Accomplishments and challenges in bioinformatics

A new IT discipline - bioinformatics - fuses computing, mathematics and biology to meet the many computational challenges in modern molecular biology and medical research. The two major themes in bioinformatics - data management and knowledge discovery - rely on effectively adopting techniques developed in IT for biological data, with IT scientists playing an essential role. The future of molecular biology and biomedicine will greatly depend on advances in informatics. As we review researchers' many achievements in bioinformatics, we're confident that the marriage between molecular biology and information technology is a happy one. Accomplishments in bioinformatics have advanced molecular biology and information technology. Although many computational challenges lie ahead, more fruitful outcomes of this successful multidisciplinary marriage are likely.     

Textual data compression in computational biology: Algorithmic techniques

In a recent review [R. Giancarlo, D. Scaturro, F. Utro, Textual data compression in computational biology: a synopsis, Bioinformatics 25 (2009) 1575–1586] the first systematic organization and presentation of the impact of textual data compression for the analysis of biological data has been given. Its main focus was on a systematic presentation of the key areas of bioinformatics and computational biology where compression has been used together with a technical presentation of how well-known notions from information theory have been adapted to successfully work on biological data. Rather surprisingly, the use of data compression is pervasive in computational biology. Starting from that one, the focus of this companion review is on the computational methods involved in the use of data compression in computational biology. Indeed, although one would expect ad hoc adaptation of compression techniques to work on biological data, unifying and homogeneous algorithmic approaches are emerging. Moreover, given that experiments based on parallel sequencing are the future for biological research, data compression techniques are among a handful of candidates that seem able, successfully, to deal with the deluge of sequence data they produce; although, until now, only in terms of storage and indexing, with the analysis still being a challenge. Therefore, the two reviews, complementing each other, are perceived to be a useful starting point for computer scientists to get acquainted with many of the computational challenges coming from computational biology in which core ideas of the information sciences are already having a substantial impact.     

Pattern recognition techniques for the emerging field of bioinformatics: A review

The emerging field of bioinformatics has recently created much interest in the computer science and engineering communities. With the wealth of sequence data in many public online databases and the huge amount of data generated from the Human Genome Project, computer analysis has become indispensable. This calls for novel algorithms and opens up new areas of applications for many pattern recognition techniques. In this article, we review two major avenues of research in bioinformatics, namely DNA sequence analysis and DNA microarray data analysis. In DNA sequence analysis, we focus on the topics of sequence comparison and gene recognition. For DNA microarray data analysis, we discuss key issues such as image analysis for gene expression data extraction, data pre-processing, clustering analysis for pattern discovery and gene expression time series data analysis. We describe current methods and show how computational techniques could be useful in these areas. It is our hope that this review article could demonstrate how the pattern recognition community could have an impact on the fascinating and challenging area of genomic research.     

Spectral distortion measures for biological sequence comparisons and database searching

In bioinformatics and computational biology, methods for biological sequence comparison play the most important role for the interpretation of complex nucleotide and protein data such as the inference of relationships between genes, proteins and species; and the discovery of novel protein structures and functions. This type of inference is derived by sequence similarity matching on the databases of biological sequences. As many entire genomes have being determined at a rapid rate, computational methods for comparing genomic and protein sequences will be more essential for probing the complexity of genes, genomes, and molecular machines. In this paper we introduce a pattern-comparison algorithm, which is based on the mathematical concepts of linear predictive coding and its cepstral-distortion measures for the analyses of both DNA and protein sequences. The results obtained from several experiments on real datasets have shown the effectiveness of the proposed approach.     

Clustering of high throughput gene expression data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics – clustering gene expression data – to the operations research community.     

The emerging landscape of bioinformatics software systems

Fusing computing and biology expertise, bioinformatics software provides a powerful tool for organizing and mining the vast amounts of data genetics researchers are accumulating. As life scientists and computational scientists interact to create useful bioinformatics software systems, several themes or lessons recur. We identify seven themes: the nature of biological data; data storage, analysis and retrieval; computational modeling and simulation; biologically meaningful information integration; data mining; image processing and visualization; and closing the loop     

Applications of Fuzzy Logic in Bioinformatics (Dong Xu et al.; 2008) [Book review]

This book is entirely focused on explaining how fuzzy concepts and approaches can be useful in bioinformatics. It is organized into seven chapters and two very useful appendices. Some of the topics covered include: the fundamental concepts of fuzzy set theory and fuzzy logic; specific examples of using fuzzy set theory and fuzzy logic to address bioinformatics analysis and modeling of data; analysis of microarray data; and future applications and directions for fuzzy computational approaches in molecular biology. Appendix I explains the fundamental biological concepts that are relevant to the biological subjects covered in the book and Appendix II lists and describes a wide variety of free online resources related to molecular biology, bioinformatics, and fuzzy set theory.Researchers in bioinformatics and fuzzy set theory and fuzzy logic will find this book to be an important resource for developing interdisciplinary research between the two fields. Educators can also use this book for a graduate course to both introduce the two fields and stimulate research ideas based on the currently active research topics presented.     

