Selecting Computational Models for Asset Management: Financial Econometrics versus Machine Learning—Is There a Conflict?

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

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Computing Possible Futures

10.1093/oso/9780198846420.001.0001 ◽

2019 ◽

Cited By ~ 2

Author(s):

William B. Rouse

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computational Modeling ◽

Mental Model ◽

Data Analytics ◽

Computational Models ◽

Senior Managers ◽

Interactive Visualizations ◽

Use Of Models ◽

Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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Assessment of Machine Learning-Based Audiovisual Quality Predictors

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3430376 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-22

Author(s):

Mythili K. ◽

Manish Narwaria

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Quality Assessment ◽

Computational Models ◽

Model Development ◽

Multimedia Communication ◽

Current Approach ◽

Learning Ability ◽

Data Uncertainty ◽

Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

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Pervasive Lying Posture Tracking

Sensors ◽

10.3390/s20205953 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5953 ◽

Cited By ~ 1

Author(s):

Parastoo Alinia ◽

Ali Samadani ◽

Mladen Milosevic ◽

Hassan Ghasemzadeh ◽

Saman Parvaneh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Computational Models ◽

Learning Algorithms ◽

Pressure Sensors ◽

Machine Learning Algorithms ◽

Sensor System ◽

Accurate Detection ◽

Research Questions ◽

Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

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Hybrid machine learning process improves operations in South Australian trial

Water e-Journal ◽

10.21139/wej.2020.027 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-16

Author(s):

Kalan Braun ◽

James Ireland ◽

Jonathan Klaric

Keyword(s):

Machine Learning ◽

Asset Management ◽

South Australia ◽

Cloud Data ◽

Engineering Teams ◽

Deterministic Modelling ◽

Full Production ◽

Machine Learning Applications ◽

Hybrid Machine ◽

One Year

TRILITY has developed a production cloud data management solution, KOIOS DatalytiX to provide hybrid machine learning applications and deterministic modelling tools to its operations, asset management, and engineering teams. Two optimiser applications for chemical coagulant prediction and filtration performance were deployed in the Riverland, South Australia at 10 conventional water treatment plants in 2018, to full production in 2019. One year later the impacts on the optimisation behaviour of operations teams, process performance and trihalomethane (THM) reduction outcomes are discussed.

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Efficient hyperparameter-tuned machine learning approach for estimation of supercapacitor performance attributes

Journal of Physics Communications ◽

10.1088/2399-6528/ac3574 ◽

2021 ◽

Author(s):

Syed Ishtiyaq Ahmed ◽

Sreevatsan Radhakrishnan ◽

Binoy B Nair ◽

Rajagopalan Thiruvengadathan

Keyword(s):

Machine Learning ◽

Surface Area ◽

Computational Models ◽

Performance Metrics ◽

Structural Features ◽

Optimization Techniques ◽

Storage Device ◽

Micropore Surface Area ◽

Supercapacitor Performance ◽

Carbon Based

Abstract Recent years have witnessed the rise of supercapacitor as effective energy storage device. Specifically, carbon-based electrodes have been experimentally well studied and used in the fabrication of supercapacitors due to their excellent electrochemical properties. This work reports the development and utilization of highly tuned and efficient Machine Learning (ML) models that give insights into correlation between structural features of electrodes and supercapacitor performance metrics namely specific capacitance, power density and energy density. Artificial Neural Networks (ANN) and Random Forest (RF) models have been employed to predict the various in-operando performance metrics of carbon-based supercapacitors based on three input features such as mesopore surface area, micropore surface area and scan rate. Experimentally measured values of these parameters used for training and testing these two models have been extracted from a set of research papers reported in literature. The optimization techniques and various tuning methodologies adopted for identifying model hyperparameters are discussed in this paper. The authors demonstrate the importance of hyperparameter tuning and optimization in building accurate and reliable computational models.

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Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

Frontiers in Genetics ◽

10.3389/fgene.2021.742902 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hae Deok Jung ◽

Yoo Jin Sung ◽

Hyun Uk Kim

Keyword(s):

Machine Learning ◽

Computational Modeling ◽

Cancer Treatment ◽

Effective Treatment ◽

Cancer Cells ◽

Computational Models ◽

Omics Data ◽

Drug Resistant ◽

Learning Models ◽

Machine Learning Models

Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

10.20944/preprints202002.0178.v1 ◽

2020 ◽

Author(s):

Robert Ancuceanu ◽

Marilena Viorica Hovanet ◽

Adriana Iuliana Anghel ◽

Florentina Furtunescu ◽

Monica Neagu ◽

...

Keyword(s):

Machine Learning ◽

Liver Injury ◽

Computational Models ◽

Liver Toxicity ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Drug Induced ◽

Reference Drug ◽

Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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Applications in Machine Learning

Particle Swarm Optimization and Intelligence - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-61520-666-7.ch006 ◽

2010 ◽

pp. 149-167 ◽

Cited By ~ 10

Author(s):

E. Parsopoulos Konstantinos ◽

N. Vrahatis Michael

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Literature Review ◽

General Framework ◽

Computational Models ◽

Cognitive Maps ◽

Fuzzy Cognitive Maps ◽

Optimization Process ◽

Learning Problems

This chapter presents the fundamental concepts regarding the application of PSO on machine learning problems. The main objective in such problems is the training of computational models for performing classification and simulation tasks. It is not our intention to provide a literature review of the numerous relative applications. Instead, we aim at providing guidelines for the application and adaptation of PSO on this problem type. To achieve this, we focus on two representative cases, namely the training of artificial neural networks, and learning in fuzzy cognitive maps. In each case, the problem is first defined in a general framework, and then an illustrative example is provided to familiarize readers with the main procedures and possible obstacles that may arise during the optimization process.

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

International Journal of Molecular Sciences ◽

10.3390/ijms21062114 ◽

2020 ◽

Vol 21 (6) ◽

pp. 2114

Author(s):

Robert Ancuceanu ◽

Marilena Viorica Hovanet ◽

Adriana Iuliana Anghel ◽

Florentina Furtunescu ◽

Monica Neagu ◽

...

Keyword(s):

Machine Learning ◽

Liver Injury ◽

Computational Models ◽

Liver Toxicity ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Drug Induced ◽

Reference Drug ◽

Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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