How to build a machine learning model to predict antimicrobial peptides (End-to-end Bioinformatics)

Antimicrobial resistance is an urgent and global health problem as existing drugs are becoming ineffective against the treatment of antimicrobial infections. In this video, i will be showing you how to build an end-to-end bioinformatics project where we will be building a machine learning model to predict antimicrobial peptides. Particularly, we will be retrieving 2 datasets consisting of antimicrobial peptides (positive set) and non-antimicrobial peptides (negative set). Then, we will be computing some peptide features to quantitatively describe peptides followed by model building and finally model interpretation where we shed light on the key important features important for predicting antimicrobial peptides.

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[DataProfessor]

Hi friends! I hope this video was helpful, please make sure to LIKE, SUBSCRIBE, COMMENT and SHARE!
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[emmanuelonah]

Discovering you is the best thing that have ever happened to me. Needless to say that my bio- and Cheminformatics skills have increased significantly. Thank you so much DataProfessor.

[jasdeep]

We biologists sincerely appreciate your efforts and love you Data Professor !!

[angsumandas]

Thank you for keeping our request. Biologists love data professor

[baotao]

Starting my self learning Bioinformatics journey and your channel is just awesome! Thank you for the guidance ❤

[Ali-pfor]

I watched it before but now I'm here to comment and share it!

[mariopaul]

It's awesome how well you show how to use data science in the bioinformatics field Chanin! Great video!

[suecheng]

Thank you. I am going to buildup some ML tools for drug discovery with bioinformatic data analysis from your great work!

[VyshnavieRSarma-rbur]

Thanks a lot Data Professor... This is very useful to us. Thank you

[qaziacademy]

So beautiful project it is. I learnt a lot. Thank you Data Professor.

[salma-amlas]

Can you explain what is meant by negative and positive? The features label here is a bit confusing

[abdulmalikaliyu]

Thank you Data Professor 🙏for this video on AMP activity prediction. I am working on AMP and have synthesised 2. My question is if we want to predict like activity value like MIC or IC50 of AMP, can you suggest how to get the data so as to try it out.

[username]

can we relate this info in this video to the molecular docking somehow? or have you ever perform molecular docking ?

[zadilkhwaja]

Any suggestions what can I modify in this project on my own if any

[asadmustafa]

Thank you for this great video! Can you please make a video to make input data to predict Antibiotic resistance by machine learning. How bioinformatics tools will be used to make the input data? Please

[Ajeet-Yadav-IIITD]

2:00 GPS raghava is my Prof. at IIITD, India

[luctiber]

Hi data professor, thanks for creating and sharing these amazing videos. I need your insight on a question bothering me. How to tackle the following kind of use case. Let’s say I am analysing the sells of a shop and the shop keeper question is which item should I present in the same area with others so I can increase my average revenue. To help the analysis I have all the items in the shop with their location over the last 10 years and on the other hand I have all the purchase my customers did over the last 10 years. How would you conduct the analysis ? Geolocation of the items associated with basket analysis? Thanks for your recommendation

[MontazerMontazer-is]

Hi Dataprofessor, Thanks again for your inspiring video. I have one additional question.
What technique should we use to scale feature values when combining several different types of individual features? Can we simply concatenate the matrices without performing any further processing

[clivedarwell]

Hello - do you know why I'm getting "'wget' is not recognized as an internal or external command" when running Jupyter through Anaconda?

[Ashis_Udgata]

I am getting error message

TypeError Traceback (most recent call last)
in <cell line: 21>()
19 return df
20
---> 21 feature = feature_calc(pos, neg, aac) # AAC
22 #feature = feature_calc(pos, neg, dpc) # DPC
23 feature

in feature_calc(po, ne, feature_name)
6 def feature_calc(po, ne, feature_name):
7 # Calculate feature
----> 8 po_feature = feature_name(po)
9 ne_feature = feature_name(ne)
10 # Create class labels

TypeError: 'str' object is not callable