Raport de cercetare - Lorentz JÄNTSCHI

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6 5 1 0 0 1 7 6 0 1 0 1 8 7 0 1 1 1 9 8 1 1 0 0 10 9 1 0 1 0 11 10 1 1 1 1 12 11 0 0 0 0 L12 Factori (nivele) 6 5 A(6) B(6) C(6) D(6) E(6) 1 0 0 0 0 3 2 1 0 5 5 0 3 2 1 0 5 4 4 3 1 5 0 2 5 4 2 4 1 5 6 5 2 2 4 3 7 0 5 4 2 4 8 1 5 1 2 0 9 2 4 3 4 5 10 3 4 3 3 1 11 4 3 2 3 2 12 5 3 1 1 1 L12 Factori (nivele) 4 2 ×3 2 ×2 1 A(4) B(4) C(3) D(3) E(2) 1 1 1 0 1 1 2 1 1 1 1 1 3 1 2 2 1 0 4 0 2 0 0 1 5 3 0 1 1 0 6 3 2 2 0 1 7 3 3 0 0 0 8 0 3 1 2 0 9 2 3 2 2 1 10 2 1 0 2 0 11 2 0 1 2 1 12 0 0 2 0 0 L12 Factori (nivele) 4 1 ×3 2 ×2 2 A(4) B(3) C(3) D(2) E(2) 1 1 1 1 0 1 2 2 1 1 1 1 3 1 1 1 0 1 4 2 1 1 1 1 5 3 0 0 0 1 6 0 2 2 1 1 7 1 0 2 0 0 8 2 0 2 1 0 9 0 2 0 0 0 10 3 2 0 1 0 11 3 2 2 0 0 12 0 0 0 1 0 L12 Factori (nivele) 68
3 1 ×2 4 A(3) B(2) C(2) D(2) E(2) 1 1 0 1 1 1 2 1 1 1 1 1 3 0 0 1 1 0 4 0 1 1 0 1 5 2 0 1 0 0 6 2 1 1 0 0 7 1 0 0 1 0 8 2 1 0 1 1 9 0 0 0 0 1 10 1 1 0 1 0 11 2 0 0 0 1 12 0 1 0 0 0 L12 Factori (nivele) 12 4 A(12) B(12) C(12) D(12) 1 0 0 0 0 2 1 1 10 11 3 2 11 1 10 4 3 10 11 1 5 4 9 2 7 6 5 2 9 6 7 6 8 7 5 8 7 7 8 2 9 8 6 5 4 10 9 5 6 8 11 10 3 4 9 12 11 4 3 3 L12 Factori (nivele) 12 1 ×6 2 A(12) B(6) C(6) 1 0 1 1 2 1 1 1 3 2 0 5 4 3 4 5 5 4 5 2 6 5 5 0 7 6 4 2 8 7 3 3 9 8 3 4 10 9 2 4 11 10 2 3 12 11 0 0 L14 Factori (nivele) 7 5 ×2 1 B(7) C(7) D(7) E(7) F(7) A(2) 1 0 0 0 0 5 0 2 0 0 6 4 1 1 3 1 6 0 6 0 0 4 1 6 1 2 5 1 5 2 1 4 5 2 0 6 2 5 5 0 2 1 7 3 5 6 1 3 0 69
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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
Page 17 and 18: ISDmsHt lADrtHg Y Equation Residue
Page 19 and 20: ISDmsHt lADrtHg Y Equation Residue
Page 21 and 22: Tabelul următor prezintă sintetic
Page 23 and 24: d_RSD INF 2.6057 d_Volum Matricea p
Page 25 and 26: 30 1.02 0.63 1.75 2.67 160 31 1.14
Page 27 and 28: Matricea valorilor coeficientului d
Page 29 and 30: evaluare sistematică pentru identi
Page 31 and 32: introductivă în subiectul prezent
Page 33 and 34: Studio (al companiei Accelrys) a pe
Page 35 and 36: Setting the weights equal to 1 give
Page 37 and 38: ÷ Lucrare: Quantitative structure-
Page 39 and 40: compound ranking in the database. A
Page 41 and 42: vectors based on the cloning strate
Page 43 and 44: "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: 12 0 0 0 1 0 0 L12 Factori (nivele)
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 and 96: (5, 11) [5, 1, 2, 3, 6, 9, 8, 7, 11
Page 97 and 98: (1, 11) [1, 2, 3, 6, 10, 11] {1, 4,
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
Page 115 and 116: produsul final al întregului lanţ
Page 117 and 118: 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
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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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