Predictive Modeling of Drug Sensitivity

Predictive Modeling of Drug Sensitivity
Author: Ranadip Pal
Publsiher: Unknown
Total Pages: 356
Release: 2017
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1162263242
Language: EN, FR, DE, ES & NL

Predictive Modeling of Drug Sensitivity Book Review:

Predictive Modeling of Drug Sensitivity

Predictive Modeling of Drug Sensitivity
Author: Ranadip Pal
Publsiher: Academic Press
Total Pages: 354
Release: 2016-11-15
ISBN 10: 012805431X
ISBN 13: 9780128054314
Language: EN, FR, DE, ES & NL

Predictive Modeling of Drug Sensitivity Book Review:

Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research challenges. It covers the major mathematical techniques used for modeling drug sensitivity, and includes the requisite biological knowledge to guide a user to apply the mathematical tools in different biological scenarios. This book is an ideal reference for computer scientists, engineers, computational biologists, and mathematicians who want to understand and apply multiple approaches and methods to drug sensitivity modeling. The reader will learn a broad range of mathematical and computational techniques applied to the modeling of drug sensitivity, biological concepts, and measurement techniques crucial to drug sensitivity modeling, how to design a combination of drugs under different constraints, and the applications of drug sensitivity prediction methodologies. Applies mathematical and computational approaches to biological problems Covers all aspects of drug sensitivity modeling, starting from initial data generation to final experimental validation Includes the latest results on drug sensitivity modeling that is based on updated research findings Provides information on existing data and software resources for applying the mathematical and computational tools available

Pacific Symposium on Biocomputing

Pacific Symposium on Biocomputing
Author: Russ B. Altman,A. Keith Dunker,Lawrence Hunter,Tiffany Murray,Marylyn D. Ritchie
Publsiher: World Scientific Publishing Company Incorporated
Total Pages: 426
Release: 2013-11-19
ISBN 10: 9789814596343
ISBN 13: 9814596345
Language: EN, FR, DE, ES & NL

Pacific Symposium on Biocomputing Book Review:

Cancer panomics: Computational methods and infrastructure for integrative analysis of cancer high-throughput "OMICS" data. Session introduction / Soren Brunak ... [et al.] -- Tumor haplotype assembly algorithms for cancer genomics / Derek Aguiar, Wendy S.W. Wong, Sorin Istrail -- Extracting significant sample-specific cancer mutations using their protein interactions / Liviu Badea -- The stream algorithm: Computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity / Elias Chaibub Neto ... [et al.] -- Sharing information to reconstruct patient-specific pathways in heterogeneous diseases / Anthony Gitter ... [et al.] -- Detecting statistical interaction between somatic mutational events and germline variation from next-generation sequence data / Hao Hu, Chad D. Huff -- Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data / In Sock Jang ... [et al.] -- Integrative analysis of two cell lines derived from a non-small-lung cancer patient - A panomics approach / Oleg Mayba ... [et al.] -- An integrated approach to blood-based cancer diagnosis and biomarker discovery / Martin Renqiang Min ... [et al.] -- Multiplex meta-analysis of medulloblastoma expression studies with external controls / Alexander A. Morgan ... [et al.] -- Computational approaches to drug repurposing and pharmacology. Session introduction / S. Joshua Swamidass ... [et al.] -- Challenges in secondary analysis of high throughput screening data / Aurora S. Blucher, Shannon K. McWeeney -- Drug intervention response predictions with paradigm (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance / Douglas Brubaker ... [et al.] -- Anti-infectious drug repurposing using an integrated chemical genomics and structural systems biology approach / Clara Ng ... [et al.] -- Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data / Fan Yang, Jinbo Xu, Jianyang Zeng -- Prediction of off-target drug effects through data fusion / Emmanuel R. Yera, Ann E. Cleves, Ajay N. Jain -- Exploring the pharmacogenomics knowledge base (PharmGKB) for repositioning breast cancer drugs by leveraging web ontology language (OWL) and cheminformatics approaches / Qian Zhu ... [et al.] -- Detecting and characterizing pleiotropy: New methods for uncovering the connection between the complexity of genomic architecture and multiple phenotypes. Session introduction / Anna L. Tyler, Dana C. Crawford, Sarah A. Pendergrass -- Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene / Christian Darabos, Samantha H. Harmon, Jason H. Moore -- Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield personalized medicine research project biobank / Molly A. Hall ... [et al.] -- Dissection of complex gene expression using the combined analysis of pleiotropy and epistasis / Vivek M. Philip, Anna L. Tyler, Gregory W. Carter -- Personalized medicine: From genotypes and molecular phenotypes towards therapy. Session introduction / Jennifer Listgarten ... [et al.] -- PATH-SCAN: A reporting tool for identifying clinically actionable variants / Roxana Daneshjou ... [et al.] -- Imputation-based assessment of next generation rare exome variant arrays / Alicia R. Martin ... [et al.] -- Utilization of an EMR-biorepository to identify the genetic predictors of calcineurin-inhibitor toxicity in heart transplant recipients/ Matthew Oetjens ... [et al.] -- Robust reverse engineering of dynamic gene networks under sample size heterogeneity / Ankur P. Parikh, Wei Wu, Eric P. Xing -- Variant priorization and analysis incorporating problematic regions of the genome / Anil Patwardhan ... [et al.] -- Bags of words models of epitope sets: HIV viral load regression with counting grids / Alessandro Perina, Pietro Lovato, Nebojsa Jojic -- Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning / Shandian Zhe ... [et al.] -- Text and data mining for biomedical discover. Session introduction / Graciela H. Gonzalez ... [et al.] -- Vector quantization kernels for the classification of protein sequences and structures / Wyatt T. Clark, Predrag Radivojac -- Combining Heterogenous data for prediction of disease related and pharmacogenes / Christopher S. Funk, Lawrence E. Hunter, K. Bretonnel Cohen -- A novel profile biomarker diagnosis for mass spectral proteomics / Henry Han -- Towards pathway curation through literature mining - A case study using PharmGKB / Ravikumar K.E., Kavishwar B. Wagholikar, Hongfang Liu -- Sparse generalized functional linear model for predicting remission status of depression patients / Yashu Liu ... [et al.] -- Development of a data-mining algorithm to identify ages at reproductive milestones in electronic medical records / Jennifer Malinowski, Eric Farber-Eger, Dana C. Crawford -- An efficient algorithm to integrate network and attribute data for gene function prediction / Shankar Vembu, Quaid Morris -- Matrix factorization-based data fusion for gene function prediction in Baker's yeast and slime mold / Marinka Zitnik, Blaz Zupan -- Workshops. Applications of bioinformatics to non-coding RNAs in the era of next-generation sequencing / Chao Cheng, Jason Moore, Casey Greene -- Building the next generation of quantitative biologists / Kristine A. Pattin ... [et al.] -- Uncovering the etiology of autism spectrum disorders: Genomics, bioinformatics, environment, data collection and exploration, and future possibilities / Sarah A. Pendergrass, Santhosh Girirajan, Scott Selleck

