Raport de cercetare - Lorentz JÄNTSCHI

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10 1 1 1 1 1 1 1 7 1 0 1 16 11 2 2 2 2 2 1 1 1 2 1 0 16 Σ 25 25 18 25 25 17 22 27 22 22 27 255 o Σ(RCJΔ): 38.01(6); o Σ(RCJΔ) 2 : 26.6857; o ΣA(RCJΔ): 9.492857; o ΣA(RCJΔ) 2 : 7.6523809; o Matricea RCJΔ: RCJΔ 1 2 3 4 5 6 7 8 9 10 11 Σ 1 0.000 1.000 1.000 1.000 1.000 0.500 0.500 0.500 0.500 0.500 0.500 7.000 2 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 10.000 3 0.143 0.143 0.000 0.143 0.143 0.200 0.200 0.200 0.200 0.200 0.200 1.771 4 1.000 1.000 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 10.000 5 1.000 1.000 1.000 1.000 0.000 0.500 0.500 0.500 0.500 0.500 0.500 7.000 6 0.167 0.167 0.167 0.167 0.167 0.000 0.143 0.167 0.167 0.167 0.167 1.643 7 0.333 0.333 0.500 0.333 0.333 0.500 0.000 1.000 1.000 1.000 1.000 6.333 8 0.500 0.500 0.500 0.500 0.500 1.000 1.000 0.000 1.000 0.500 1.000 7.000 9 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 1.000 0.143 9.143 10 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.143 1.000 0.000 1.000 9.143 11 0.500 0.500 0.500 0.500 0.500 1.000 1.000 1.000 0.500 1.000 0.000 7.000 Σ 6.643 6.643 7.667 6.643 6.643 7.700 7.343 6.510 6.867 6.867 6.510 76.033 ÷ Matricea Cluj Fragmental pe Distanţe, CFD: o Definiţie: CFDi,j = max|CFDSi,j|; {k}∈CFDSi,j ⇔ d(Gp)k,i
(1, 11) [1, 2, 3, 6, 10, 11] {1, 4, 5} (11, 1) [11, 10, 6, 3, 2, 1] {7, 8, 9, 11} (2, 3) [2, 3] {1, 2} (3, 2) [3, 2] {3, 4, 6, 7, 8, 9, 10, 11} (2, 4) [2, 3, 4] {1, 2} (4, 2) [4, 3, 2] {4, 5} (2, 5) [2, 1, 5] {2, 3, 6, 7, 8, 9, 10, 11} (5, 2) [5, 1, 2] {4, 5} (2, 6) [2, 3, 6] {1, 2, 4, 5} (6, 2) [6, 3, 2] {6, 7, 8, 9, 10, 11} (2, 7) [2, 3, 6, 9, 8, 7] {1, 2, 4, 5} (7, 2) [7, 8, 9, 6, 3, 2] {7, 10, 11} (2, 7) [2, 3, 6, 10, 11, 7] {1, 2, 4, 5} (7, 2) [7, 11, 10, 6, 3, 2] {7, 8, 9} (2, 8) [2, 3, 6, 9, 8] {1, 2, 4, 5} (8, 2) [8, 9, 6, 3, 2] {7, 8, 10, 11} (2, 9) [2, 3, 6, 9] {1, 2, 4, 5} (9, 2) [9, 6, 3, 2] {7, 8, 9, 10, 11} (2, 10) [2, 3, 6, 10] {1, 2, 4, 5} (10, 2) [10, 6, 3, 2] {7, 8, 9, 10, 11} (2, 11) [2, 3, 6, 10, 11] {1, 2, 4, 5} (11, 2) [11, 10, 6, 3, 2] {7, 8, 9, 11} (3, 4) [3, 4] {2, 3, 6, 7, 8, 9, 10, 11} (4, 3) [4, 3] {4, 5} (3, 5) [3, 4, 5] {2, 3, 6, 7, 8, 9, 10, 11} (5, 3) [5, 4, 3] {1, 5} (3, 6) [3, 6] {1, 2, 3, 4, 5} (6, 3) [6, 3] {6, 7, 8, 9, 10, 11} (3, 7) [3, 6, 10, 11, 7] {1, 2, 3, 4, 5} (7, 3) [7, 11, 10, 6, 3] {7, 8, 9} (3, 7) [3, 6, 9, 8, 7] {1, 2, 3, 4, 5} (7, 3) [7, 8, 9, 6, 3] {7, 10, 11} (3, 8) [3, 6, 9, 8] {1, 2, 3, 4, 5} (8, 3) [8, 9, 6, 3] {7, 8, 10, 11} (3, 9) [3, 6, 9] {1, 2, 3, 4, 5} (9, 3) [9, 6, 3] {7, 8, 9, 10, 11} (3, 10) [3, 6, 10] {1, 2, 3, 4, 5} (10, 3) [10, 6, 3] {7, 8, 9, 10, 11} (3, 11) [3, 6, 10, 11] {1, 2, 3, 4, 5} (11, 3) [11, 10, 6, 3] {7, 8, 9, 11} (4, 5) [4, 5] {3, 4, 6, 7, 8, 9, 10, 11} (5, 4) [5, 4] {1, 5} (4, 6) [4, 3, 6] {1, 2, 4, 5} (6, 4) [6, 3, 4] {6, 7, 8, 9, 10, 11} (4, 7) [4, 3, 6, 9, 8, 7] {1, 2, 4, 5} (7, 4) [7, 8, 9, 6, 3, 4] {7, 10, 11} (4, 7) [4, 3, 6, 10, 11, 7] {1, 2, 4, 5} (7, 4) [7, 11, 10, 6, 3, 4] {7, 8, 9} (4, 8) [4, 3, 6, 9, 8] {1, 2, 4, 5} (8, 4) [8, 9, 6, 3, 4] {7, 8, 10, 11} 97
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Raport de cercetare - lucrare în e
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2. Scop şi obiective Scopul proiec
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abordare integrată în căutarea r
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• cu acces parţial restricţiona
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2007A1. Planificarea activităţilo
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2 Y = 0.852 -1 =a+bX -1 (Y -1 -a-bX
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PCB128 136.38 -3.4116 0.7761 PCB129
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ISDmsHt lADrtHg Y Equation Residue
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Tabelul următor prezintă sintetic
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d_RSD INF 2.6057 d_Volum Matricea p
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30 1.02 0.63 1.75 2.67 160 31 1.14
