scholarly journals Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhi-Min Zou ◽  
De-Hua Chang ◽  
Hui Liu ◽  
Yu-Dong Xiao
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Predictive Models ◽  
Critical Role ◽  
Model Development ◽  
Human Learning ◽  
Therapeutic Outcome ◽  
Treatment Modalities ◽  
Hidden Patterns ◽  
Prognostic Prediction

AbstractWith the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.

scholarly journals Flame: an open source framework for model development, hosting, and usage in production environments

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Manuel Pastor ◽  
José Carlos Gómez-Tamayo ◽  
Ferran Sanz
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Predictive Models ◽  
Web Application ◽  
Model Building ◽  
Molecular Descriptor ◽  
Model Development ◽  
Graphic Interface ◽  
Chemical Structures ◽  
Production Environments

AbstractThis article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

scholarly journals A Novel Performance Measure for Machine Learning Classification

2021 ◽  
Vol 13 (1) ◽  
pp. 11-19
Author(s):  
Mingxing Gong
Keyword(s):  
Machine Learning ◽  
Performance Measures ◽  
Critical Role ◽  
Model Development ◽  
Performance Measure ◽  
Classification Problems ◽  
Comprehensive Overview ◽  
Machine Learning Classification ◽  
And Performance ◽  
Evaluation Metric

Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.

scholarly journals A Machine Learning-Based Model to Predict Survival After Transarterial Chemoembolization for BCLC Stage B Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Huapeng Lin ◽  
Lingfeng Zeng ◽  
Jing Yang ◽  
Wei Hu ◽  
Ying Zhu
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Proportional Hazards ◽  
External Validation ◽  
Model Development ◽  
Survival Rates ◽  
Model Performance ◽  
Cox Proportional Hazards ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models

ObjectiveWe sought to develop and validate a novel prognostic model for predicting survival of patients with Barcelona Clinic Liver Cancer Stages (BCLC) stage B hepatocellular carcinoma (HCC) using a machine learning approach based on random survival forests (RSF).MethodsWe retrospectively analyzed overall survival rates of patients with BCLC stage B HCC using a training (n = 602), internal validation (n = 301), and external validation (n = 343) groups. We extracted twenty-one clinical and biochemical parameters with established strategies for preprocessing, then adopted the RSF classifier for variable selection and model development. We evaluated model performance using the concordance index (c-index) and area under the receiver operator characteristic curves (AUROC).ResultsRSF revealed that five parameters, namely size of the tumor, BCLC-B sub-classification, AFP level, ALB level, and number of lesions, were strong predictors of survival. These were thereafter used for model development. The established model had a c-index of 0.69, whereas AUROC for predicting survival outcomes of the first three years reached 0.72, 0.71, and 0.73, respectively. Additionally, the model had better performance relative to other eight Cox proportional-hazards models, and excellent performance in the subgroup of BCLC-B sub-classification B I and B II stages.ConclusionThe RSF-based model, established herein, can effectively predict survival of patients with BCLC stage B HCC, with better performance than previous Cox proportional hazards models.

scholarly journals Flame: An Open Source Framework for Model Development, Hosting, and Usage in Production Environments

2020 ◽  
Author(s):  
Manuel Pastor ◽  
José Carlos Gómez-Tamayo ◽  
Ferran Sanz
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Predictive Models ◽  
Web Application ◽  
Model Building ◽  
Molecular Descriptor ◽  
Model Development ◽  
Graphic Interface ◽  
Chemical Structures ◽  
Production Environments

Abstract This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application, with a Python backend and a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures, since the software supports structural normalization, molecular descriptor generation and machine learning building, using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code adding unlimited model customization. Models generated with Flame can be easily exported facilitating collaborative model development. All models are stored in a persistent model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly three years of development in collaboration with users from pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high quality predictive models based on shared legacy data for assessing the safety of drug candidates.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

scholarly journals The GNAQ T96S Mutation Affects Cell Signaling and Enhances the Oncogenic Properties of Hepatocellular Carcinoma

2021 ◽  
Vol 22 (6) ◽  
pp. 3284
Author(s):  
Eugene Choi ◽  
Sung Jean Park ◽  
Gunhee Lee ◽  
Seung Kew Yoon ◽  
Minho Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Vector ◽  
Signal Transduction Pathway ◽  
Inhibitory Effect ◽  
The Cancer Genome Atlas ◽  
Treatment Modalities ◽  
Protein Signaling ◽  
Wild Type ◽  
Anchorage Independent Growth ◽  
Mapk Pathways

Hepatocellular carcinoma (HCC), the most common malignant tumor in the liver, grows and metastasizes rapidly. Despite advances in treatment modalities, the five-year survival rate of HCC remains less than 30%. We sought genetic mutations that may affect the oncogenic properties of HCC, using The Cancer Genome Atlas (TCGA) data analysis. We found that the GNAQ T96S mutation (threonine 96 to serine alteration of the Gαq protein) was present in 12 out of 373 HCC patients (3.2%). To examine the effect of the GNAQ T96S mutation on HCC, we transfected the SK-Hep-1 cell line with the wild-type or the mutant GNAQ T96S expression vector. Transfection with the wild-type GNAQ expression vector enhanced anchorage-independent growth, migration, and the MAPK pathways in the SK-Hep-1 cells compared to control vector transfection. Moreover, cell proliferation, anchorage-independent growth, migration, and the MAPK pathways were further enhanced in the SK-Hep-1 cells transfected with the GNAQ T96S expression vector compared to the wild-type GNAQ-transfected cells. In silico structural analysis shows that the substitution of the GNAQ amino acid threonine 96 with a serine may destabilize the interaction between the regulator of G protein signaling (RGS) protein and GNAQ. This may reduce the inhibitory effect of RGS on GNAQ signaling, enhancing the GNAQ signaling pathway. Single nucleotide polymorphism (SNP) genotyping analysis for Korean HCC patients shows that the GNAQ T96S mutation was found in only one of the 456 patients (0.22%). Our data suggest that the GNAQ T96S hotspot mutation may play an oncogenic role in HCC by potentiating the GNAQ signal transduction pathway.

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