Assessment of Modeling Strategies for Drug Response Prediction in Cell Lines and Xenografts

Assessment of Modeling Strategies for Drug Response Prediction in Cell Lines and Xenografts
Author: Roman Kurilov
Publsiher: Unknown
Total Pages: 135
Release: 2019*
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1135073066
Language: EN, FR, DE, ES & NL

Assessment of Modeling Strategies for Drug Response Prediction in Cell Lines and Xenografts Book Review:

Abstract: Despite significant progress in cancer research, effective cancer treatment is still a challenge. Cancer treatment approaches are shifting from standard cytotoxic chemotherapy regimens towards a precision oncology paradigm, where a choice of treatment is personalized, i.e. based on a tumor's molecular features. In order to match tumor molecular features with therapeutics we need to identify biomarkers of response and build predictive models. Recent growth of large-scale pharmacogenomics resources which combine drug sensitivity and multi-omics information on a large number of samples provides necessary data for biomarker identification and drug response modelling. However, although many efforts of using this information for drug response prediction have been made, our ability to accurately predict drug response using genetic data remains limited. In this work we used pharmacogenomics data from the largest publicly available studies in order to systematically assess various aspects of the drug response model-building process with the ultimate goal of improving prediction accuracy. We applied several machine learning methods (regularized regression, support vector machines, random forest) for predicting response to a number of drugs. We found that while accuracy of response prediction varies across drugs (in most of the cases R2 values vary between 0.1 and 0.3), different machine learning algorithms applied for the the same drug have similar prediction performance. Experiments with a range of different training sets for the same drug showed that predictive power of a model depends on the type of molecular data, the selected drug response metric, and the size of the training set. It depends less on number of features selected for modelling and on class imbalance in training set. We also implemented and tested two methods for improving consistency for pharmacogenomics data coming from different datasets. We tested our ability to correctly predict response in xenografts and patients using models trained on cell lines. Only in a fraction of the tested cases we managed to get reasonably accurate predictions, particularly in case of response to erlotinib in the NSCLC xenograft cohort, and in cases of responses to erlotinib and docetaxel in the NSCLC and BRCA patient cohorts respectively. This work also includes two applied pharmacogenomics analyses. The first is an analysis of a drug-sensitivity screen performed on a panel of Burkitt cell lines. This combines unsupervised data exploration with supervised modelling. The second is an analysis of drug-sensitivity data for the DKFZ-608 compound and the generation of the corresponding response prediction model. In summary, we applied machine learning techniques to available high-throughput pharmacogenomics data to study the determinants of accurate drug response prediction. Our results can help to draft guidelines for building accurate models for personalized drug response prediction and therefore contribute to advancing of precision oncology.

