edited by Tao Jiang (University of California, Riverside, USA), Ueng-Cheng Yang (National Yang-Ming University, Taiwan), Yi-Ping Phoebe Chen (Deakin University, Australia), & Limsoon Wong (National University of Singapore, Singapore)

High-throughput sequencing and functional genomics technologies have given us a draft human genome sequence and have enabled large-scale genotyping and gene expression profiling of human populations. Databases containing large numbers of sequences, polymorphisms, and gene expression profiles of normal and diseased tissues in different clinical states are rapidly being generated for human and model organisms. Bioinformatics is thus rapidly growing in importance in the annotation of genomic sequences, in the understanding of the interplay between genes and proteins, in the analysis of the genetic variability of species, and so on. This proceedings contains an up-to-date exchange of knowledge, ideas, and solutions to conceptual and practical issues of bioinformatics, by researchers, professionals, and industrial practitioners at the 4th Asia-Pacific Bioinformatics Conference held in Taipei in February 2006.

• Accuracy of Four Heuristics for the Full Sibship Reconstruction Problem in the Presence of Genotype Errors (D A Konovalov)
• Predicting Ranked SCOP Domains by Mining Asociations of Visual Contents in Distance Matrices (P-H Chi & C-R Shyu)
• An Efficient Algorithm for String Motif Discovery (F Y L Chin & H C M Leung)
• On the Complexity of Finding Control Strategies for Boolean Networks (T Akutsu et al.)
• Microarray Missing Value Imputation by Iterated Local Least Squares (Z Cai et al.)
• Techniques for Assessing Phylogenetic Branch Support: A Performance Study (D Ruths & L Nakhleh)
• Identification of Over-Represented Combinations of Transcription Factor Binding Sites in Sets of Co-Expressed Genes (S-S Huang et al.)
• A Knowledge-Based Approach to Protein Local Structure Prediction (C-T Chen et al.)
• Resolving the Gene Tree and Species Tree Problem by Phylogenetic Mining (X Han)
• Gene Expression Data Clustering Based on Local Similarity Combination (D Pan & F Wang)
• and other papers