Acta Scientific Medical Sciences (ASMS)(ISSN: 2582-0931)

Short Communication Volume 4 Issue 11

BactClass: Simplifying the Use of Machine Learning in Biology and Medicine

Tian Tong Liu1 and Maurice HT Ling2*

1Department of Information Systems and Operation Management, Warrington College of Business, University of Florida, USA
2HOHY PTE LTD, Singapore

*Corresponding Author:Maurice HT Ling, HOHY PTE LTD, Singapore.

Received: September 02, 2020; Published: October 17, 2020

×

Abstract

  Machine learning has many applications in biology and medicine. However, most existing tools require substantial programming skills, which can be a challenge to many biologists. Here, we present BactClass as a command-line tool for machine learning algorithms on formatted data, aiming to reduce the challenges faced by biologists who are interested to use machine learning approaches. BactClass is part of the Bactome project (https://github.com/mauriceling/bactome) and is licensed under GNU General Public Licence version 3 for academic and non-commercial purposes only.

Keywords: BactClass; Biology; Medicine

×

References

  1. Libbrecht MW and Noble WS. “Machine Learning Applications in Genetics and Genomics”. Nature Reviews Genetics 6 (2015): 321-332.
  2. Mahood EH., et al. “Machine Learning: A Powerful Tool for Gene Function Prediction in Plants”. Applied Plant Science7 (2020): e11376.
  3. Ching T., et al. “Opportunities and Obstacles for Deep Learning in Biology and Medicine”. Journal of the Royal Society Interface141 (2018): 20170387.
  4. Colmenarejo G. “Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review”. Nutrients 12.8 (2020): 2466.
  5. Xie T., et al. “Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer”. Frontiers in Oncology 9 (2019): 505.
  6. Lynch CM., et al. “Prediction of Lung Cancer Patient Survival via Supervised Machine Learning Classification Techniques”. International Journal of Medical Informatics 108 (2017): 1-8.
  7. Kegerreis B., et al. “Machine Learning Approaches to Predict Lupus Disease Activity from Gene Expression Data”. Scientific Report 9 (2019): 9617.
  8. Patrício M., et al. “Using Resistin, Glucose, Age and BMI to Predict the Presence of Breast Cancer”. BMC Cancer1 (2018): 29.
  9. Morais DK., et al. “BTW - Bioinformatics Through Windows: An Easy-to-Install Package to Analyze Marker Gene Data”. Peer Journal 6 (2018): e5299.
  10. Cock PJA., et al. “Biopython: Freely Available Python Tools for Computational Molecular Biology and Bioinformatics”. Bioinformatics 11 (2009): 1422-1423.
  11. Kunzmann P., et al. “Biotite: A Unifying Open Source Computational Biology Framework in Python”. BMC Bioinformatics 1 (2018): 346.
  12. Lopez CF., et al. “Programming Biological Models in Python using PySB”. Molecular Systems Biology 1 (2013): 646.
  13. Carey Ma and Papin JA. “Ten Simple Rules for Biologists Learning to Program”. PLOS Computational Biology 1 (2018): e1005871.
  14. Auker LA and Barthelmess EL. “Teaching R in the Undergraduate Ecology Classroom: Approaches, Lessons Learned, and Recommendations”. Ecosphere4 (2020).
  15. Camacho C., et al. “BLAST+: Architecture and Applications”. BMC Bioinformatics 10 (2009): 421.
  16. Dirmeier S., et al. “PyBDA: A Command Line Tool for Automated Analysis of Big Biological Data Sets”. BMC Bioinformatics1 (2019): 564.
  17. Seim I., et al. “RadAA: A Command-line Tool for Identification of Radical Amino Acid Changes in Multiple Sequence Alignments”. Molecular Informatics 2019 38 (1-2): e1800057.
  18. Jones P., et al. “InterProScan 5: Genome-Scale Protein Function Classification”. Bioinformatics 9 (2014): 1236-1240.
  19. Afgan E., et al. “The Galaxy Platform for Accessible, Reproducible and Collaborative Biomedical Analyses: 2018 Update”. Nucleic Acids Research 46 (2018): W537-544.