Cancer Bioinformatics

Cancer Bioinformatics
Author: Alexander Krasnitz
Publsiher: Unknown
Total Pages: 280
Release: 2018
ISBN 10: 9781493988686
ISBN 13: 1493988689
Language: EN, FR, DE, ES & NL

Cancer Bioinformatics Book Review:

Biomarkers in Drug Discovery and Development

Biomarkers in Drug Discovery and Development
Author: Ramin Rahbari,Jonathan Van Niewaal,Michael R. Bleavins
Publsiher: John Wiley & Sons
Total Pages: 608
Release: 2020-03-17
ISBN 10: 1119187508
ISBN 13: 9781119187509
Language: EN, FR, DE, ES & NL

Biomarkers in Drug Discovery and Development Book Review:

This book continues the legacy of a well-established reference within the pharmaceutical industry – providing perspective, covering recent developments in technologies that have enabled the expanded use of biomarkers, and discussing biomarker characterization and validation and applications throughout drug discovery and development. • Explains where proper use of biomarkers can substantively impact drug development timelines and costs, enable selection of better compounds and reduce late stage attrition, and facilitate personalized medicine • Helps readers get a better understanding of biomarkers and how to use them, for example which are accepted by regulators and which still non-validated and exploratory • Updates developments in genomic sequencing, and application of large data sets into pre-clinical and clinical testing; and adds new material on data mining, economics, and decision making, personal genetic tools, and wearable monitoring • Includes case studies of biomarkers that have helped and hindered decision making • Reviews of the first edition: “If you are interested in biomarkers, and it is difficult to imagine anyone reading this who wouldn't be, then this book is for you." (ISSX) and “…provides a good introduction for those new to the area, and yet it can also serve as a detailed reference manual for those practically involved in biomarker implementation.” (ChemMedChem)

Personalized Medicine

Personalized Medicine
Author: Bo-Juen Chen
Publsiher: Unknown
Total Pages: 135
Release: 2013
ISBN 10: 1928374650XXX
ISBN 13: OCLC:884490264
Language: EN, FR, DE, ES & NL

Personalized Medicine Book Review:

In order to account for this, I propose a method to associate contextual genomic features with drug sensitivity. The algorithm is based on information theory, Bayesian statistics, and transfer learning. The algorithm demonstrates the importance of context specificity in predictive modeling of cancer pharmacogenomics. The two complementary algorithms highlight the challenges faced in personalized medicine and the potential solutions. This thesis detailed the results and analysis that demonstrate the importance of causality and context specificity in predictive modeling of drug response, which will be crucial for us towards bringing personalized medicine in practice.

Integration of Multisource Heterogenous Omics Information in Cancer

Integration of Multisource Heterogenous Omics Information in Cancer
Author: Victor Jin,Junbai Wang,Binhua Tang
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2020-01-30
ISBN 10: 2889634485
ISBN 13: 9782889634484
Language: EN, FR, DE, ES & NL

Integration of Multisource Heterogenous Omics Information in Cancer Book Review:

Multisource heterogenous omics data can provide unprecedented perspectives and insights into cancer studies, but also pose great analytical problems for researchers due to the vast amount of data produced. This Research Topic aims to provide a forum for sharing ideas, tools and results among researchers from various computational cancer biology fields such as genetic/epigenetic and genome-wide studies.

