate the current data, and correlate the solubility of drug particles in supercritical solvent. The methods of Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB) regression models were used to build the models on the available data. RF, ET, and GB have R-squared of 0.857, 0.998, and 0.992, based on the analysis results. Additionally, in terms of MAE, they illustrated error value 2.90E-06, 1.98E-06, and 1.10E-06, respectively. One more metric to consider is MAPE, in which the error rates for the three regions were 3.15E-01, 2.27E-01, and 1.16E-01, respectively. The DT method was chosen as the best method, and can be used to find optimal amounts, which is summarized as a vector: (×1 = 383, X2 = 333.15, Y = 6.004e-05).

Pengaruh Jiwa Kewirausahaan dan Kemandirian Pribadi Terhadap Keberhasilan Usaha Kuliner Martabak di Jalan P. Antarsari Bandar Lampung

Amid the difficult circumstances of the pandemic, many of Martabak's merchants have expressed concern about declining sales. To address this situation, this study aims to explore the interplay between entrepreneurial spirit, personal independence, and business success of Martabak dealers on Antarsari Road. This study is based on robust numerical and quantitative data and utilizes advanced computational and statistical methods supported by SPSS 25 software for comprehensive data analysis. The findings highlight that both entrepreneurship and individual independence play an important role in the success of Antarsari Street Martabak merchants. As businesses grapple with the unprecedented challenges of the pandemic, these key factors are proving to be key drivers affecting overall business outcomes in this situation. By uncovering these key connections, this analysis aims to provide Martabak vendors with valuable insights to help them manage uncertainty in the pandemic era and adjust their strategies for lasting success.

Computational framework for polymer synthesis to study dielectric properties using polarizable reactive molecular dynamics

The increased energy and power density required in modern electronics poses a challenge for designing new dielectric polymer materials with high energy density while maintaining low loss at high applied electric fields. Recently, an advanced computational screening method coupled with hierarchical modelling has accelerated the identification of promising high energy density materials. It is well known that the dielectric response of polymeric materials is largely influenced by their phases and local heterogeneous structures as well as operational temperature. Such inputs are crucial to accelerate the design and discovery of potential polymer candidates. However, an efficient computational framework to probe temperature dependence of the dielectric properties of polymers, while incorporating effects controlled by their morphology is still lacking. In this paper, we propose a scalable computational framework based on reactive molecular dynamics with a valence-state aware polarizable charge model, which is capable of handling practically relevant polymer morphologies and simultaneously provide near-quantum accuracy in estimating dielectric properties of various polymer systems. We demonstrate the predictive power of our framework on high energy density polymer systems recently identified through rational experimental-theoretical co-design. Our scalable and automated framework may be used for high-throughput theoretical screenings of combinatorial large design space to identify next-generation high energy density polymer materials.

Artificial Intelligence in Health Care-Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study.

Artificial intelligence (AI) is as a branch of computer science that uses advanced computational methods such as machine learning (ML), to calculate and/or predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving healthcare, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in healthcare management. To support and ensure safe use of AI/ML technologies in healthcare, the team worked to better understand: 1) patient information and training needs, 2) the factors that influence patients' perceived value and trust in AI/ML healthcare applications; and 3) on how best to support safe and appropriate use of AI/ML enabled devices and applications among people living with diabetes. To understand general patient perspectives and information needs related to the use of AI/ML in healthcare, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=40) and interviews with providers (n=6) in Alaska, Idaho, and Virginia. Grounded Theory guided data gathering, synthesis, and analysis. Thematic content and constant comparison analysis were used to identify relevant themes and sub-themes. Inductive approaches were used to link data to key concepts including preferred patient-provider-interactions, patient perceptions of trust, accuracy, value, assurances, and information transparency. Key summary themes and recommendations focused on: 1) patient preferences for AI/ML enabled device and/or application information; 2) patient and provider AI/ML-related device and/or application training needs; 3) factors contributing to patient and provider trust in AI/ML enabled devices and/or application; and 4) AI/ML-related device and/or application functionality and safety considerations. A number of participant (patients and providers) recommendations to improve device functionality to guide information and labeling mandates (e.g., links to online video resources, and access to 24/7 live in-person or virtual emergency support). Other patient recommendations include: 1) access to practice devices; 2) connection to local supports and reputable community resources; 3) simplified display and alert limits. Recommendations from both patients and providers could be used by Federal Oversight Agencies to improve utilization of AI/ML monitoring of technology use in diabetes, improving device safety and efficacy.

