BioSeq-BLM:a platform for analyzing DNA, RNA, and protein sequences based on biological language models |
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Step 1. Click the contine button and your can see 'DNA-BLM', 'RNA-BLM' and 'Protein-BLM' options for DNA, RNA and Protein sequence analysis as show in Fig2. Select 'Protein-BLM' option and enter next step.
Step 2. Click the contine button and your can see 'residue-level' and 'sequence-level' options for biological sequence analyis at residue-level and sequence-level as show in Fig3. Select 'residue-level' option and enter next step.
Step 3. Click the contine button and your can see all the machine learning algorithm for predictor construction at residue-level as shown in Fig4. Select 'Support vector machine' algorithm for your predictor and enter next step.
Step 4. Click the contine button and your can see the submit interface as shown in Fig5. You can set the parameters of feature extraction, predictor construction, performance evaluation and feature analysis at submit interface.
Step 5. This step is for parameter setting and submitting. You can just click example button for automaticly setting optimal parameter and submitting the example data or set the parameter one by one and submit your dataset. If you just click example button, your can see the results as show in Fig6.
step 6. You can download and check output feature vectors and model at results interface. And you can click back button to adjust your parameter or click re-analysis button for a new analyis.
Step 1. Click the contine button and your can see 'DNA-BLM', 'RNA-BLM' and 'Protein-BLM' options for DNA, RNA and Protein sequence analysis as show in Fig7. Select 'DNA-BLM' option and enter next step.
Step 2. Click the contine button and your can see 'residue-level' and 'sequence-level' options for biological sequence analyis at residue-level and sequence-level as show in Fig8. Select 'sequence-level' option and enter next step.
Step 3. Click the contine button and your can see all the feature extraction mode for biological sequence analyis at sequence-level as shown in fig9. Select 'bag of words' option and enter next step.
Step 4. Click the contine button and your can see 'No' and 'Yes' options for semantic similarity calculation as show in Fig10. Select 'No' option and enter next step.
Step 5. Click the contine button and your can see all the machine learning algorithm for predictor construction at residue-level as shown in Fig11. Select 'Support vector machine' algorithm for your predictor and enter next step.
Step 6. Click the contine button and your can see the submit interface as shown in Fig12. You can set the parameters of feature extraction, predictor construction, performance evaluation and feature analysis at submit interface.
Step 7. This step is for parameter setting and submitting. You can just click example button for automaticly setting optimal parameter and submitting the example data or set the parameter one by one and submit your dataset. If you just click example button, your can see the results as show in Fig13.
step 8. You can download and check output feature vectors and model at results interface. And you can click back button to adjust your parameter or click re-analysis button for a new analyis.