Target Discovery for Anticancer Therapy Facilitated by Artificial Intelligence

Target Discovery for Anticancer Therapy Facilitated by Artificial Intelligence
Author: Feng Zhu,Yu Zong Chen,Weiwei Xue
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2021-08-19
ISBN 10: 2889712001
ISBN 13: 9782889712007
Language: EN, FR, DE, ES & NL

Target Discovery for Anticancer Therapy Facilitated by Artificial Intelligence Book Review:

AACR 2019 Proceedings Abstracts 2749 5314

AACR 2019 Proceedings  Abstracts 2749 5314
Author: American Association for Cancer Research
Publsiher: Coe Truman International, LLC
Total Pages: 135
Release: 2019-03-08
ISBN 10: 0463372727
ISBN 13: 9780463372722
Language: EN, FR, DE, ES & NL

AACR 2019 Proceedings Abstracts 2749 5314 Book Review:

American Association for Cancer Research 2019 Proceedings: Abstracts 1-2748 - Part B

Holland Frei Cancer Medicine 8

Holland Frei Cancer Medicine 8
Author: Robert C. Bast Jr,James F. Holland
Publsiher: PMPH-USA
Total Pages: 2052
Release: 2010
ISBN 10: 9781607950141
ISBN 13: 1607950146
Language: EN, FR, DE, ES & NL

Holland Frei Cancer Medicine 8 Book Review:

Holland Frei Cancer Medicine serves as a quick reference to current information on an extensive list of cancers, including breast, lung, thyroid, colorectal, ovarian, prostate, and gastric cancer, to name but a few. Presented as an accessible pocket-sized handbook, the chapters are organized in an outline format, offering only the most essential information on the etiology, staging (including TNM staging) and treatment for each cancer type. Individual chapters are devoted to the molecular biology of cancer, cancer prevention, cancer screening, the mechanisms of chemotherapy, and diagnostic imaging in cancer. Additionally, each chapter lists all the major phase III clinical trials, and therefore, serves as an excellent reference of the major randomized controlled trials for each cancer reported to date. Specific chapters are also dedicated to the discussion of oncologic emergencies, pain and palliation, and prescription complications. At the conclusion of the book, a glossary of oncologic terms and chemotherapeutic drug programs, a table of common cancer incidences, and an overview of the mechanisms, common uses, and related toxicities of various anti-cancer agents are featured. In addition, performance status tables, mathematical formulas and a listing of common biomedical / cancer web sites are highlighted.

Artificial Intelligence in Oncology Drug Discovery and Development

Artificial Intelligence in Oncology Drug Discovery and Development
Author: John Cassidy,Belle Taylor
Publsiher: BoD – Books on Demand
Total Pages: 192
Release: 2020-09-09
ISBN 10: 1789846897
ISBN 13: 9781789846898
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Oncology Drug Discovery and Development Book Review:

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence.

Computational Intelligence Methods for Bioinformatics and Biostatistics

Computational Intelligence Methods for Bioinformatics and Biostatistics
Author: Paolo Cazzaniga,Daniela Besozzi,Ivan Merelli,Luca Manzoni
Publsiher: Springer Nature
Total Pages: 350
Release: 2020-12-09
ISBN 10: 3030630617
ISBN 13: 9783030630614
Language: EN, FR, DE, ES & NL

Computational Intelligence Methods for Bioinformatics and Biostatistics Book Review:

This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019. The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine.

Intelligent Computing Theories and Methodologies

Intelligent Computing Theories and Methodologies
Author: De-Shuang Huang,Kang-Hyun Jo,Abir Hussain
Publsiher: Springer
Total Pages: 755
Release: 2015-08-10
ISBN 10: 3319221868
ISBN 13: 9783319221861
Language: EN, FR, DE, ES & NL

Intelligent Computing Theories and Methodologies Book Review:

This two-volume set LNCS 9225 and LNCS 9226 constitutes - in conjunction with the volume LNAI 9227 - the refereed proceedings of the 11th International Conference on Intelligent Computing, ICIC 2015, held in Fuzhou, China, in August 2015. The total of 191 full and 42 short papers presented in the three ICIC 2015 volumes was carefully reviewed and selected from 671 submissions. The papers are organized in topical sections such as evolutionary computation and learning; compressed sensing, sparse coding and social computing; neural networks, nature inspired computing and optimization; pattern recognition and signal processing; image processing; biomedical informatics theory and methods; differential evolution, particle swarm optimization and niche technology; intelligent computing and knowledge discovery and data mining; soft computing and machine learning; computational biology, protein structure and function prediction; genetic algorithms; artificial bee colony algorithms; swarm intelligence and optimization; social computing; information security; virtual reality and human-computer interaction; healthcare informatics theory and methods; unsupervised learning; collective intelligence; intelligent computing in robotics; intelligent computing in communication networks; intelligent control and automation; intelligent data analysis and prediction; gene expression array analysis; gene regulation modeling and analysis; protein-protein interaction prediction; biology inspired computing and optimization; analysis and visualization of large biological data sets; motif detection; biomarker discovery; modeling; simulation; and optimization of biological systems; biomedical data modeling and mining; intelligent computing in biomedical signal/image analysis; intelligent computing in brain imaging; neuroinformatics; cheminformatics; intelligent computing in computational biology; computational genomics; special session on biomedical data integration and mining in the era of big data; special session on big data analytics; special session on artificial intelligence for ambient assisted living; and special session on swarm intelligence with discrete dynamics.

