Methods for detecting protein-protein interactions (PPIs) have given researchers a global picture of protein interactions on a genomic scale. ""Biological Data Mining in Protein Interaction Networks"" explains bioinformatic methods for predicting PPIs, as well as data mining methods to mine or analyze various protein interaction networks. A defining body of research within the field, this book discovers underlying interaction mechanisms by studying intra-molecular features that form the common denominator of various PPIs.

Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.

"The data economy" is a term used by many, but properly understood by few. Even more so the concept of "big data". Both terms embody the notion of a digital world in which many transactions and data flows animate a virtual space. This is the unseen world in which technology has become the master, with the hand of the human less visible. In fact, however, it is human interaction in and around technology that makes data so pervasive and important - the ability of the human mind to extract, manipulate and shape data that gives meaning to it. This book outlines the findings and conclusions of a multidisciplinary team of data scientists, lawyers, and economists tasked with studying both the possibilities of exploiting the rich data sets made available from many human-technology interactions and the practical and legal limitations of trying to do so. It revolves around a core case study of Singapore's public transport system, using data from both the private company operating the contactless payment system (EZ-Link) and the government agency responsible for public transport infrastructure (Land Transport Authority). In analysing both the possibilities and the limitations of these data sets, the authors propose policy recommendations in terms of both the uses of large data sets and the legislation necessary to enable these uses while protecting the privacy of users.

This volume contains papers presented at the 19th International Conference on Genome Informatics (GIW 2008) held at the Marriott Surfers Paradise Resort, Gold Coast, Queensland, Australia from December 1 to 3, 2008. The GIW Series provides an international forum for the presentation and discussion of original research papers on all aspects of bioinformatics, computational biology and systems biology. Its scope includes biological sequence analysis, protein structure prediction, genetic regulatory networks, bioinformatic algorithms, comparative genomics, and biomolecular data integration and analysis. Boasting a history of 19 years, GIW is the longest-running international bioinformatics conference.

A total of 18 contributed papers were selected for presentation at GIW 2008 and for inclusion in this book. The selected papers come from institutions in 18 countries. In addition, this book contains abstracts from the six invited speakers: Sean Grimmond (Institute for Molecular Bioscience, The University of Queensland, Australia), Eugene V Koonin (National Center for Biotechnology Information, National Institutes of Health, USA), Ming Li (University of Waterloo, Canada), Yi-Xue Li (Chinese Academy of Sciences and Shanghai Jiaotong University, China), John Mattick (Institute for Molecular Bioscience, The University of Queensland, Australia), and Eric Schadt (Rosetta Inpharmatics, USA).

This volume contains papers presented at the 18th International Conference on Genome Informatics (GIW 2007) held at the Biopolis, Singapore from December 3 to 5, 2007. The GIW Series provides an international forum for the presentation and discussion of original research papers on all aspects of bioinformatics, computational biology and systems biology. Its scope includes biological sequence analysis, protein folding prediction, gene regulatory network, clustering algorithms, comparative genomics, and text mining. Boasting a history of 18 years, GIW is likely the longest-running international bioinformatics conference.A total of 16 papers were selected for presentation at GIW 2007 and inclusion in this book. The notable authors include Ming Li (University of Waterloo, Canada), Minoru Kanehisa (Kyoto University, Japan), Vladimir Kuznetsov (Genome Institute of Singapore), Tao Jiang (UC Riverside, USA), Christos Ouzounis (European Bioinformatics Institute, UK), and Satoru Miyano (University of Tokyo, Japan). In addition, this book contains abstracts from the five invited speakers: Frank Eisenhaber (Bioinformatics Institute, Singapore), Sir David Lane (Institute of Molecular and Cell Biology, Singapore), Hanah Margalit (The Hebrew University of Jerusalem, Israel), Lawrence Stanton (Genome Institute of Singapore), and Michael Zhang (Cold Spring Harbor Laboratory, USA).

The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.

The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.