Raport de cercetare - Lorentz JÄNTSCHI

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unde variaţia este dată de formula: s 2 τ Z τ Ken , a Ken, a n = C − D = s 2 τ Ken , a ( n −1)( n + 5) Coeficientul de corelaţie Kendall tau-b: ÷ Ipoteza nulă H0: τKen,b = 0 (nu există corelaţie) ÷ Ipoteza alternativă H1: τKen,b 0 (există corelaţie) Formula folosită pentru calculul parametrului testului Z în cazul coeficientului de corelaţie Kendall tau-b este: C − D Z τ Ken, b = unde variaţia este dată de formula Tied( X) Tied( Y) ⎛ ⎞ ⎜ ∑ ni ( ni −1)( ni + 5) + ∑ mi ( mi −1)( mi + 5) ⎟ 2 2 s s ⎜ i= 1 i= 1 ⎟ τ = Ken , b τ − + Ken , a ⎜ 18 ⎟ ⎜ ⎟ ⎝ ⎠ Tied( X) Tied( Y) Tied( X) Tied( Y) ni ( )( ) ( )( ) ( ni −1)( ni − 2) mi ( mi −1)( mi − 2) ∑ ni ni −1 ni + 5 ∑ mi mi −1 mi + 5 ∑ ∑ i= 1 i= 1 i= 1 9 i= 1 9 + + 2n( n −1) 9n( n −1)( n − 2) Coeficientul de corelaţie Kendall tau-c: ÷ Ipoteza nulă H0: τKen,c = 0 (nu există corelaţie) ÷ Ipoteza alternativă H1: τKen,c 0 (există corelaţie) 4.c. Rezultate: Matricea valorilor coeficientului de corelaţie Kendall tau-a: d_IP d_IR d_Cr d_RSD d_Volum d_IP 1 0.9 0.0474 0.8947 0.2368 d_IR 1 0.0105 0.9316 0.2632 d_Cr 1 0.0158 0.2053 d_RSD 1 0.2368 d_Volum Matricea valorilor parametrului testului Z: 1 Z(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP INF 5.548 -0.292 5.5155 1.46 d_IR INF -0.0649 5.7426 1.6222 d_Cr INF -0.0973 -1.2653 d_RSD INF 1.46 d_Volum Matricea probabilităţiilor (p-value): INF pZ(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP 0 2.89E-08 1.2297 3.48E-08 0.1443 d_IR 0 1.0517 9.32E-09 0.1048 d_Cr 0 1.0775 1.7942 d_RSD 0 0.1443 d_Volum 0 Valorile lui p-value colorate in albastru sunt semnificative (
Matricea valorilor coeficientului de corelaţie Kendall tau-b: τb(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP 1 0.9293 0.0481 0.9239 0.2446 d_IR 1 0.0107 0.962 0.2717 d_Cr 1 0.016 0.2086 d_RSD 1 0.2446 d_Volum Matricea valorilor parametrului testului Z: 1 Z(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP INF 5.5987 -0.2933 5.566 1.4733 d_IR INF -0.0652 5.7951 1.637 d_Cr INF -0.0978 -1.2711 d_RSD INF 1.4733 d_Volum Matricea probabilităţiilor (p-value): INF pZ(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP 0 2.16E-08 1.2307 2.61E-08 0.1407 d_IR 0 1.052 6.83E-09 0.1016 d_Cr 0 1.0779 1.7963 d_RSD 0 0.1407 d_Volum Matricea valorilor coeficientului de corelaţie Kendall tau-b: 0 τc(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP 1 0.855 0.045 0.85 0.225 d_IR 1 0.01 0.885 0.25 d_Cr 1 0.015 0.195 d_RSD 1 0.225 d_Volum Matricea valorilor parametrului testului Z: 1 Z(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP INF 5.3188 -0.2787 5.2877 1.3997 d_IR INF -0.0619 5.5054 1.5552 d_Cr INF -0.0929 -1.2076 d_RSD INF 1.3997 d_Volum Matricea probabilităţiilor (p-value): INF pZ(XX) d_IP d_IR d_Cr d_RSD d_Volum d_IP 0 1.05E-07 1.2195 1.24E-07 0.1616 d_IR 0 1.0494 3.68E-08 0.1199 d_Cr 0 1.074 1.7728 d_RSD 0 0.1616 d_Volum 0 Valorile lui p-value colorate în albastru sunt semnificative (
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muchiei e); ponderile pentru vârfu
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ale grafului; între numărul de fe
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Detur d5,1=3; d5,2=3; d5,3=2; d5,4=
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3 1 024220000 1145 4 0 022100222 11
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o ΣA(·) 2 : 231; o Matricea: De 1
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(10, 3) [10, 6, 3] {7, 10, 11} (3,
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(5, 11) [5, 1, 2, 3, 6, 9, 8, 7, 11
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(1, 11) [1, 2, 3, 6, 10, 11] {1, 4,
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(9, 11) [9, 8, 7, 11] {1, 2, 3, 4,
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(3, 8) [3, 6, 10, 11, 7, 8] {1, 2,
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Tabelul 10. Matricea caracteristic
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10 6 7 8 9 10 11 11 6 7 8 9 10 11 6
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Astfel, adaptând principiul I al t
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Din formula sa de definiţie dS = d
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produsul final al întregului lanţ
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Tabelul 16. Cât de dulci sunt zaha
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÷ CSLS; ÷ Cyberlipid Center; ÷ D
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÷ STING Millenium Suite; ÷ wwPDB;
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pentru care algoritmul performează
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scopul maximizării randamentului d
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solului, calitatea vremii, manageme
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Fragmentation criteria Fragment of
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o Se încrucişează Genotip1 cu Ge
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Measures of Disease in Health Resea
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Generarea întâmplătoare stratifi
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Functional Networks Noelia Sánchez
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o SVMs application to protein secon
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Meta-learning for Algorithm Recomme
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Similarity Assessment with PDR-FP
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• Representations of a molecular
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Introduction - Non HTS Hit Recognit
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Post-processing: Visual Inspection
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PCB013 146.55 -2.7348 0.3315 0.3241
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PCB113 136.08 -3.2367 0.5862 0.5861
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Regression 1 6.730776811 6.730777 7
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Metoda Formula intervalului de înc
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1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS 0
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Metoda AvADA(0) AvADA(1) AvADS(0) A
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12 11.01 9.99 10 8 6 4 2 0 6 5 4 3
