ellisbrown/BDML25SP_hw1_processed_data · Datasets at Hugging Face

text stringlengths 0 5.58k
generation code) with the dataset to demonstrate credibility. Furthermore, each fungal strain will be
referenced via its MycoBank ID [1], ensuring accurate comparisons with other studies and enabling
dataset users to link other resources such as the fungal DNA information provided by MycoBank. Via
the MycoBank ID, dataset users can straightforwardly map each fungal strain to the enzymes within,
which themselves are uniquely identifiable via the Enzyme Commission Number [8]. For example,
Tramates Versicolor (Mycobank ID 281625 contains active enzymes Laccase (EC 1.10.3.2) which
catalyzes the oxidation of phenolic compounds, Manganese Peroxidase (MnP, EC 1.11.1.13) which
xidizes Mn(II) to Mn(III), which in turn oxidizes organic substrates, and Lignin Peroxidase (LiP, EC
1.11.1.14) which breaks down lignin by cleaving its non-phenolic structures. These fungal to enzyme
mappings can be deterministically generated as needed by the study. Similarly, enzyme featurization
for machine learning is an active research area [11,12]. We hope that state-of-the-art methods already
discovered in other areas of enzyme engineering can be applied to the mycoremediation problem
through the use of our dataset and that the dataset can contribute to new advances at the intersection
of enzyme featurization, enzyme behavior prediction, and machine learning.
Code and dataset will be available at https://github.com/danikagupta/Deep-Myco as the project
proceeds.
Figure 2: Study Methodology
3

References
[1] European Parliament. (2020) The Impact of Textile Production and Waste on the Environment - Infograph-
ics. Retrieved from https://www.europarl.europa.eu/topics/en/article/20201208STO93327/the-impact-of-textile-
production-and-waste-on-the-environment-infographics.
[2] Cairns, R. (2023) One-fifth of water pollution comes from textile dyes. But a shellfish-inspired solution
could clean it up. CNN. Retrieved from https://www.cnn.com/2023/06/15/world/textile-dyes-water-pollution-
shellfish-solution-scn.
[3] Water Commission. (2023) Turning the Tide: A Report on Water Sustainability. Retrieved from
https://watercommission.org/wp-content/uploads/2023/03/Turning-the-Tide-Report-Web.pdf.
[4] Tripathi, M., Singh, P., Singh, R., Bala, S., Pathak, N., Singh, S., Chauhan, R.S., Singh, P.K. (2023) Microbial
biosorbent for remediation of dyes and heavy metals pollution: A green strategy for sustainable environment.
Frontiers in Microbiology 14:1168954. DOI: 10.3389/fmicb.2023.1168954. PMCID: PMC10109241. PMID:
37077243
[5] Antón-Herrero, R., Chicca, I., García-Delgado, C., Crognale, S., Lelli, D., Gargarello, R.M., Herrero, J.,
Fischer, A., Thannberger, L., Eymar, E., Petruccioli, M., D’Annibale, A. (2024) Main Factors Determining the
Scale-Up Effectiveness of Mycoremediation for the Decontamination of Aliphatic Hydrocarbons in Soil. Journal
of Fungi.
[6]Harms, H., Schlosser, D., Wick, L.Y . (2011) Untapped potential: exploiting fungi in bioremediation of
hazardous chemicals. Nature Reviews Microbiology, 9:177–192. DOI: 10.1038/nrmicro2519.
[7] Health the Planet. (n.d.) Textile Dyeing and Its Environmental Impact. Retrieved from
https://healtheplanet.com/100-ways-to-heal-the-planet/textile-dyeing.
[8] Wikipedia. (n.d.) Enzyme Commission number. Wikipedia. Retrieved from
https://en.wikipedia.org/wiki/Enzyme Commission number
[9] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V ., Goyal, N., Küttler, H., Lewis, M., Yih, W.,
Rocktäschel, T., Riedel, S., Kiela, D. (2020) Retrieval-Augmented Generation for Knowledge-Intensive NLP
Tasks. arXiv preprint arXiv:2005.11401. DOI: 10.48550/arXiv.2005.11401
[10] Crous, P.W., Gams, W., Stalpers, J.A., Robert, V ., Stegehuis, G. (2004) MycoBank: an online initiative to
launch mycology into the 21st century. Studies in Mycology 50:19-22.
[11] Yang, J., Li, F.-Z., Arnold, F.H. (2024) Opportunities and Challenges for Machine Learning-Assisted
Enzyme Engineering. ACS Central Science 10(2):141-150. DOI: 10.1021/acscentsci.3c01275.
[12] Salas-Nuñez, L.F., Barrera-Ocampo, A., Caicedo, P.A., Cortes, N., Osorio, E.H., Villegas-Torres, M.F.,
González Barrios, A.F. (2024) Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Syn-
thesis Pathways: A Review. Metabolites 14(3):154. DOI: 10.3390/metabo14030154. PMCID: PMC10972002.
PMID: 38535315.
[13] Gugel, I., Summa, D., Costa, S., Manfredini, S., Vertuani, S., Marchetti, F., Tamburini, E. (2024)
Mycoremediation of Synthetic Azo Dyes by White-Rot Fungi Grown on Dairy Waste: A Step toward Sustainable
and Circular Bioeconomy. Fermentation, 10(2), 80. DOI: 10.3390/fermentation10020080
[14] Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res.
2015 Oct 19. doi: 10.1093/nar/gkv1075
[15] Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized ad-
verse drug event resource to accelerate drug safety research. Scientific Data. 2016 May 10;3:160026. doi:
10.1038/sdata.2016.26
[16] Halepoto, H., Gong, T., Memon, H. (2024) Current status and research trends of textile wastewater
treatments—A bibliometric-based study. Frontiers of Environmental Science.
[17] Rit Dye. (n.d.) Rit All-Purpose Dye. Retrieved from https://www.ritdye.com
[18] Faysse, M., Sibille, H., Wu, T., Omrani, B., Viaud, G., Hudelot, C., Colombo, P. (2024) Col-
Pali: Efficient Document Retrieval with Vision Language Models. arXiv preprint arXiv:2407.01449. DOI:
10.48550/arXiv.2407.01449.
[19] Facebook AI Research (FAIR). (2018) Detectron: Object Detection Platform. Retrieved from
https://github.com/facebookresearch/detectron
[20] Smith, R. (2007) An Overview of the Tesseract OCR Engine. Proceedings of the Ninth International
Conference on Document Analysis and Recognition (ICDAR 2007), IEEE, pp. 629-633. DOI: 10.1109/IC-
DAR.2007.4376991.
4

