Penentuan Matriks Similaritas Protein Menggunakan Transfer Learning Determination of Protein Similarity Matrix using Transfer Learning
Main Article Content
Abstract
Penjajaran sekuen protein merupakan hal yang penting untuk menentukan kesamaan antara protein
satu dengan protein lainnya. Namun algoritma penjajaran sekuen yang sudah ada mempunyai tingkat
kompleksitas tinggi sehingga eksekusi program pencarian kesamaan protein memerlukan waktu yang
lama apabila jumlah sekuen yang disejajarkan sangat banyak. Penelitian ini bertujuan mendapatkan
matriks similaritas protein dengan metode eksperimen berbasis transfer learning dengan arsitektur
transformer. Data yang digunakan sebagai masukan untuk transformer adalah data sekuen protein
berformat teks. Hasil penelitian berupa matriks similaritas protein yang dapat digunakan oleh para
peneliti lain di bidangnya masing-masing sebagai bahan untuk dianalisis lebih lanjut.
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright for articles in this journal are retained by the author(s), with first publication rights granted to the journal. By virtue of their appearance in this open access journal, articles are free to use with proper attribution in educational and other non-commercial settings.References
Y. S. Lee, Y. S. Kim, and R. L. Uy,
“Serial and parallel implementation of
Needleman-Wunsch algorithm,”
International Journal of Advances in
Intelligent Informatics, vol. 6, no. 1, pp.
–108, Mar. 2020, doi:
26555/ijain.v6i1.361.
Da Li and M. Becchi, “Abstract: Multiple
Pairwise Sequence Alignments with the
Needleman-Wunsch Algorithm on GPU,”
in 2012 SC Companion: High
Performance Computing, Networking
Storage and Analysis, IEEE, Nov. 2012,
pp. 1471–1472. doi:
1109/SC.Companion.2012.267.
C. Kyal, R. Kumar, and A. Zamal,
“Performance-Based Analogising of
Needleman Wunsch Algorithm to Align
DNA Sequences Using GPU and FPGA,”
in 2020 IEEE 17th India Council
International Conference, INDICON
, IEEE, Dec. 2020, pp. 1–5. doi:
1109/INDICON49873.2020.9342078.
A. Chaudhary, D. Kagathara, and V.
Patel, “A GPU based implementation of
Needleman-Wunsch algorithm using
skewing transformation,” in 2015 8th
International Conference on
Contemporary Computing, IC3 2015,
IEEE, Aug. 2015, pp. 498–502. doi:
1109/IC3.2015.7346733.
H. Nadim, M. Assal, and A. A. Hegazy,
“An efficient framework for accelerating
Needleman-Wunsch algorithm using
GPU,” Int J Bioinform Res Appl, vol. 17,
no. 2, pp. 101–110, 2021, doi:
1504/IJBRA.2021.114412.
M. Fakirah, M. A. Shehab, Y. Jararweh,
and M. Al-Ayyoub, “Accelerating
Needleman-Wunsch global alignment
algorithm with GPUs,” in 2015
IEEE/ACS 12th International Conference
of Computer Systems and Applications
(AICCSA), IEEE, Nov. 2015, pp. 1–5. doi:
1109/AICCSA.2015.7507113.
M. Fakirah, M. A. Shehab, Y. Jararweh,
and M. Al-Ayyoub, “Accelerating
Needleman-Wunsch global alignment
algorithm with GPUs,” Proceedings of
IEEE/ACS International Conference on
Computer Systems and Applications,
AICCSA, vol. 2016-July. 2016. doi:
1109/AICCSA.2015.7507113.
J. Pérez Serrano, E. F. De Oliveira
Sandes, A. C. Magalhaes Alves de Melo,
and M. Ujaldón, “Smith-Waterman
acceleration in multi-GPUs: A
performance per watt analysis,” Lecture
Notes in Computer Science (including
subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in
Bioinformatics), vol. 10209 LNCS. pp.
–523, 2017. doi: 10.1007/978-3-319-
-7_46.
A. Bustamam, G. Ardaneswari, H.
Tasman, and D. Lestari, “Performance
evaluation of fast smith-waterman
algorithm for sequence database searches
using CUDA GPU-based parallel
computing,” Journal of Next Generation
Information Technology, vol. 5, no. 2, pp.
