LLSn2List: convert data.frame of HumanNet log-likelihood Score to list
In dSimer: Integration of Disease Similarity Methods

Description Usage Arguments Value Author(s) References See Also Examples

convert HumanNet normalized log-likelihood score from data.frame to list, which will be used in FunSim method

1	LLSn2List(LLSn)

LLSn

data.frame of gene-gene normalized log-likelihood score in HumanNet

a list of normalized log-likelihood score

Peng Ni, Min Li

Cheng L, Li J, Ju P, et al. SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association[J]. PloS one, 2014, 9(6): e99415.

FunSim

## see examples in function FunSim
data(HumanNet_sample)
llsnlist<-LLSn2List(HumanNet_sample[1:100,])
llsnlist

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union



$`10755`
     4646 
0.9500702 

$`4175`
    55388      4176     51053 
0.9258415 0.8600542 0.8220114 

$`207`
     5170      3480      2309      7046 
0.9020164 0.8225438 0.7928080 0.7757723 

$`4292`
     5395      4436      4437      9156      5378     54802 
0.8919834 0.8350411 0.8318869 0.7694810 0.7565174 0.7555089 

$`3614`
     3615      8833      4839     55131     64794 
0.8862736 0.8428776 0.8017585 0.7944462 0.7698911 

$`3845`
     5605      5604      5594       673      1956 
0.8845156 0.8816185 0.8747013 0.8628220 0.8389933 

$`6206`
     6210      9045      6233      6228      6207      6230      6218      9349 
0.8811791 0.8782109 0.8613759 0.8597318 0.8591496 0.8514737 0.8396623 0.8230757 
     6231      6227      6223      6229      7311      6234      6224      6217 
0.8064044 0.7840032 0.7835836 0.7821113 0.7809096 0.7801854 0.7716179 0.7674980 
     6208 
0.7588320 

$`1956`
     2885       868     55327      3845      8573       321      2002 
0.8811789 0.8605607 0.8435167 0.8389933 0.8225438 0.8225438 0.8225438 

$`4436`
     4437      5395      9156      5111      6117      4292 
0.8798991 0.8530540 0.8482553 0.8332843 0.8287356 0.8350411 

$`1630`
     9423    219699 
0.8695297 0.8388120 

$`2065`
   3084 
0.85506 

$`57096`
     6103 
0.8527287 

$`4089`
     4093      4090       658        92      7046       653      4087 
0.8505702 0.8338044 0.8189088 0.7757723 0.7714445 0.7583792 0.8298381 

$`3364`
     5884 
0.8474567 

$`3480`
     5728      8856      7376      7046      5290       207 
0.8448986 0.8225438 0.8225438 0.8225438 0.8225438 0.8225438 

