Raport de cercetare - Lorentz JÄNTSCHI

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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
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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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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
Page 231 and 232: end; with(MDF)do repeat CF_Rs;SA_Fr
Page 233 and 234: sfs_FITNESS_strategy= proportional/
Page 235 and 236: parametrul statistic descriptiv mx
Page 237 and 238: d_n:S9; Variabilă folosită la pen
Page 239 and 240: (s:S9):B0T; configurare a execuţie
Page 241 and 242: ; ÷ mută părinţi cu probabilita
Page 243 and 244: procedure SL_QSr (i,j:I0T); procedu
Page 245 and 246: XX_min = XX_max = XX_avg = XX (XX =
Page 247 and 248: $p_max[$i]=1.0*$m-1.0*$m*$tdist->_g
Page 249 and 250: $d[$i][$k++]=$c[$i][$j]; for($i=0;$
Page 251 and 252: for($i=0;$i
Page 253 and 254: mat_means($n,$m,$y,$x,$ma,$mb); red
Page 255 and 256: } if(strlen($s)>0) $t[]=$s; return
Page 257 and 258: este necesar ca să se asume ipotez
Page 259 and 260: 6(( b − a + 1) Asimetria; excesul
Page 261 and 262: Minim; Maxim 0; ∞ Funcţia de pro
Page 263 and 264: Varianţa Var γ1 (nX-1)σ 2 /nX 2
Page 265 and 266: Σ1≤i≤m(Ŷi-Yi) 2 → min. (3)
Page 267 and 268: Tabelul 24. Valorile optimizate ale
Page 269 and 270: eroare m(n-1) 2 m ⎛ n ⎛ n ⎞
Page 271 and 272: http://l.academicdirect.ro/Chemistr
Page 277 and 278: 73 la convergenţă folosind un alg
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Page 287 and 288: PCB151 Cl Cl Cl Cl Cl Cl 6.647 PCB1
Page 289 and 290: PCB191 Cl Cl Cl Cl Cl Cl Cl 7.557 P
Page 291 and 292: O altă remarcă se poate face cu p
Page 293 and 294: al eşantionului pe care o induc me
Page 295 and 296: PmkEt 1 26 26 sDDJEg 1 26 26 MDDKHt
Page 297 and 298: 86 Abateri semnificative statistic
Page 299 and 300: Generaţii ce produc evoluţii (num
Page 301 and 302: Rar (D, P) (D, T) Figura 2. Cât de
Page 303 and 304: 2010A5. Construirea bazei de date c
Page 305 and 306: Portalul "MDF" Portalul "Statistics
Page 307 and 308: (Bolboacă & Jäntschi, 2007-AAHS):
Page 309 and 310: Environment, Applied Medical Inform
Page 311 and 312: AcademicDirect, ISSN 1583-1078, www
Page 313 and 314: Observable: Maximum Likelihood Esti
Page 315 and 316: and Veterinary Medicine Cluj-Napoca
Page 317 and 318: Relative Response Factor using Mole
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Raport de cercetare - Lorentz JÄNTSCHI

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