–46, 2014, [Online]. Available:
https://api.elsevier.com/content/abstract/s
copus_id/84930012659
X. Feng, H. Jin, R. Zheng, Z. Shao, and L.
Zhu, “Implementing Smith-Waterman
algorithm with two-dimensional cache on
GPUs,” Proceedings - 2nd International
Conference on Cloud and Green
Computing and 2nd International
Conference on Social Computing and Its
Applications, CGC/SCA 2012. pp. 25–30,
doi: 10.1109/CGC.2012.98
K. Dohi, K. Benkrid, C. Ling, T. Hamada,
and Y. Shibata, “Highly efficient
mapping of the Smith-Waterman
algorithm on CUDA-compatible GPUs,”
Proceedings of the International Conference on Application-Specific
Systems, Architectures and Processors.
pp. 29–36, 2010. doi:
1109/ASAP.2010.5540796.
C. Ling, K. Benkrid, and T. Hamada, “A
parameterisable and scalable SmithWaterman algorithm implementation on
CUDA-compatible GPUs,” in 2009 IEEE
th Symposium on Application Specific
Processors, IEEE, Jul. 2009, pp. 94–100.
doi: 10.1109/SASP.2009.5226343.
M. J. Yin, X. Xu, Z. Xiong, T. Zhang, and
F. Zheng, “An improved smith-waterman
algorithm on heterogeneous CPU-GPU
Systems,” International Journal of
Applied Mathematics and Statistics, vol.
, no. 20, pp. 499–507, 2013, [Online].
Available:
https://api.elsevier.com/content/abstract/s
copus_id/84896782034
E. F. D. O. Sandes and A. C. M. A. De
Melo, “Smith-Waterman alignment of
huge sequences with GPU in linear
space,” Proceedings - 25th IEEE
International Parallel and Distributed
Processing Symposium, IPDPS 2011. pp.
–1211, 2011. doi:
1109/IPDPS.2011.114.
E. F. Edans and A. C. M. A. De Melo,
“Retrieving smith-waterman alignments
with optimizations for megabase
biological sequences using GPU,” IEEE
Transactions on Parallel and Distributed
Systems, vol. 24, no. 5, pp. 1009–1021,
, doi: 10.1109/TPDS.2012.194.
M. Yin, X. Xu, Z. Xiong, F. Zheng, and
T. Zhang, “Optimizing Smith-Waterman
algorithm based on CPU and GPU
through CUDA platform,” International
Review on Computers and Software, vol.
, no. 7, pp. 3627–3632, 2012, [Online].
Available:
https://api.elsevier.com/content/abstract/s
copus_id/84875160251
D. V. V. Prasad and S. Jaganathan,
“Improving the performance of Smith–
Waterman sequence algorithm on GPU
using shared memory for biological
protein sequences,” Cluster Comput, vol.
, no. S4, pp. 9495–9504, Jul. 2019, doi:
1007/s10586-018-2421-7.
Ł. Ligowski, W. R. Rudnicki, Y. Liu, and
B. Schmidt, “Accurate scanning of
sequence databases with the smithwaterman algorithm,” GPU Computing
Gems Emerald Edition. pp. 155–171,
doi: 10.1016/B978-0-12-384988-
00011-5.
A. Ali and B. Ram, “GPU performance
survey on OpenCL and CUDA using
smith waterman algorithm,” International
Journal of Applied Engineering
Research, vol. 10, no. 55, pp. 1320–1323,
, [Online]. Available:
https://api.elsevier.com/content/abstract/s
copus_id/84942465871
B. Mumbai, Mastering spaCy, 2021.
J. Devlin, “BERT: Pre-training of deep
bidirectional transformers for language
understanding,” NAACL HLT 2019 - 2019
Conference of the North American
Chapter of the Association for
Computational Linguistics: Human
Language Technologies - Proceedings of
the Conference, vol. 1. pp. 4171–4186,
[Online]. Available:
https://api.elsevier.com/content/abstract/s
copus_id/85083815650
A. Vaswani, “Attention Is All You Need,”
no. Nips, 2017