生物文本中蛋白质名称的识别*

随着基因和蛋白质序列的发布和分子生物学研究的发展,其相关的数据呈指数级增长,因此如何从海量的相关文献中直接获取生物学家研究领域的相关信息变得迫在眉睫,识别生物文献中的命名实体如蛋白质、基因、脱氧核糖核酸名称等成为生物信息学中信息抽取的最基本任务。介绍了国际同类研究中生物命名实体识别的各种方法,重点介绍了蛋白质名称识别的相关方法、所用资源、实验结果及与国际同类研究的比较结果。     

自组装DNA计算的研究进展及展望

近年来,许多研究者已经证明二维自组装模型有通用计算能力,同时证明了自组装DNA计算具有可扩展性。随着分子生物学技术的发展,自组装DNA计算有着广阔的应用前景,在纳米科学、优化计算、密码学、医学等众多科学领域中有突破性的创新与应用。较全面地介绍了自组装DNA计算的研究现状、原理、分子结构和数学模型,以及自组装DNA计算的复杂度和误差分析,并对自组装DNA计算待研究的问题和发展前景进行了分析和展望。     

PRIME: A Mass Spectrum Data Mining Tool for <Emphasis Type="Italic">De Nova</Emphasis> Sequencing and PTMs Identification

De novo sequencing is one of the most promising proteomics techniques for identification of protein posttranslation modifications (PTMs) in studying protein regulations and functions. We have developed a computer tool PRIME for identification of b and y ions in tandem mass spectra, a key challenging problem in de novo sequencing. PRIME utilizes a feature that ions of the same and different types follow different mass-difference distributions to separate b from y ions correctly. We have formulated the problem as a graph partition problem. A linear integer-programming algorithm has been implemented to solve the graph partition problem rigorously and efficiently. The performance of PRIME has been demonstrated on a large amount of simulated tandem mass spectra derived from Yeast genome and its power of detecting PTMs has been tested on 216 simulated phosphopeptides.     

Introduction: Knowledge Discovery in High-Throughput Biological Domains

The sequencing of the human genome was a major step in understanding the ways in which we are wired. Although an important milestone, this genetic blueprint provides only a ``parts list"; it does not offer any information about how the human organism is actually working, and it gives little insight into the function or interactions among the approximately thirty thousand constitutive parts that comprise our genome. To date, research in molecular biology had resulted in annotating only a small percentage (around 10%) of the gene set, and even less is known about proteins. Because of the quantity of information being generated, we increasingly rely on computational techniques to provide insight into the genome, proteome, and interactome data. Robotics and computational biology are rapidly changing the way we formulate and test biological hypotheses. Advances in gene expression profiling by microarrays and protein profiling by mass spectrometry have suggested the potential to simultaneously view all genes expressed, all subsequent protein products, and all the interacting partners of each individual protein within a biological system. We can rapidly and accurately measure the relative activity of genes and proteins in normal and diseased tissue. Diverse computational techniques have been applied to solve biological and medical problems over the years. Increasingly, such systems face challenges that arise from the enormous increase in information complexity and volume in these domains. In addition, the pace of evolution of our understanding of underlying principles requires continuous updates to existing databases, as well as systems that support reasoning and knowledge discovery. Performing these changes manually is becoming the bottleneck of the successful application of computer science to biological and medical domains.     

Constraint Programming in Structural Bioinformatics

Bioinformatics aims at applying computer science methods to the wealth of data collected in a variety of experiments in life sciences (e.g. cell and molecular biology, biochemistry, medicine, etc.) in order to help analysing such data and eliciting new knowledge from it. In addition to string processing bioinformatics is often identified with machine learning used for mining the large banks of bio-data available in electronic format, namely in a number of web servers. Nevertheless, there are opportunities of applying other computational techniques in some bioinformatics applications. In this paper, we report the application of constraint programming to address two structural bioinformatics problems, protein structure prediction and protein interaction (docking). The efficient application of constraint programming requires innovative modelling of these problems, as well as the development of advanced propagation techniques (e.g. global reasoning and propagation), which were adopted in Chemera, a system that is currently used to support biochemists in their research.     