Morphological analysis of historical landscapes based on cultural DNA approach

Recent trends in landscape architecture investigate new approaches, methods, and technologies to understand, monitor, manage, continuity, and sustainably develop heritage landscapes. Cultural DNA (CD) represents designs' transferable geometric, behavioural and functional properties. The morphological structure of Persian historical gardens possesses discoverable hidden patterns, computable through the CD's mathematical and computational models. This study aims to investigate and discover effective parameters in the spatial structure of historical Persian gardens using Space Syntax. Six Persian Gardens were surveyed, and spatial structures were analysed using DepthMap10 software. The results show that these spaces follow meaningful mathematical patterns and rules that can be formulated in the context of generative processes and generalized for the evolutionary continuity of gardens. However, further research is proposed to develop more advanced computational methods, such as artificial intelligence (AI) algorithms and AI-based decision support tools, to help to generate design scenarios and to transmit culture through the design process.

The Application of Artificial Intelligence Models in Commercial Banks − Opportunities and Threats

One of the main sectors that makes heavy use of the development of advanced computational methods is the banking sector. The goals of our research are as follows: 1) to compare scientific and regulatory approaches to defining artificial intelligence (AI) and machine learning (ML), 2) to propose AI and ML definitions for regulatory purposes that allow us to clearly state if a given method is AI/ML or not, 3) to compare the complex quantitative methods applied in banking in terms of complexity and interpretability in order to provide a clear classification of methods to the interested parties (practitioners and management), 4) to propose a possible approach towards the further development of quantitative methods in the areas of required strict interpretability. Our literature review focuses on the definitions of AI/ML applied by scientists and regulators, as well as the proposals of application of complex quantitative solutions in different banking domains. We propose practical definitions of AI and ML based on the current state of the art and requirements of clarity in the banking industry (a very limited risk appetite regarding regulations non‑compliance) and compare quantitative methods applied in different banking domains. For regulatory purposes, we propose general and inclusive definitions of AI and ML which allow for a clear classification of specific methods. In the case of strict requirements towards the interpretability of applied methods, we propose a gradual and controlled increase in the complexity of existing solutions. Therefore, we propose the differentiation of quantitative methods in terms of interpretability and complexity. We also think that the definitions of AI/ML in further regulations should make it possible to clearly say whether particular approaches are AI/ML. Our research is directed to policymakers, practitioners, and executives related to the banking sector.

LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 ± 0.0043, 0.9449 ± 0.022, 0.9375 ± 0.0331 and 0.9556 ± 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. TLDAEXC utilizes disease semantic similarity, lncRNA expression similarity, and Gaussian interaction profile kernel similarity of lncRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder to extract reduced features, and an XGBoost classifier is used to predict the lncRNA-disease associations based on the reduced features. The fivefold and tenfold cross-validation experiments on a benchmark dataset showed that LDAEXC could achieve AUC scores of 0.9676 and 0.9682, respectively, significantly higher than other state-of-the-art similar methods.

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA).

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

The Interaction of SRL-2 Peptide with LRP-1 Receptor and Identification of Breast Cancer Related Biomarkers: An In-silico Approach

Background: Breast cancer is a multifaceted disease characterized by genetic and epigenetic changes that lead to uncontrolled cell growth and metastasis. Early detection and treatment are crucial for managing diseases. Objectives: The objective of this study is to investigate the potential of chimeric peptides for drug delivery and to identify biomarkers associated with breast cancer. Recent studies have shown that the low-density lipoprotein receptor-related protein 1 (LRP-1) receptor has a significant impact on the development of breast cancer. In order to facilitate the identification of biomarkers, we have created a chimeric peptide that has been proven to bind successfully to the LRP-1 receptor. Methods: To identify biomarkers, we utilized advanced computational methods to conduct a meta-analysis of microarray data. Specifically, the g:Profiler and eXpression2Kinases (X2K) databases were utilized to identify gene ontologies and transcription factors. We then used the Human Protein Atlas to identify and assess crucial gene expressions. Results: Our results demonstrated that nucleolar and spindle-associated protein 1 (NUSAP1), melatonin receptor 1A (MELT), and cyclin-dependent kinase 1 (CDK1) are three hub genes that play pivotal roles in the pathogenesis of breast cancer. Conclusions: The research findings provide a deeper understanding of the molecular mechanisms involved in developing breast cancer. These findings have significant implications for developing novel therapies and diagnostics for this disease.

