Raport de cercetare - Lorentz JÄNTSCHI

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Modified Learning Rules with the Self-Adaptive Learning Rate Convergence of the learning algorithm MVN as a model of a biological neuron P-realizable Boolean Functions over the field of Complex numbers and a Universal Binary Neuron Dependence of the number of sectors on the number of inputs Learning Algorithm for UBN Mail Applications o Kernel PLS Introduction • Latent Variables • The Machine Learning Paradox • Beyond Regression (PCR) Principal Component Analysis (PCA) • Definition • NIPALS algorithm • Principal Component Regression (PCR) • Loading factors and variable selection Partial Least Squares (PLS) Nonlinear PLS • Kernels • Direct kernel methods • K-PCA • K-PLS • Preprocessing and centering the kernel • Variable/feature selection with sensitivity analysis Applications • Portuguese Wine data • Pima Indians • Italian Olive Oils multi-class classification • Svante Wold’s QSAR data • Real QSAR data • Time series prediction o Multi-Valued and UB Neurons - II Associative memory A feedforward multilayer MVN-based neural network (MLMVN), its derivative-free backpropagation learning algorithm and solving some classification and prediction problems Learning of the genetic code using MLMVN Gene expression data classification using MLMVN Blur identification using MLMVN Solving the problem of Gene Expression Patterns Classification using the MVN-based neural network Learning of non-threshold Boolean functions using a single UBN o Selecting Algorithms and Parameters with Meta-Learning Background: why is this a problem? • approximating functions with machine learning • algorithm selection and bias • a few solutions Meta-Learning: THE solution 140
Meta-learning for Algorithm Recommendation o Text Mining What is text mining Text mining process Visualizations for text mining Kernel methods Case studies Fingerprinting text Text categorization Applying text mining to bioinformatics Customer service center analysis Detecting ontologies 2008A4. Stabilirea necesarelor de materiale şi consumabile, identificarea furnizorilor şi condiţiilor de procurare, întocmirea documentaţiilor de procurare, derularea licitaţiilor pentru achiziţii ÷ S-au achiziţionat serie de echipamente pentru cercetare: o Fluorometru (pentru analize în soluţie) o Staţie Meteo Wireless (pentru monitorizare parametrii de mediu) o Notebook (pentru preluare date de la fluorometru şi de monitorizare parametrii de mediu) ÷ S-au achiziţionat o serie de obiecte de inventar necesare cercetării: o Procesor CPU Intel Xeon Quad Core; Multifunctional Brother; Panou solar fotovoltaic; Acumulatori; Regulator de încărcare; Afişaj regulator; Invertor; Imprimanta Canon; Senzor umiditate frunze; Senzor umiditate sol; Senzor temperatura din otel inoxidabil; Repetitor; Antena Yagi; Repetitor standard wireless ÷ S-au achiziţionat o serie de cărţi: o Evolutionary Dynamics; Evolutionary Computation: The Fossil Record; The Genetical Theory of Natural Selection 2008A5. Participări la manifestări ştiinţifice (diseminare - conferinţe, congrese, workshop-uri) şi dobândirea de competenţe complementare (stagii de documentare/cercetare în străinătate) Strasbourg Summer School on Chemoinformatics: CheminfoS3 ÷ Organizator: Louis Pasteur University (ULP), Strasbourg, FR ÷ Tip: şcoală de vară ÷ Perioadă: 20-04 Iunie-Iulie 2008; ÷ Deplasare: aferentă Obiectiv 2, Activitate 5; ÷ Participanţi: Lorentz JÄNTSCHI, Carmen E. STOENOIU; ÷ Cursurile şcolii: QSAR: discovery and first steps T. Fujita History and challenges of chemoinformatics J. Gasteiger Current trends in chemoinformatics W. Warr Fingerprint Design and Molecular Complexity Effects J. Bajorath Diversity Analysis and Library Design V. Gillet De novo Design G. Schneider Lessons learned from modelling bioactivity - what works and what doesn't R. Glen 141
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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
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
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: o SVMs application to protein secon
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 and 158: Metoda Formula intervalului de înc
Page 159 and 160: 1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS 0
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
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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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