Latest Research into Quality Control

Latest Research into Quality Control
Author: Isin Akyar
Publsiher: BoD – Books on Demand
Total Pages: 516
Release: 2012-12-12
ISBN 10: 9535108689
ISBN 13: 9789535108689
Language: EN, FR, DE, ES & NL

Latest Research into Quality Control Book Review:

Quality control has an emerging importance in every field of life. Quality control is a process that is used to guarantee a certain level of quality in a product or service. It might include whatever actions a business deems necessary to provide for the control and verification of certain characteristics of a product or service. With the improvement of technology everyday we meet new and complicated devices and methods in different fields. Quality control should be performed in all of those new techniques. In this book "Latest Research Into Quality Control" our aim was to collect information about quality control in many different fields. The aim of this book is to share useful and practical knowledge about quality control in several fields with the people who want to improve their knowledge.

Machine Intelligence and Smart Systems

Machine Intelligence and Smart Systems
Author: Shikha Agrawal,Kamlesh Kumar Gupta,Jonathan H. Chan,Jitendra Agrawal,Manish Gupta
Publsiher: Springer Nature
Total Pages: 562
Release: 2022-05-23
ISBN 10: 9811696500
ISBN 13: 9789811696503
Language: EN, FR, DE, ES & NL

Machine Intelligence and Smart Systems Book Review:

This book is a collection of peer-reviewed best selected research papers presented at the Second International Conference on Machine Intelligence and Smart Systems (MISS 2021), organized during September 24–25, 2021, in Gwalior, India. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation. Scientists, engineers, academicians, technology developers, researchers, students and government officials will find this book useful in handling their complicated real-world issues by using machine intelligence methodologies.

Methods in Computational Biology

Methods in Computational Biology
Author: Ross Carlson,Herbert Sauro
Publsiher: MDPI
Total Pages: 214
Release: 2019-07-03
ISBN 10: 3039211633
ISBN 13: 9783039211630
Language: EN, FR, DE, ES & NL

Methods in Computational Biology Book Review:

Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections: • Reviews of Computational Methods • Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels • The Interface of Biotic and Abiotic Processes • Processing of Large Data Sets for Enhanced Analysis • Parameter Optimization and Measurement

Artificial Intelligence for Translational Pharmacology

Artificial Intelligence for Translational Pharmacology
Author: Zhi-Liang Ji,Lixia Yao,Kartick Chandra Pramanik,Zhaohui John Cai
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2020-07-28
ISBN 10: 288963888X
ISBN 13: 9782889638888
Language: EN, FR, DE, ES & NL

Artificial Intelligence for Translational Pharmacology Book Review:

Multi omic Data Integration in Oncology

Multi omic Data Integration in Oncology
Author: Chiara Romualdi,Enrica Calura,Davide Risso,Sampsa Hautaniemi,Francesca Finotello
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2020-12-03
ISBN 10: 2889661512
ISBN 13: 9782889661510
Language: EN, FR, DE, ES & NL

Multi omic Data Integration in Oncology Book Review:

Applied Chemoinformatics

Applied Chemoinformatics
Author: Thomas Engel,Johann Gasteiger
Publsiher: John Wiley & Sons
Total Pages: 416
Release: 2018-04-19
ISBN 10: 3527806520
ISBN 13: 9783527806522
Language: EN, FR, DE, ES & NL

Applied Chemoinformatics Book Review:

Edited by world-famous pioneers in chemoinformatics, this is a clearly structured and applications-oriented approach to the topic, providing up-to-date and focused information on the wide range of applications in this exciting field. The authors explain methods and software tools, such that the reader will not only learn the basics but also how to use the different software packages available. Experts describe applications in such different fields as structure-spectra correlations, virtual screening, prediction of active sites, library design, the prediction of the properties of chemicals, the development of new cosmetics products, quality control in food, the design of new materials with improved properties, toxicity modeling, assessment of the risk of chemicals, and the control of chemical processes. The book is aimed at advanced students as well as lectures but also at scientists that want to learn how chemoinformatics could assist them in solving their daily scientific tasks. Together with the corresponding textbook Chemoinformatics - Basic Concepts and Methods (ISBN 9783527331093) on the fundamentals of chemoinformatics readers will have a comprehensive overview of the field.