The Interaction of SRL-2 Peptide with LRP-1 Receptor and Identification of Breast Cancer Related Biomarkers: An In-silico Approach

Background: Breast cancer is a multifaceted disease characterized by genetic and epigenetic changes that lead to uncontrolled cell growth and metastasis. Early detection and treatment are crucial for managing diseases. Objectives: The objective of this study is to investigate the potential of chimeric peptides for drug delivery and to identify biomarkers associated with breast cancer. Recent studies have shown that the low-density lipoprotein receptor-related protein 1 (LRP-1) receptor has a significant impact on the development of breast cancer. In order to facilitate the identification of biomarkers, we have created a chimeric peptide that has been proven to bind successfully to the LRP-1 receptor. Methods: To identify biomarkers, we utilized advanced computational methods to conduct a meta-analysis of microarray data. Specifically, the g:Profiler and eXpression2Kinases (X2K) databases were utilized to identify gene ontologies and transcription factors. We then used the Human Protein Atlas to identify and assess crucial gene expressions. Results: Our results demonstrated that nucleolar and spindle-associated protein 1 (NUSAP1), melatonin receptor 1A (MELT), and cyclin-dependent kinase 1 (CDK1) are three hub genes that play pivotal roles in the pathogenesis of breast cancer. Conclusions: The research findings provide a deeper understanding of the molecular mechanisms involved in developing breast cancer. These findings have significant implications for developing novel therapies and diagnostics for this disease.

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes

The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational methods to analyze large-scale, high-dimensional genomic data. However, traditional machine learning methods face a challenge in handling the high-dimensional, low-sample size problem that is shown in most genomic data sets. To address this, our group has developed geometric network analysis techniques on multi-omics data in connection with prior biological knowledge derived from protein-protein interactions (PPIs) or pathways. Geometric features obtained from the genomic network, such as Ollivier-Ricci curvature and the invariant measure of the associated Markov chain, have been shown to be predictive of survival outcomes in various cancers. In this study, we propose a novel supervised deep learning method called geometric graph neural network (GGNN) that incorporates such geometric features into deep learning for enhanced predictive power and interpretability. More specifically, we utilize a state-of-the-art graph neural network with sparse connections between the hidden layers based on known biology of the PPI network and pathway information. Geometric features along with multi-omics data are then incorporated into the corresponding layers. The proposed approach utilizes a local–global principle in such a manner that highly predictive features are selected at the front layers and fed directly to the last layer for multivariable Cox proportional-hazards regression modeling. The method was applied to multi-omics data from the CoMMpass study of multiple myeloma and ten major cancers in The Cancer Genome Atlas (TCGA). In most experiments, our method showed superior predictive performance compared to other alternative methods.

Co-loading of doxorubicin and gemcitabine on folic acid functionalized carbon nanotubes; a molecular dynamics simulation study

Nanomedicine based drug delivery design was performed in this study for two drugs of small-molecule API by advanced computational method. The co-loading of doxorubicin (DOX) and gemcitabine (GEM) on pristine and functionalized carbon nanotubes (CNTs) was investigated by molecular dynamics (MD) simulation. The functionalization of CNTs was done with carboxyl (CNT-COO) and folic acid groups (CNT-FA). Different ratios of DOX and GEM were simulated. Functionalized nanotubes have stronger binding energy with drugs due to electrostatic interactions. CNT-FA showed the highest amount of GEM encapsulation. In the co-loading of DOX and GAM, GEM tends to encapsulate inside the CNT because it has a weaker dipole moment. Encapsulation of drugs was investigated by radial function analysis. The results showed that the CNT-DA in a molar ratio of 1:2 (DOX:GEM) is the best selection for co-loading of DOX and GEM.

Computational Structural Validation of CYP2C9 Mutations and Evaluation of Machine Learning Algorithms in Predicting the Therapeutic Outcomes of Warfarin.

