Raport de cercetare - Lorentz JÄNTSCHI

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1.2 1 0.8 0.6 0.4 0.2 1.2 0.8 0.6 0.4 0.2 Metoda Wald 0 0 1 2 3 4 5 6 7 8 9 10 1 Metoda Wald_C 0 0 1 2 3 4 5 6 7 8 9 10 1.2 1 0.8 0.6 0.4 0.2 Metoda Wilson 0 0 1 2 3 4 5 6 7 8 9 10 1.2 1 0.8 0.6 0.4 0.2 Metoda Wilson_C 0 0 1 2 3 4 5 6 7 8 9 10 158 Limita inferioara Limita superioara Limita inferioara Limita superioara Limita inferioara Limita superioara Limita inferioara Limita superioara
1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS 0 0 1 2 3 4 5 6 7 8 9 10 1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS1 0 0 1 2 3 4 5 6 7 8 9 10 1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS2 0 0 1 2 3 4 5 6 7 8 9 10 1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS3 0 0 1 2 3 4 5 6 7 8 9 10 159 Limita inferioara Limita superioara Limita inferioara Limita superioara Limita inferioara Limita superioara Limita inferioara Limita superioara
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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
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will be highly inactive, moderately
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Publication Frequency: 8 issues per
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of strand breaks and when they do o
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ibonucleotide reductase Journal of
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scenarios are to be compared, e.g.
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hydrazones • ligand-based drug de
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modalitatea de culegere, colectare
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÷ Transversală: studierea unui e
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9 10 12 8 1 4 3 2 2 15 8 5 4 3 4 8
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2 1 1 2 3 2 0 1 4 3 2 2 5 4 2 0 6 5
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10 1 0 1 0 1 1 1 1 0 1 0 11 0 0 0 1
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12 0 0 0 1 0 0 L12 Factori (nivele)
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3 1 ×2 4 A(3) B(2) C(2) D(2) E(2)
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9 8 0 2 2 2 1 1 10 9 1 2 1 1 0 0 11
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7 1 4 1 1 2 4 8 2 2 3 4 1 1 9 3 2 1
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1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0
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11 10 9 9 4 11 12 11 8 10 3 13 13 1
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÷ Endorelaţie binară (`2-e-r`):
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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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11 0.250 0.333 0.333 0.333 0.250 0.
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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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6 0.167 0.167 0.167 0.167 0.167 0.0
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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
Page 115 and 116: produsul final al întregului lanţ
Page 117 and 118: Tabelul 16. Cât de dulci sunt zaha
Page 119 and 120: ÷ CSLS; ÷ Cyberlipid Center; ÷ D
Page 121 and 122: ÷ STING Millenium Suite; ÷ wwPDB;
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
Page 147 and 148: Introduction - Non HTS Hit Recognit
Page 149 and 150: Post-processing: Visual Inspection
Page 151 and 152: PCB013 146.55 -2.7348 0.3315 0.3241
Page 153 and 154: PCB113 136.08 -3.2367 0.5862 0.5861
Page 155 and 156: Regression 1 6.730776811 6.730777 7
Page 157: Metoda Formula intervalului de înc
Page 161 and 162: Metoda AvADA(0) AvADA(1) AvADS(0) A
Page 163 and 164: 12 11.01 9.99 10 8 6 4 2 0 6 5 4 3
Page 165 and 166: Planificarea activităţilor experi
Page 167 and 168: o Generalized functions; o Operator
Page 169 and 170: approaches of QSAR modeling o Sessi
Page 171 and 172: Bookhaven pe care o transferă prog
Page 173 and 174: 1.21739507388622E+000 y= 7.38149868
Page 175 and 176: 20 020_3613389 7.783 -1.627 5.661 0
Page 177 and 178: } function atom_info($atom,&$cnv_to
Page 179 and 180: }else{ } } $this->tmp_dist=array();
Page 181 and 182: } } return "({".implode(",",$this->
Page 183 and 184: if($this->p12_2) $this->f[57] =$thi
Page 185 and 186: $fr_f_t=pow(pow($fr_f_x,2)+pow($fr_
Page 187 and 188: } } $this->f[$a]=$ret; } Interacţi
Page 189 and 190: ÷ 2 tipuri de selecţii ale valori
Page 191 and 192: } $ret[$j][$k]=array(0.0,0.0,0.0,0.
Page 193 and 194: if(!$q){ } echo($query." :ERROR!\r\
Page 195 and 196: } unset($a); $query="INSERT INTO `"
Page 197 and 198: $ok=$this->check_finite();if(!$ok)r
Page 199 and 200: $q=mysql_query($query_prop."'".$mdf
Page 201 and 202: 2005-RIMC; Jäntschi & Bolboacă, 2
Page 203 and 204: 0.918 0.9 5 14 23110_ 69 332064 837
Page 205 and 206: 0.9665 0.9525 4 30 33504 73 r = 0.9
Page 207 and 208: 20 41521_ lNMrEQg*0.336843860556055
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54 DevMTOp25_ y=1.893045064859439+
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82 19654_ lIDRFMg*-1.31755354516111
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y=3.463929176330566+ ABDmtQg*-13.01
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iBMmwHg*1195.781250000000000+ iFPME
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161 15aacidsHyd_ lSDmwMt*6.37250438
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Relaţii semicantitative structură
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MDFV (Jäntschi şi Bolboacă, 2008
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SAPF (©2009) CF:3 DO:2 AP:5 DP:6 P
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N0) fenotipurilor între generaţii
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adevăr (T/F) pentru fiecare operat
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S5 String[255] Şir de maxim 255 ca
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end; with(MDF)do repeat CF_Rs;SA_Fr
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sfs_FITNESS_strategy= proportional/
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parametrul statistic descriptiv mx
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d_n:S9; Variabilă folosită la pen
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(s:S9):B0T; configurare a execuţie
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; ÷ mută părinţi cu probabilita
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procedure SL_QSr (i,j:I0T); procedu
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XX_min = XX_max = XX_avg = XX (XX =
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$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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