[21] Liu, Haotian, Linxi Fan, Chunyuan Li, Yong Jae Lee. "LLaV A: Large Language and Vision Assistant."
2023. Available: https://github.com/haotian-liu/LLaV A
[22] Maroš Kollár. (n.d.) PyPDF: A Python PDF Library. Retrieved from https://pypdf.readthedocs.io/.
[23] Anthropic. Claude, version 1.0, [AI language model]. Anthropic, 2023. Available:
https://www.anthropic.com/claude
[24] Touvron, Hugo, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix,
Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard
Grave, and Guillaume Lample. "LLaMA: Open and Efficient Foundation Language Models." 2023. Available:
https://arxiv.org/abs/2302.13971
[25] Hassabis, Demis, et al. "Gemini 1.5: Unlocking Multimodal Understanding Across Millions of Tokens of
Context." Google DeepMind, 2024. Available: https://arxiv.org/abs/2403.05530
[26] OpenAI. "GPT-4o." OpenAI Documentation, 2024. Available: https://www.openai.com/gpt-4o
5

generation code) with the dataset to demonstrate credibility. Furthermore, each fungal strain will be

referenced via its MycoBank ID [1], ensuring accurate comparisons with other studies and enabling

dataset users to link other resources such as the fungal DNA information provided by MycoBank. Via

the MycoBank ID, dataset users can straightforwardly map each fungal strain to the enzymes within,

which themselves are uniquely identifiable via the Enzyme Commission Number [8]. For example,

Tramates Versicolor (Mycobank ID 281625 contains active enzymes Laccase (EC 1.10.3.2) which

catalyzes the oxidation of phenolic compounds, Manganese Peroxidase (MnP, EC 1.11.1.13) which

xidizes Mn(II) to Mn(III), which in turn oxidizes organic substrates, and Lignin Peroxidase (LiP, EC

1.11.1.14) which breaks down lignin by cleaving its non-phenolic structures. These fungal to enzyme

mappings can be deterministically generated as needed by the study. Similarly, enzyme featurization

for machine learning is an active research area [11,12]. We hope that state-of-the-art methods already

discovered in other areas of enzyme engineering can be applied to the mycoremediation problem

through the use of our dataset and that the dataset can contribute to new advances at the intersection

of enzyme featurization, enzyme behavior prediction, and machine learning.

Code and dataset will be available at https://github.com/danikagupta/Deep-Myco as the project

proceeds.

Figure 2: Study Methodology

3

References

[1] European Parliament. (2020) The Impact of Textile Production and Waste on the Environment - Infograph-

ics. Retrieved from https://www.europarl.europa.eu/topics/en/article/20201208STO93327/the-impact-of-textile-

production-and-waste-on-the-environment-infographics.

[2] Cairns, R. (2023) One-fifth of water pollution comes from textile dyes. But a shellfish-inspired solution

could clean it up. CNN. Retrieved from https://www.cnn.com/2023/06/15/world/textile-dyes-water-pollution-

shellfish-solution-scn.

[3] Water Commission. (2023) Turning the Tide: A Report on Water Sustainability. Retrieved from

https://watercommission.org/wp-content/uploads/2023/03/Turning-the-Tide-Report-Web.pdf.

[4] Tripathi, M., Singh, P., Singh, R., Bala, S., Pathak, N., Singh, S., Chauhan, R.S., Singh, P.K. (2023) Microbial

biosorbent for remediation of dyes and heavy metals pollution: A green strategy for sustainable environment.

