A combination of compositional index and genetic algorithm for predicting transmembrane helical segments.

References 30

1. 1.

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2. 2.

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3. 3.

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4. 4.

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5. 5.

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6. 6.

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7. 7.

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8. 8.

   This reference’s wording can’t be shown under the paper’s licence.

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9. 9.

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10. 10.

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11. 11.

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12. 12.

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13. 13.

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14. 14.

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15. 15.

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16. 16.

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17. 17.

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18. 18.
    Transmembrane protein topology prediction using support vector machines · 2009

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19. 19.

    This reference’s wording can’t be shown under the paper’s licence.

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20. 20.

    This reference’s wording can’t be shown under the paper’s licence.

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21. 21.

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    Not in our library

22. 22.

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    Not in our library

23. 23.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

24. 24.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

25. 25.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

26. 26.

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    Not in our library

27. 27.

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    Not in our library

28. 28.

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    Not in our library

29. 29.

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    Not in our library

30. 30.

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