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Planificarea activităţilor experi
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o Generalized functions; o Operator
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approaches of QSAR modeling o Sessi
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Bookhaven pe care o transferă prog
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1.21739507388622E+000 y= 7.38149868
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20 020_3613389 7.783 -1.627 5.661 0
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} function atom_info($atom,&$cnv_to
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}else{ } } $this->tmp_dist=array();
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} } return "({".implode(",",$this->
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$fr_f_t=pow(pow($fr_f_x,2)+pow($fr_
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} } $this->f[$a]=$ret; } Interacţi
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÷ 2 tipuri de selecţii ale valori
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} $ret[$j][$k]=array(0.0,0.0,0.0,0.
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$ok=$this->check_finite();if(!$ok)r
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$q=mysql_query($query_prop."'".$mdf
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2005-RIMC; Jäntschi & Bolboacă, 2
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0.918 0.9 5 14 23110_ 69 332064 837
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0.9665 0.9525 4 30 33504 73 r = 0.9
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20 41521_ lNMrEQg*0.336843860556055
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54 DevMTOp25_ y=1.893045064859439+
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82 19654_ lIDRFMg*-1.31755354516111
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y=3.463929176330566+ ABDmtQg*-13.01
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iBMmwHg*1195.781250000000000+ iFPME
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161 15aacidsHyd_ lSDmwMt*6.37250438
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Relaţii semicantitative structură
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MDFV (Jäntschi şi Bolboacă, 2008
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SAPF (©2009) CF:3 DO:2 AP:5 DP:6 P
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N0) fenotipurilor între generaţii
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adevăr (T/F) pentru fiecare operat
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S5 String[255] Şir de maxim 255 ca
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end; with(MDF)do repeat CF_Rs;SA_Fr
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sfs_FITNESS_strategy= proportional/
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parametrul statistic descriptiv mx
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d_n:S9; Variabilă folosită la pen
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(s:S9):B0T; configurare a execuţie
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; ÷ mută părinţi cu probabilita
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procedure SL_QSr (i,j:I0T); procedu
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XX_min = XX_max = XX_avg = XX (XX =
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$p_max[$i]=1.0*$m-1.0*$m*$tdist->_g
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$d[$i][$k++]=$c[$i][$j]; for($i=0;$
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for($i=0;$i
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mat_means($n,$m,$y,$x,$ma,$mb); red
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} if(strlen($s)>0) $t[]=$s; return
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este necesar ca să se asume ipotez
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6(( b − a + 1) Asimetria; excesul
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Minim; Maxim 0; ∞ Funcţia de pro
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Varianţa Var γ1 (nX-1)σ 2 /nX 2
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Σ1≤i≤m(Ŷi-Yi) 2 → min. (3)
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Tabelul 24. Valorile optimizate ale
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eroare m(n-1) 2 m ⎛ n ⎛ n ⎞
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http://l.academicdirect.ro/Chemistr
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73 la convergenţă folosind un alg
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Cl(n) Cl (n) PCBs: Seria bifenililo
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PCB027 Cl Cl Cl 5.447 PCB028 Cl Cl
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PCB071 Cl Cl Cl Cl 5.987 PCB072 Cl
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PCB110 Cl Cl Cl Cl Cl 6.532 PCB111
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PCB151 Cl Cl Cl Cl Cl Cl 6.647 PCB1
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PCB191 Cl Cl Cl Cl Cl Cl Cl 7.557 P
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O altă remarcă se poate face cu p
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al eşantionului pe care o induc me
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PmkEt 1 26 26 sDDJEg 1 26 26 MDDKHt
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86 Abateri semnificative statistic
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Generaţii ce produc evoluţii (num
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Rar (D, P) (D, T) Figura 2. Cât de
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2010A5. Construirea bazei de date c
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Portalul "MDF" Portalul "Statistics
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(Bolboacă & Jäntschi, 2007-AAHS):
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Environment, Applied Medical Inform
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AcademicDirect, ISSN 1583-1078, www
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Observable: Maximum Likelihood Esti
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and Veterinary Medicine Cluj-Napoca
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Relative Response Factor using Mole
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Raport de cercetare - Lorentz JÄNTSCHI

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