The study aimed to identify the key pharmacogenetic variable influencing the therapeutic outcomes of warfarin using machine learning algorithms and bioinformatics tools. Warfarin, a commonly used anticoagulant drug, is influenced by cytochrome P450 (CYP) enzymes, particularly CYP2C9. MLAs have been identified to have great potential in personalized therapy. The purpose of the study was to evaluate MLAs in predicting the critical outcomes of warfarin therapy and validate the key predictor genotyping variable using bioinformatics tools. An observational study was conducted on adults receiving warfarin. Allele discrimination method was used for estimating the single nucleotide polymorphisms (SNPs) in CYP2C9, VKORC1, and CYP4F2. MLAs were used for identifying the significant genetic and clinical variables in predicting the poor anticoagulation status (ACS) and stable warfarin dose. Advanced computational methods (SNPs' deleteriousness and impact on protein destabilization, molecular dockings, and 200ns molecular dynamics simulations) were employed for examining the influence of CYP2C9 SNPs on structure and function. Machine learning algorithms revealed CYP2C9 to be the most important predictor for both outcomes compared to the classical methods. Computational validation confirmed the altered structural activity, stability, and impaired functions of protein products of CYP2C9 SNPs. Molecular docking and dynamics simulations revealed significant conformational changes with mutations R144C and I359L in CYP2C9. We evaluated various MLAs in predicting the critical outcome measures associated with warfarin and observed CYP2C9 as the most critical predictor variable. The results of our study provide insight into the molecular basis of warfarin and the CYP2C9 gene. A prospective study validating the MLAs is urgently needed.

Removing geographic boundaries from liver allocation: A method for designing continuous distribution scores.

The Organ Procurement and Transplantation Network (OPTN) is eliminating geographic boundaries in liver allocation, in favor of continuous distribution. Continuous distribution allocates organs via a composite allocation score (CAS): a weighted sum of attributes like medical urgency, candidate biology, and placement efficiency. The opportunity this change represents, to include new variables and features for prioritizing candidates, will require lengthy and contentious discussions to establish community consensus. Continuous distribution could instead be implemented rapidly by computationally translating the allocation priorities for pediatric, status 1, and O/B blood type liver candidates that are presently implemented via geographic boundaries into points and weights in a CAS. Using simulation with optimization, we designed a CAS that is minimally disruptive to existing prioritizations, and that eliminates geographic boundaries and minimizes waitlist deaths without harming vulnerable populations. Compared with Acuity Circles (AC) in a 3-year simulation, our optimized CAS decreased deaths from 7771.2 to 7678.8 while decreasing average (272.66 NM vs. 264.30 NM) and median (201.14 NM vs. 186.49 NM) travel distances. Our CAS increased travel only for high MELD and status 1 candidates (423.24 NM vs. 298.74 NM), and reduced travel for other candidates (198.98 NM vs. 250.09 NM); overall travel burden decreased. Our CAS reduced waitlist deaths by sending livers for high-MELD and status 1 candidates farther, while keeping livers for lower MELD candidates nearby. This advanced computational method can be applied again after wider discussions of adding new priorities conclude; our method designs score weightings to achieve any specified feasible allocation outcomes.

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials.

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Syntactic Pattern Recognition for the Prediction of L-Type Pseudoknots in RNA

The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more accurate results in all areas of interest. One highly challenging task, in terms of RNA analysis, is the prediction of its structure, which provides valuable information about how it transforms and operates numerous significant tasks in organisms. In this paper, we focus on the prediction of the 2-D or secondary structure of RNA, specifically, on a rare but yet complex type of pseudoknot, the L-type pseudoknot, extending our previous framework specialized for H-type pseudoknots. We propose a grammar-based framework that predicts all possible L-type pseudoknots of a sequence in a reasonable response time, leveraging also the advantages of core biological principles, such as maximum base pairs and minimum free energy. In order to evaluate the effectiveness of our methodology, we assessed four performance metrics: precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology outperformed the other three well known methods in terms of Precision, with a score of 0.844, while other methodologies scored 0.500, 0.333, and 0.308. Regarding the F1-score, our platform scored 0.671, while other methodologies scored 0.661, 0.449, and 0.449. The proposed methodology surpassed all methods in terms of the MCC metric, achieving a score of 0.521. The proposed method was added to our RNA toolset, which aims to enhance the capabilities of biologists in the prediction of RNA motifs, including pseudoknots, and holds the potential to be applied in a multitude of biological domains, including gene therapy, drug design, and comprehending RNA functionality. Furthermore, the suggested approach can be employed in conjunction with other methodologies to enhance the precision of RNA structure prediction.

Big Data in Oncology Nursing Research: State of the Science

ObjectiveTo review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data. Data SourcesPeer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines. ConclusionBig data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns. Implications for Nursing PracticeWith their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.