$`355`
     8772       356 
0.8439580 0.7733132 

$`80155`
    8260 
0.835611 

$`4087`
     4089 
0.8298381 

$`857`
      858 
0.8259293 

$`5879`
    63916      7204 
0.8225438 0.7906668 

$`26271`
     991 
0.819102 

$`1869`
      983      7027 
0.8161709 0.7953175 

$`3481`
     3488 
0.8060364 

$`11200`
    25842 
0.8044691 

$`1535`
     4688      4689 
0.7953668 0.7759786 

$`5054`
     5328      7448 
0.7948348 0.7530541 

$`1025`
      904 
0.7820505 

$`2252`
     2255 
0.7798692 

$`3756`
    51384       473 
0.7757723 0.7757723 

$`2064`
     2886 
0.7755482 

$`581`
      637       598 
0.7750670 0.7531713 

$`5894`
     7529      5906 
0.7741392 0.7570881 

$`6310`
     6311 
0.7707042 

$`11065`
      983 
0.7645746 

$`1026`
     1647      8900 
0.7616913 0.7554571 

$`2260`
     2885      8822 
0.7547656 0.7532358 

$`1027`
     8900 
0.7502851 

$`4646`
    10755 
0.9500702 

$`55388`
     4175 
0.9258415 

$`5170`
      207 
0.9020164 

$`5395`
     4292      4436 
0.8919834 0.8530540 

$`3615`
     3614 
0.8862736 

$`5605`
     3845 
0.8845156 

$`5604`
     3845 
0.8816185 

$`6210`
     6206 
0.8811791 

$`2885`
     1956      2260 
0.8811789 0.7547656 

$`4437`
     4436      4292 
0.8798991 0.8318869 

$`9045`
     6206 
0.8782109 

$`5594`
     3845 
0.8747013 

$`9423`
     1630 
0.8695297 

$`673`
    3845 
0.862822 

$`6233`
     6206 
0.8613759 

$`868`
     1956 
0.8605607 

$`4176`
     4175 
0.8600542 

$`6228`
     6206 
0.8597318 

$`6207`
     6206 
0.8591496 

$`3084`
   2065 
0.85506 

$`6103`
    57096 
0.8527287 

$`6230`
     6206 
0.8514737 

$`4093`
     4089 
0.8505702 

$`9156`
     4436      4292 
0.8482553 0.7694810 

$`5884`
     3364 
0.8474567 

$`5728`
     3480 
0.8448986 

$`8772`
     355 
0.843958 

$`55327`
     1956 
0.8435167 

$`8833`
     3614 
0.8428776 

$`6218`
     6206 
0.8396623 

$`219699`
    1630 
0.838812 

$`8260`
   80155 
0.835611 

$`4090`
     4089 
0.8338044 

$`5111`
     4436 
0.8332843 

$`6117`
     4436 
0.8287356 

$`858`
      857 
0.8259293 

$`9349`
     6206 
0.8230757 

$`63916`
     5879 
0.8225438 

$`8856`
     3480 
0.8225438 

$`7376`
     3480 
0.8225438 

$`7046`
     3480       207      4089 
0.8225438 0.7757723 0.7714445 

$`5290`
     3480 
0.8225438 

$`8573`
     1956 
0.8225438 

$`321`
     1956 
0.8225438 

$`2002`
     1956 
0.8225438 

$`51053`
     4175 
0.8220114 

$`991`
   26271 
0.819102 

$`658`
     4089 
0.8189088 

$`983`
     1869     11065 
0.8161709 0.7645746 

$`6231`
     6206 
0.8064044 

$`3488`
     3481 
0.8060364 

$`25842`
    11200 
0.8044691 

$`4839`
     3614 
0.8017585 

$`4688`
     1535 
0.7953668 

$`7027`
     1869 
0.7953175 

$`5328`
     5054 
0.7948348 

$`55131`
     3614 
0.7944462 

$`2309`
     207 
0.792808 

$`7204`
     5879 
0.7906668 

$`6227`
     6206 
0.7840032 

$`6223`
     6206 
0.7835836 

$`6229`
     6206 
0.7821113 

$`904`
     1025 
0.7820505 

$`7311`
     6206 
0.7809096 

$`6234`
     6206 
0.7801854 

$`2255`
     2252 
0.7798692 

$`4689`
     1535 
0.7759786 

$`92`
     4089 
0.7757723 

$`51384`
     3756 
0.7757723 

$`473`
     3756 
0.7757723 

$`2886`
     2064 
0.7755482 

$`637`
     581 
0.775067 

$`7529`
     5894 
0.7741392 

$`356`
      355 
0.7733132 

$`6224`
     6206 
0.7716179 

$`6311`
     6310 
0.7707042 

$`64794`
     3614 
0.7698911 

$`6217`
    6206 
0.767498 

$`1647`
     1026 
0.7616913 

$`6208`
    6206 
0.758832 

$`653`
     4089 
0.7583792 

$`5906`
     5894 
0.7570881 

$`5378`
     4292 
0.7565174 

$`54802`
     4292 
0.7555089 

$`8900`
     1026      1027 
0.7554571 0.7502851 

$`8822`
     2260 
0.7532358 

$`598`
      581 
0.7531713 

$`7448`
     5054 
0.7530541