Answers.Body: <p>Just a recommendation: Be careful when making your data sets.</p>
  <p>P31327_57, P31327_119, P31327_157 are all annotated as &quot;By similarity&quot; to the mouse entry in Swiss-Prot <a href="https://www.uniprot.org/uniprot/P31327#ptm_processing" rel="nofollow noreferrer">https://www.uniprot.org/uniprot/P31327#ptm_processing</a>, which means there is no direct experimental evidence in the human entry. If you do have publications that report experimental evidence for these PTMs, please don't hesitate to share them with the UniProt team.</p>
  <p>I also recommend reading this UniProt help page about using UniProt data in negative datasets if you haven't already done so: <a href="https://www.uniprot.org/help/negative_datasets" rel="nofollow noreferrer">https://www.uniprot.org/help/negative_datasets</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-03-16T09:32:33.963Z
Answers.LastActivityDate: 2021-03-16T09:32:33.963Z
Answers.UserDisplayName: Elisabeth.Gasteiger
Body: <p>I am new to the bioinformatics field. I have positive and negative protein sequences for acetylation PTM. Now, I want to train a classifier, say SVM. What will be the next step? How can I convert these sequences into usable features? Any information or links would help.</p>
  <pre><code>&gt;P31327_55|1|testing
  KAQTAHIVLEDGTKMKGYSFGHPSSVA
  &gt;P31327_57|1|testing
  QTAHIVLEDGTKMKGYSFGHPSSVAGE
  &gt;P31327_119|1|testing
  APDTTALDELGLSKYLESNGIKVSGLL
  &gt;P31327_157|1|testing
  LATKSLGQWLQEEKVPAIYGVDTRMLT
  </code></pre>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-03-05T16:06:39.617Z
Comments.Text: It may be worth checking what other folk do for motif classification, for example see http://elm.eu.org/elms/candidates.html#MOD_acetylation which lists the different motifs (different enzymes recognise different motifs). Although ELM uses metadata such as protein localisation to filter the motifs for much better results.
Comments.UserDisplayName: Matteo Ferla
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-03-05T16:08:38.437Z
Comments.Text: Q to others. a ML `features` (measurable property) is a different thing than the sequence `feature` present in a GenBank file (i.e. an annotation on the sequence, e.g. acetylation sites). This the second time I have seen this tag usage, is this something worth discussing in Meta?
Comments.UserDisplayName: Matteo Ferla
Tags: <fasta><proteins><machine-learning><features>
Title: Extract features from fasta sequences and train the classifier
ThreadText: # Extract features from fasta sequences and train the classifier
  
  (Asked by: username_0 on 2021-03-05)
  
  I am new to the bioinformatics field. I have positive and negative protein sequences for acetylation PTM. Now, I want to train a classifier, say SVM. What will be the next step? How can I convert these sequences into usable features? Any information or links would help.
  
  ```
  >P31327_55|1|testing
  KAQTAHIVLEDGTKMKGYSFGHPSSVA
  >P31327_57|1|testing
  QTAHIVLEDGTKMKGYSFGHPSSVAGE
  >P31327_119|1|testing
  APDTTALDELGLSKYLESNGIKVSGLL
  >P31327_157|1|testing
  LATKSLGQWLQEEKVPAIYGVDTRMLT
  
  ```
  
  **Tags:** fasta, proteins, machine-learning, features
  
  > **Comments:**
  > **username_1**: It may be worth checking what other folk do for motif classification, for example see http://elm.eu.org/elms/candidates.html#MOD_acetylation which lists the different motifs (different enzymes recognise different motifs). Although ELM uses metadata such as protein localisation to filter the motifs for much better results.  
  > **username_1**: Q to others. a ML `features` (measurable property) is a different thing than the sequence `feature` present in a GenBank file (i.e. an annotation on the sequence, e.g. acetylation sites). This the second time I have seen this tag usage, is this something worth discussing in Meta?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2021-03-16. Score: 1
  
  Just a recommendation: Be careful when making your data sets.
  
  P31327\_57, P31327\_119, P31327\_157 are all annotated as "By similarity" to the mouse entry in Swiss-Prot <https://www.uniprot.org/uniprot/P31327#ptm_processing>, which means there is no direct experimental evidence in the human entry. If you do have publications that report experimental evidence for these PTMs, please don't hesitate to share them with the UniProt team.
  
  I also recommend reading this UniProt help page about using UniProt data in negative datasets if you haven't already done so: <https://www.uniprot.org/help/negative_datasets>
  
  
  ---
  
  