  20. Joppich M and Zimmer R. “From Command-Line Bioinformatics to BioGUI”. Peer Journal 7 (2019): e8111.
  21. Mitchell AL., et al. “InterPro in 2019: Improving Coverage, Classification and Access to Protein Sequence Annotations”. Nucleic Acids ResearchD1 (2019): D351-360.
  22. Afgan E., et al. “Galaxy: A Gateway to Tools in e-Science”. In: Yang X, Wang L, Jie W, editors. Guide to e-Science. London: Springer London (2011): 145-77.
  23. Seemann T. “Ten Recommendations for Creating Usable Bioinformatics Command Line Software”. GigaScience1 (2013): 15.
  24. Pedregosa F., et al. “Scikit-learn: Machine learning in Python”. Journal of Machine Learning Research 12 (2011): 2825-30.
  25. Paszke A., et al. “PyTorch: An Imperative Style, High-Performance Deep Learning Library”. In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada (2019): 8026-8037.
  26. Chen T., et al. “MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems”. In: Neural Information Processing Systems, Workshop on Machine Learning Systems (2015).
  27. Meng X., et al. “Mllib: Machine Learning in Apache Spark”. Journal of Machine Learning Research1 (2016): 1235-1241.
  28. Buitinck L., et al. “API Design for Machine Learning Software: Experiences from the Scikit-learn Project”. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning (2013) 108-122.
  29. Ling MH. “Island: A Simple Forward Simulation Tool for Population Genetics”. Acta Scientific Computer Sciences2 (2019): 20-22.
  30. Ling MH. “RANDOMSEQ: Python command‒line random sequence generator”. MOJ Proteomics and Bioinformatics4 (2018): 206-208.
  31. Ling MH. “SEcured REcorder BOx (SEREBO) based on blockchain technology for immutable data management and notarization”. MOJ Proteomics and Bioinformatics6 (2018): 169-174.
  32. Ling MH. “Draft Implementation of a Method to Secure Data by File Fragmentation”. Acta Scientific Computer Sciences 2 (2019): 10-13.
  33. Ling MHT. “SeqProperties: A Python Command-Line Tool for Basic Sequence Analysis”. Acta Scientific Microbiology 6 (2020): 103-106.
  34. Cortes C and Vapnik V. “Support-Vector Networks”. Machine Learning3 (1995): 273-297.
  35. Popescu M-C., et al. “Multilayer Perceptron and Neural Networks”. WSEAS Transactions on Circuits and Systems7 (2009): 579-88.
  36. Gayathri B and Sumathi C. “An Automated Technique Using Gaussian Naïve Bayes Classifier to Classify Breast Cancer”. International Journal of Computer Applications6 (2016): 16-21.
  37. McCallum A and Nigam K. “A Comparison of Event Models for Naive Bayes Text Classification”. In: AAAI-98 Workshop on Learning for Text Categorization (1998): 41-48.
  38. Rennie JD., et al. “Tackling the Poor Assumptions of Naive Bayes Text Classifiers”. In: 20th international conference on machine learning (ICML-03) (2003): 616-23.
  39. Breiman L., et al. “Classification and Regression Trees”. CRC Press (1984).
  40. Wolberg WH., et al. “Machine Learning Techniques to Diagnose Breast Cancer from Image-Processed Nuclear Features of Fine Needle Aspirates”. Cancer Letter2-3 (1994): 163-171.
  41. Koul A., et al. “Cross-Validation Approaches for Replicability in Psychology”. Frontiers in Psychology 9 (2018): 1117.
  42. Powers DM. “Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation”. Journal of Machine Learning Technologies 1 (2011): 37-63.
  43. Rosenberg A., et al. “A Conditional Entropy-Based External Cluster Evaluation Measure”. In: 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (2007): 410-420.

 

×

Citation

Citation: Tian Tong Liu and Maurice HT Ling. “BactClass: Simplifying the Use of Machine Learning in Biology and Medicine". Acta Scientific Medical Sciences 4.11 (2020): 43-47.




Metrics

Acceptance rate30%
Acceptance to publication20-30 days
Impact Factor1.403

Indexed In





News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is May 30, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.

Contact US