Frontiers in Microbiology 14:1168954. DOI: 10.3389/fmicb.2023.1168954. PMCID: PMC10109241. PMID:

37077243

[5] Antón-Herrero, R., Chicca, I., García-Delgado, C., Crognale, S., Lelli, D., Gargarello, R.M., Herrero, J.,

Fischer, A., Thannberger, L., Eymar, E., Petruccioli, M., D’Annibale, A. (2024) Main Factors Determining the

Scale-Up Effectiveness of Mycoremediation for the Decontamination of Aliphatic Hydrocarbons in Soil. Journal

of Fungi.

[6]Harms, H., Schlosser, D., Wick, L.Y . (2011) Untapped potential: exploiting fungi in bioremediation of

hazardous chemicals. Nature Reviews Microbiology, 9:177–192. DOI: 10.1038/nrmicro2519.

[7] Health the Planet. (n.d.) Textile Dyeing and Its Environmental Impact. Retrieved from

https://healtheplanet.com/100-ways-to-heal-the-planet/textile-dyeing.

[8] Wikipedia. (n.d.) Enzyme Commission number. Wikipedia. Retrieved from

https://en.wikipedia.org/wiki/Enzyme Commission number

[9] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V ., Goyal, N., Küttler, H., Lewis, M., Yih, W.,

Rocktäschel, T., Riedel, S., Kiela, D. (2020) Retrieval-Augmented Generation for Knowledge-Intensive NLP

Tasks. arXiv preprint arXiv:2005.11401. DOI: 10.48550/arXiv.2005.11401

[10] Crous, P.W., Gams, W., Stalpers, J.A., Robert, V ., Stegehuis, G. (2004) MycoBank: an online initiative to

launch mycology into the 21st century. *Studies in Mycology* **50**:19-22.

[11] Yang, J., Li, F.-Z., Arnold, F.H. (2024) Opportunities and Challenges for Machine Learning-Assisted

Enzyme Engineering. ACS Central Science 10(2):141-150. DOI: 10.1021/acscentsci.3c01275.

[12] Salas-Nuñez, L.F., Barrera-Ocampo, A., Caicedo, P.A., Cortes, N., Osorio, E.H., Villegas-Torres, M.F.,

González Barrios, A.F. (2024) Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Syn-

thesis Pathways: A Review. Metabolites 14(3):154. DOI: 10.3390/metabo14030154. PMCID: PMC10972002.

PMID: 38535315.

[13] Gugel, I., Summa, D., Costa, S., Manfredini, S., Vertuani, S., Marchetti, F., Tamburini, E. (2024)

Mycoremediation of Synthetic Azo Dyes by White-Rot Fungi Grown on Dairy Waste: A Step toward Sustainable

and Circular Bioeconomy. Fermentation, 10(2), 80. DOI: 10.3390/fermentation10020080

[14] Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res.

2015 Oct 19. doi: 10.1093/nar/gkv1075

[15] Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized ad-

verse drug event resource to accelerate drug safety research. Scientific Data. 2016 May 10;3:160026. doi:

10.1038/sdata.2016.26

[16] Halepoto, H., Gong, T., Memon, H. (2024) Current status and research trends of textile wastewater

treatments—A bibliometric-based study. Frontiers of Environmental Science.

[17] Rit Dye. (n.d.) Rit All-Purpose Dye. Retrieved from https://www.ritdye.com

[18] Faysse, M., Sibille, H., Wu, T., Omrani, B., Viaud, G., Hudelot, C., Colombo, P. (2024) Col-

Pali: Efficient Document Retrieval with Vision Language Models. arXiv preprint arXiv:2407.01449. DOI:

10.48550/arXiv.2407.01449.

[19] Facebook AI Research (FAIR). (2018) Detectron: Object Detection Platform. Retrieved from

https://github.com/facebookresearch/detectron

[20] Smith, R. (2007) An Overview of the Tesseract OCR Engine. Proceedings of the Ninth International

Conference on Document Analysis and Recognition (ICDAR 2007), IEEE, pp. 629-633. DOI: 10.1109/IC-

DAR.2007.4376991.

4

[21] Liu, Haotian, Linxi Fan, Chunyuan Li, Yong Jae Lee. "LLaV A: Large Language and Vision Assistant."

2023. Available: https://github.com/haotian-liu/LLaV A

[22] Maroš Kollár. (n.d.) PyPDF: A Python PDF Library. Retrieved from https://pypdf.readthedocs.io/.

[23] Anthropic. Claude, version 1.0, [AI language model]. Anthropic, 2023. Available:

https://www.anthropic.com/claude

[24] Touvron, Hugo, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix,

Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard

Grave, and Guillaume Lample. "LLaMA: Open and Efficient Foundation Language Models." 2023. Available:

https://arxiv.org/abs/2302.13971

[25] Hassabis, Demis, et al. "Gemini 1.5: Unlocking Multimodal Understanding Across Millions of Tokens of

Context." Google DeepMind, 2024. Available: https://arxiv.org/abs/2403.05530

[26] OpenAI. "GPT-4o." OpenAI Documentation, 2024. Available: https://www.openai.com/gpt-4o

5