An advanced computational method for studying drug nanonization using green supercritical-based processing for improvement of pharmaceutical bioavailability in aqueous media

In this study, we implemented and compared various non-mechanistic based models for prediction of drug solubility in supercritical solvent. The data were collected from references and the models were built considering various operational circumstances. Small data sets, like the solubility data used in this study, have always been one of the challenges for modeling in machine learning method. In this study, in order to solve the regression problem related to the solubility of drugs, which includes 32 laboratory data, we implemented and studied models that are naturally compatible with very small data like solubility data of drugs in solvents. These models included Random Forest (RF), KNN and Extra Tree (ET). After obtaining the best settings for each model, their final results were compared in terms of accuracy for predicting drug solubility. The ET model had the best result with a score of 0.9999 on the R2 criterion. Random forests with 0.978 and KNN with 0.972 also had acceptable regression results. Finally, the trained model was used to display and evaluate the effect of input parameters like pressure and temperature on drug solubility to understand the process.

Abstract 3140: Computational modeling of stromal-immune cell interactions in triple-negative breast cancer

Abstract Breast cancer is the most common cancer type in women worldwide, and it represents the second most common cause of cancer deaths. Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) has the worst prognosis. The tumor microenvironment (TME) of TNBC is a complex ecosystem, characterized by the dynamic interactions between stromal, immune, extracellular matrices (ECM), and cancer cells. Understanding the fibroblasts as a heterogeneous population with pleiotropic actions on TME, mediated by ECM formation and interactions with immune cells, is necessary to develop approaches to interfere with the loss of ECM homeostasis, promote immunity, and improve the outcome of concomitant therapeutic approaches in TNBC and other cancer types. Our recent studies suggested distinct fibroblast sub cell types of varied biosynthesis of ECM components in the TME of TNBC, which have significant associations with the infiltration and activity of immune cells. In this study, we developed advanced computational methods to characterize the role of fibroblast sub cell types, their functional variations, and stromal-immune cell interactions in TNBC, by integrative analysis of tissue, single-cell, and spatial multi-omics data. We hypothesized that the genes involved in sub cell types and functional variations of fibroblast cells may form distinct data structures in omics data, which are identifiable by carefully designed pattern recognition methods. We also hypothesized that different subtypes and functional activities of fibroblast cells lead to varied ECM formations, metabolic shifts, and affect the infiltration and functions of immune cells, which could be identified as the spatial regions with distinct expression changes of stromal and immune marker genes.We first reconstructed TNBC TME context-specific gene sets of fibroblast sub cell types and functions. We assemble a series of computational methods including tissue data deconvolution, functional module detection on scRNA-seq data, and metabolic flux analysis into an integrative data analysis framework to characterize fibroblast cells and their functions in the TME of TNBC and reconstruct context-specific pathways of fibroblast sub cell types and functions. We further detected spatially dependent ECM formation and stromal-immune cell interactions in TNBC by using spatial resolved transcriptomics data. We observed spatially dependent variations of (1) sub cell type and cell type-specific functions, and (2) stromal-immune cell interactions, in the TME of TNBC. Citation Format: Yuhui Wei, Haiqi Zhu, Xiao Wang, Xinyu Zhou, Jia Wang, Tingbo Guo, Pengtao Dang, Sha Cao, Chi Zhang. Computational modeling of stromal-immune cell interactions in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3140.

Towards a precise understanding of social entrepreneurship: An integrated bibliometric–machine learning based review and research agenda

This paper focuses on building a more precise and comprehensive understanding of the state of social entrepreneurship (SE) research by using an integrated bibliometric and unsupervised machine learning approach. Bibliometric analysis, along with Latent Dirichlet Allocation (LDA) for topic modeling enables us to identify key trends and themes in the SE domain. This approach is superior to tools and methods used in the past, which primarily employed systematic literature reviews and bibliometric analysis. While systematic manual literature reviews become impractical as the literature grows, bibliometric analysis focuses on the most cited articles, ignoring recent influential work, suffering from citation biases, and giving more weight to impact over thematic discovery. The methodology used by us overcomes these issues by first extracting large amounts of information through advanced computational methods and then using unsupervised machine learning to discover the latent themes and topics in this large collection of publications. This research uses the Scopus and Web of Science (WoS) databases to extract corpora of 3844 texts (titles, abstracts, and keywords) from published research on SE. We decipher the key trends in the literature and segregate them into three broad categories – individual attributes and motivation, organizational actions, and institutional conditions and development, with 21 sub-topics to enhance the understanding of this field of inquiry. This study is the first in the entrepreneurship domain to use this integrated approach to review the literature, and the findings lay the groundwork for future research.