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Matricea valorilor coeficientului d
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evaluare sistematică pentru identi
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introductivă în subiectul prezent
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Studio (al companiei Accelrys) a pe
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Setting the weights equal to 1 give
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÷ Lucrare: Quantitative structure-
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compound ranking in the database. A
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vectors based on the cloning strate
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"Recent Advances in Synthesis and C
Page 45 and 46: will be highly inactive, moderately
Page 47 and 48: Publication Frequency: 8 issues per
Page 49 and 50: of strand breaks and when they do o
Page 51 and 52: ibonucleotide reductase Journal of
Page 53 and 54: scenarios are to be compared, e.g.
Page 55 and 56: hydrazones • ligand-based drug de
Page 57 and 58: modalitatea de culegere, colectare
Page 59 and 60: ÷ Transversală: studierea unui e
Page 61 and 62: 9 10 12 8 1 4 3 2 2 15 8 5 4 3 4 8
Page 63 and 64: 2 1 1 2 3 2 0 1 4 3 2 2 5 4 2 0 6 5
Page 65 and 66: 10 1 0 1 0 1 1 1 1 0 1 0 11 0 0 0 1
Page 67 and 68: 12 0 0 0 1 0 0 L12 Factori (nivele)
Page 69 and 70: 3 1 ×2 4 A(3) B(2) C(2) D(2) E(2)
Page 71 and 72: 9 8 0 2 2 2 1 1 10 9 1 2 1 1 0 0 11
Page 73 and 74: 7 1 4 1 1 2 4 8 2 2 3 4 1 1 9 3 2 1
Page 75 and 76: 1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0
Page 77 and 78: 11 10 9 9 4 11 12 11 8 10 3 13 13 1
Page 79 and 80: ÷ Endorelaţie binară (`2-e-r`):
Page 81 and 82: muchiei e); ponderile pentru vârfu
Page 83 and 84: ale grafului; între numărul de fe
Page 85 and 86: Detur d5,1=3; d5,2=3; d5,3=2; d5,4=
Page 87 and 88: 3 1 024220000 1145 4 0 022100222 11
Page 89 and 90: o ΣA(·) 2 : 231; o Matricea: De 1
Page 91 and 92: (10, 3) [10, 6, 3] {7, 10, 11} (3,
Page 93 and 94: 11 0.250 0.333 0.333 0.333 0.250 0.
Page 95: (5, 11) [5, 1, 2, 3, 6, 9, 8, 7, 11
Page 99 and 100: (9, 11) [9, 8, 7, 11] {1, 2, 3, 4,
Page 101 and 102: (3, 8) [3, 6, 10, 11, 7, 8] {1, 2,
Page 103 and 104: 6 0.167 0.167 0.167 0.167 0.167 0.0
Page 105 and 106: 5 0.500 0.500 0.500 0.500 0.000 0.3
Page 107 and 108: Tabelul 10. Matricea caracteristic
Page 109 and 110: 10 6 7 8 9 10 11 11 6 7 8 9 10 11 6
Page 111 and 112: Astfel, adaptând principiul I al t
Page 113 and 114: Din formula sa de definiţie dS = d
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Page 117 and 118: Tabelul 16. Cât de dulci sunt zaha
Page 119 and 120: ÷ CSLS; ÷ Cyberlipid Center; ÷ D
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Page 123 and 124: pentru care algoritmul performează
Page 125 and 126: scopul maximizării randamentului d
Page 127 and 128: solului, calitatea vremii, manageme
Page 129 and 130: Fragmentation criteria Fragment of
Page 131 and 132: o Se încrucişează Genotip1 cu Ge
Page 133 and 134: Measures of Disease in Health Resea
Page 135 and 136: Generarea întâmplătoare stratifi
Page 137 and 138: Functional Networks Noelia Sánchez
Page 139 and 140: o SVMs application to protein secon
Page 141 and 142: Meta-learning for Algorithm Recomme
Page 143 and 144: Similarity Assessment with PDR-FP
Page 145 and 146: • 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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if($this->p12_2) $this->f[57] =$thi
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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][$k]=array(0.0,0.0,0.0,0.
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if(!$q){ } echo($query." :ERROR!\r\
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} unset($a); $query="INSERT INTO `"
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$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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