A random walk down the genomes: DNA evolution in Valis

The authors propose a new software system that incorporates biological data and domain-specific knowledge and show how biologists can use it to model, analyze, and experiment with genomic evolutionary processes. A better understanding of biology will come through information-theoretic studies of genomes that provide insights into DNA's role in governing metabolic and regulatory pathways. Understanding the evolutionary processes that act on these "codes of life" requires the ability to analyze vast amounts of continually generated genomic data. Researchers in the emerging bioinformatics discipline require more complex mechanisms to investigate the full ensemble of available biological facts. To meet this challenge, New York University's Bioinformatics Group is creating a computational environment called Valis, the vast active living intelligent system. Valis is designed to solve the immediate genomic and proteomic problems that the biological community currently faces, while remaining flexible enough to adapt to the maturing bioinformatics field     

蛋白质相互作用网络功能模块检测的研究综述

蛋白质相互作用（Protein-protein interaction,PPI）网络是生命活动中一种极其重要的生物分子关系网络,利用计算方法从PPI网络中检测功能模块是目前生物信息学中一项重要的研究课题. 本文首先总结了功能模块检测过程的基本流程,说明了预处理和后处理的作用;其次,提出了一种模块检测方法的分类体系,并对其中一些代表性的检测算法进行了阐述;再次,给出了模块检测常用的数据库、评价指标和相关软件工具,并通过实验对代表性算法进行了性能对比. 最后,通过对该领域挑战性问题的分析预测了模块检测未来的研究方向,以期对相关研究提供一定的参考.     

Preface

It is our great honor to announce the publication of this special section on AI and big data analytics in biology and medicine in the Journal of Computing Science and Technology (JCST). As more and more modern biological and medical data are produced,artificial intelligence (AI) and big data analytics are playing an increasingly important role in helping to draw meaningful and logical conclusions about biology and medicine.Understanding biological and medical data will help answer important life questions on Earth,find solutions to global health problems,and even help solve tough problems such as drug design and disease diagnosis.The information obtained from biology and medicine is not only very detailed,but also has unique properties such as low quality data,big data sizes,different complex formats,high dimensions,many duplications and much noise,and so on.They all require special skills or unique tools for analysis and interpretation.Thus,a lot of studies using AI and big data analytics on biological and medical data are becoming very popular and hot topics in the computer science research field.     

Intelligent systems in biology: why the excitement?

Biology has rapidly become a data-rich, information-hungry science because of recent massive data generation technologies. Our biological colleagues are designing more clever and informative experiments because of recent advances in molecular science. These experiments and data hold the key to the deepest secrets of biology and medicine, but we cannot fully analyze this data due to the wealth and complexity of the information available. The result is a great need for intelligent systems in biology. There are many opportunities for intelligent systems to help produce knowledge in biology and medicine. Intelligent systems probably helped design the last drug your doctor prescribed, and they were probably involved in some aspect of the last medical care you received. Intelligent computational analysis of the human genome will drive medicine for at least the next half-century. Intelligent systems are working on gene expression data to help understand genetic regulation and ultimately the regulated control of all life processes including cancer, regeneration, and aging. Knowledge bases of metabolic pathways and other biological networks make inferences in systems biology that, for example, let a pharmaceutical program target a pathogen pathway that does not exist in humans, resulting in fewer side effects to patients. Modern intelligent analysis of biological sequences produces the most accurate picture of evolution ever achieved. Knowledge-based empirical approaches currently are the most successful method known for general protein structure prediction. Intelligent literature-access systems exploit a knowledge flow exceeding half a million biomedical articles per year. Machine learning systems exploit heterogenous online databases whose exponential growth mimics Moore's law.     

Challenges for intelligent systems in biology

Biological processes have produced the ultimate intelligent system (humans), and now we are trying to understand biology (and ourselves) by building intelligent systems. Intelligent systems research in biology strives to understand how living systems perform difficult tasks routinely (ranging from molecular phenomena such as protein-folding to organism-level phenomena such as cognition). The definition of intelligent systems in biology can lead to hours of debate. Some say that all high-performance systems that do something difficult with (or to) biological data should be considered intelligent systems. Others insist that the term intelligent system should be reserved for systems using the methods typically associated with modem AI.     

Grid as a bioinformatic tool

The grid is a promising tool to resolve the crucial issue of software and data integration in biology. In this paper, we have reported on our experience in the deployment of bioinformatic grid applications within the framework of the DataGrid project. These applications inquired the potential impact of grids for CPU demanding algorithms and bioinformatics web portals and for the update and distribution of biological databases.Grid computing tests showed how resources sharing improves the current practice of bioinformatics. Reached performance demonstrated the interest of the grid tool.