Emerging Bioinformatics Methods and Resources in Drug Toxicology

Audouze, Karine; Taboureau, Olivier

doi:10.1007/978-1-0716-1960-5_6

Cited by 4 publications

(3 citation statements)

References 62 publications

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“…Several literature articles exist today that have demonstrated active learning strategies used successfully to teach bioinformatics and computational biology courses [ 19 , 28 , 29 ]. Cooperative learning of bioinformatics, as proposed by Wright et al [ 30 ], involves students working in groups or in pairs.…”

Section: Bioinformatics and Computational Biologymentioning

confidence: 99%

Integrating computational skills in undergraduate Microbiology curricula in developing countries

Adenaike,

Olabanjo,

Adedeji

2023

Biology Methods and Protocols

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The employability of young graduates has gained increasing significance in the labour market of the 21st century. Universities turn out millions of graduates annually, but at the same time, employers highlight their lack of the requisite skills for sustainable employment. We live today; in a world of data, and therefore courses that feature numerical and computational tools to gather and analyse data are to be sourced for and integrated into life sciences’ curricula as they provide a number of benefits for both the students and faculty members that are engaged in teaching the courses. The lack of this teaching in undergraduate Microbiology curricula is devastating and leaves a knowledge-gap in the graduates that are turned out. This results in an inability of the emerging graduates to compete favourably with their counterparts from other parts of the world. There is a necessity on the part of life science educators to adapt their teaching strategies to best support students’ curricula that prepare them for careers in Science. Bioinformatics, Statistics and Programming are key computational skills to embrace by life Scientists and the need for training beginning at undergraduate level cannot be overemphasized. This paper reviews the need to integrate computational skills in undergraduate Microbiology curricula in Developing Countries with emphasis on Nigeria.

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Section: Bioinformatics and Computational Biologymentioning

confidence: 99%

Integrating computational skills in undergraduate Microbiology curricula in developing countries

Adenaike,

Olabanjo,

Adedeji

2023

Biology Methods and Protocols

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“…Recent computational methods have displayed good capacity in the prediction of toxicity outcomes, have provided content relating to chemical exposure, and have become a powerful tool that allows for the creation and administration of large genes or proteins [81]. Most gene-set analysis websites do not offer users the chance to modify their gene-set database.…”

Section: Computational Biology/toxicologymentioning

confidence: 99%

State of the Art of Genomic Technology in Toxicology: A Review

Recio-Vega

Facio-Campos

Hernández-González

et al. 2023

IJMS

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The rapid growth of genomics techniques has revolutionized and impacted, greatly and positively, the knowledge of toxicology, ushering it into a “new era”: the era of genomic technology (GT). This great advance permits us to analyze the whole genome, to know the gene response to toxicants and environmental stressors, and to determine the specific profiles of gene expression, among many other approaches. The aim of this work was to compile and narrate the recent research on GT during the last 2 years (2020–2022). A literature search was managed using the PubMed and Medscape interfaces on the Medline database. Relevant articles published in peer-reviewed journals were retrieved and their main results and conclusions are mentioned briefly. It is quite important to form a multidisciplinary taskforce on GT with the aim of designing and implementing a comprehensive, collaborative, and a strategic work plan, prioritizing and assessing the most relevant diseases, so as to decrease human morbimortality due to exposure to environmental chemicals and stressors.

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“…DINeurot also resulted in the withdrawal of several marketed drugs in the past decades. 5 Assessing drug safety at the early stages of drug discovery is a critical issue 6 and can substantially reduce the likelihood of drug development failure.…”

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

Zhao

Sun

Zhang

et al. 2022

J. Chem. Inf. Model.

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Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The bestperforming model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.

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Emerging Bioinformatics Methods and Resources in Drug Toxicology

Cited by 4 publications

References 62 publications

Integrating computational skills in undergraduate Microbiology curricula in developing countries

Integrating computational skills in undergraduate Microbiology curricula in developing countries

State of the Art of Genomic Technology in Toxicology: A Review

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

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