Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Sub Saharan Africa ◽

Sub Saharan ◽

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

F1000Research ◽

10.12688/f1000research.21539.1 ◽

2020 ◽

Vol 9 ◽

pp. 62 ◽

Cited By ~ 1

Author(s):

Colby T. Ford ◽

Daniel Janies

Keyword(s):

Machine Learning ◽

Computational Models ◽

Artemisinin Resistance ◽

Parasite Clearance ◽

Sub Saharan Africa ◽

Sub Saharan ◽

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

F1000Research ◽

10.12688/f1000research.21539.5 ◽

2020 ◽

Vol 9 ◽

pp. 62 ◽

Cited By ~ 1

Author(s):

Colby T. Ford ◽

Daniel Janies

Keyword(s):

Machine Learning ◽

Computational Models ◽

Artemisinin Resistance ◽

Parasite Clearance ◽

Sub Saharan Africa ◽

Sub Saharan ◽

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

F1000Research ◽

10.12688/f1000research.21539.2 ◽

2020 ◽

Vol 9 ◽

pp. 62 ◽

Cited By ~ 1

Author(s):

Colby T. Ford ◽

Daniel Janies

Keyword(s):

Machine Learning ◽

Computational Models ◽

Artemisinin Resistance ◽

Parasite Clearance ◽

Sub Saharan Africa ◽

Sub Saharan ◽

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria

10.1101/856922 ◽

2019 ◽

Author(s):

Colby T. Ford ◽

Daniel Janies

Keyword(s):

Machine Learning ◽

Computational Models ◽

Artemisinin Resistance ◽

Parasite Clearance ◽

Final Model ◽

Massively Parallel Processing ◽

ABSTRACTAntiparasitic resistance in malaria is a growing concern affecting many areas of the eastern world. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway.The 2019 Malaria Dream Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict Artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles.In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

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 ◽

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.

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 ◽

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.

The Diversity of the Plasmodium falciparum K13 Propeller Domain Did Not Increase after Implementation of Artemisinin-Based Combination Therapy in Uganda

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01234-19 ◽

2019 ◽

Vol 63 (10) ◽

Author(s):

Melissa D. Conrad ◽

Sam L. Nsobya ◽

Philip J. Rosenthal

Keyword(s):

Plasmodium Falciparum ◽

Southeast Asia ◽

Artemisinin Resistance ◽

Standard Of Care ◽

Parasite Clearance ◽

Sub Saharan Africa ◽

Clinical Isolates ◽

Nucleotide Polymorphisms ◽

Content Type ◽

Sub Saharan

ABSTRACT Artemisinin-based combination therapies (ACTs) are the standard of care to treat uncomplicated falciparum malaria. However, resistance to artemisinins, defined as delayed parasite clearance after therapy, has emerged in Southeast Asia, and the spread of resistance to sub-Saharan Africa could have devastating consequences. Artemisinin resistance has been associated in Southeast Asia with multiple nonsynonymous single nucleotide polymorphisms (NS-SNPs) in the propeller domain of the gene encoding the Plasmodium falciparum K13 protein (K13PD). Some K13PD NS-SNPs have been seen in Africa, but the relevance of these mutations is unclear. To assess whether ACT use has selected for specific K13PD mutations, we compared the K13PD genetic diversity in clinical isolates collected before and after the implementation of ACT use from seven sites across Uganda. We detected K13PD NS-SNPs in 16 of 683 (2.3%) clinical isolates collected between 1999 and 2004 and in 26 of 716 (3.6%) isolates collected between 2012 and 2016 (P = 0.16), representing a total of 29 different polymorphisms at 27 codons. Individual NS-SNPs were usually detected only once, and none were found in more than 0.7% of the isolates. Three SNPs (C469F, P574L, and A675V) associated with delayed clearance in Southeast Asia were seen in samples collected between 2012 and 2016, each in a single isolate. No differences in diversity following implementation of ACT use were found at any of the seven sites, nor was there evidence of selective pressures acting on the locus. Our results suggest that selection by ACTs is not impacting on K13PD diversity in Uganda.

Computational Methods for Structure-to-Function Analysis of Diet-Derived Catechins-Mediated Targeting of In Vitro Vasculogenic Mimicry

Cancer Informatics ◽

10.1177/11769351211009229 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110092

Author(s):

Abicumaran Uthamacumaran ◽

Narjara Gonzalez Suarez ◽

Abdoulaye Baniré Diallo ◽

Borhane Annabi

Keyword(s):

Machine Learning ◽

Cancer Cells ◽

Structural Changes ◽

Function Analysis ◽

Vasculogenic Mimicry ◽

Emergent Behavior ◽

Molecular Signature ◽

Ovarian Cancer Cells

Background: Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science. Procedures: In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers. Results: These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes. Conclusions: Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

Schistosoma mansoni eggs induce Wnt/β-catenin signaling and activate the protooncogene c-Jun in human and hamster colon

Scientific Reports ◽

10.1038/s41598-020-79450-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jakob Weglage ◽

Friederike Wolters ◽

Laura Hehr ◽

Jakob Lichtenberger ◽

Celina Wulz ◽

...

Keyword(s):

Signaling Pathways ◽

Colorectal Carcinogenesis ◽

Sub Saharan Africa ◽

Interleukin 4 ◽

Health Burden ◽

Sub Saharan ◽

Considerable Morbidity ◽

First Time

AbstractSchistosomiasis (bilharzia) is a neglected tropical disease caused by parasitic flatworms of the genus Schistosoma, with considerable morbidity in parts of the Middle East, South America, Southeast Asia, in sub-Saharan Africa, and particularly also in Europe. The WHO describes an increasing global health burden with more than 290 million people threatened by the disease and a potential to spread into regions with temperate climates like Corsica, France. The aim of our study was to investigate the influence of S. mansoni infection on colorectal carcinogenic signaling pathways in vivo and in vitro. S. mansoni infection, soluble egg antigens (SEA) and the Interleukin-4-inducing principle from S. mansoni eggs induce Wnt/β-catenin signaling and the protooncogene c-Jun as well as downstream factor Cyclin D1 and markers for DNA-damage, such as Parp1 and γH2a.x in enterocytes. The presence of these characteristic hallmarks of colorectal carcinogenesis was confirmed in colon biopsies from S. mansoni-infected patients demonstrating the clinical relevance of our findings. For the first time it was shown that S. mansoni SEA may be involved in the induction of colorectal carcinoma-associated signaling pathways.