Prediction of essential genes in G20 using machine learning model

Nembot, Thierry Kongne; Fotseu, Ernest Basile Fotseu; Sani, Rajesh K.; Gnimpieba, Z. Etienne; Lushbough, Carol; Bomgni, Alain Bertrand

doi:10.1109/bibm52615.2021.9669756

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Following our gene extraction from free text, we used our essential gene prediction model to check the status of each gene in our collection ( Nembot et al, 2021 ). Figure 2 describes the predictive model and emphasizes the feature analysis step.…”

Section: Methodsmentioning

confidence: 99%

“…The key basis of any computational study involves the development of an algorithm-based program using text mining and predictive modeling approaches ( Rosário-Ferreira et al, 2021 ). The text mining algorithm is used to extract data from relevant literature in different databases such as database of essential genes, online gene essentiality database, essential genes of genome scale, and cluster essential genes ( Nembot et al, 2021 ). However, one of the major limitations of text mining via machine learning algorithms includes their inability to identify the essential genes on the condition basis due to the unavailability of classified data to train the text mining algorithm ( Aromolaran et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

et al. 2023

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The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein–protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under “persistent,” inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under “shell.” Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

et al. 2023

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“…This framework produced artifacts from each mini-capstone project. The various projects had artifacts produced during the 18 months, with IEEE presentations in both 2021 and 2022 conference proceedings [5][6][7][8][9][10][11][12][13]. Performance in the production of these artifacts (presentations, machine learning models) allowed the participants to integrate their new knowledge (Merrill's 5th principle) [2] into a presentation at the conference.…”

Section: Artifacts and Conference(s)mentioning

confidence: 99%

“…The outcome exceeded expectations: some projects earned slots for presentation at the international IEEE Bioinformatics conference in 2022 [5][6][7][8], yielding three Machine Learning models published in a journal [9][10][11][12][13][14][15][16][17][18]. Feedback from participants a rmed the success of this experience, underlining the potential for integrating convergence research into the curriculum through problem-based learning.…”

Section: Pre-post Ratings Work Ow Understandingmentioning

confidence: 99%

Convergence research and training in computational bioengineering: a case study on AI/ML driven biofilm-material interaction discovery

Zylla,

Z.,

Bomgni

et al. 2023

Preprint

Self Cite

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Initially, research disciplines operated independently, but the emergence of trans- disciplinary sciences led to convergence research, impacting graduate programs and research laboratories, especially in bioengineering and material engineering as presented here. Current graduate curriculum fails to efficiently prepare students for multidisciplinary and convergence research, thus creating a gap between the students and research laboratory expectations. We present a convergence training framework for graduate students, incorporating problem-based learning under the guidance of senior scientists and collaboration with postdoctoral researchers. This case study serves as a template for transdisciplinary convergent training projects - bridging the expertise gap and fostering successful convergence learning experiences in computational biointerface (material-biology interface). The 18-month Advanced Data Science Workshop, initiated in 2019, involves project-based learning, online training modules, and data collection. A pilot solution utilized Jupyter notebook on Google collaborator and culminated in a face-to-face workshop where project presentations and finalization occurred. The program started with 9 experts in the four diverse fields creating 14 curated projects in data science (Artificial Intelligence/Machine Learning), material science, biofilm engineering, and biointerface. These were integrated into convergence research through webinars by the experts. The experts chose 8 of the 14 projects to be part of an all-day in-person workshop, where over 20 learners formed eight teams that tackled complex problems at the interface of digital image processing, gene expression analysis, and material prediction. Each team was comprised of students and postdoctoral researchers or research scientists from diverse domains including computer science, materials science, and biofilm research. Some projects were selected for presentation at the international IEEE Bioinformatics conference in 2022, with three resulting Machine Learning (ML) models submitted as a journal paper. Students engaged in problem discussions, collaborated with experts from different disciplines, and received guidance in decomposing learning objectives. Based on learner feedback, this successful experience allows for consolidation and integration of convergence research via problem-based learning into the curriculum. Three bioengineering participants, who received training in data science and engineering, have received bioinformatics jobs in biotechnology industries.

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Convergence Research and Training in Computational Bioengineering: A Case Study on AI/ML-Driven Biofilm–Material Interaction Discovery

Zylla,

Bomgni,

Sani

et al. 2024

Biomed Eng Education

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Historically, research disciplines have successfully operated independently. However, the emergence of transdisciplinary research has led to convergence methodologies, resulting in groundbreaking discoveries. Despite the benefits, graduate programs face challenges in implementing transdisciplinary research and preparing students for real-world collaboration across diverse disciplines and experience levels. We propose a convergence training framework integrating project-based learning, training modules, and collaborative teaming to address this. This approach, tested in a multi-institutional workshop, proved effective in bridging expertise gaps and fostering successful convergence learning experiences in computational biointerface (material–biology interface) research. Here, biointerface research focuses on control of biomolecular interactions with technologically relevant material surfaces, which is a critical component of biotechnology and engineering applications. Positive outcomes, including conference presentations and published models, endorse the framework's application in graduate curricula, particularly for students engaging in transdisciplinary collaboration.

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Prediction of essential genes in G20 using machine learning model

Cited by 5 publications

References 15 publications

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

Convergence research and training in computational bioengineering: a case study on AI/ML driven biofilm-material interaction discovery

Convergence Research and Training in Computational Bioengineering: A Case Study on AI/ML-Driven Biofilm–Material Interaction Discovery

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