Biological Data Management Research Articles

COMPUTATIONAL VALIDATION AND ANALYSIS OF SEMI-QUANTITATIVE DATA USING IN-SILICO APPROACHES

With the advent use of computers in every daily life the computational approacheshas collaborative effort between biologists and computer scientists and thus covers a wide variety of traditionalcomputer science domains, including data retrieval, data integration,data cleaning, data modeling, data mining, data warehousing, data managing, ontologies, simulation, parallel computing, agent-based technology, grid computing, and visualization. However, applying each of these domains to biomolecular and biomedicalapplications raises specific and unexpectedly challenging research issues. This review is to provide life scientists and computer scientists with a complete view on biological data management byidentifying specificissues, presenting existing solutions from both academia and industry and providing a framework in which to compare thesesystems.

Evaluation of the Construction of a Data Center-Driven Financial Shared Service Platform From the Remote Multimedia Network Perspective

The financial shared service (FSS) platform under the remote multimedia network perspective is a newly proposed accounting and reporting business management mode in recent years that plays a good role in the operation of enterprises. The multimedia technology in the health cloud biological authentication and data management system can be applied to the construction of the data center-driven FSS platform. This study used multimedia in the data management system to build a data center-driven FSS platform to maintain the normal operation of the enterprise. Moreover, the study analyzed from the perspective of employee satisfaction, work efficiency, enterprise cost, and work continuity and drew a conclusion. The survey of employee satisfaction showed that the satisfaction of employees after modeling had been greatly improved compared with that before modeling. Moreover, according to the survey of work efficiency, the work efficiency of employees in the five enterprises after modeling has been greatly improved compared with that before modeling. Furthermore, in the survey of enterprise cost, the cost reduction effect of postal enterprises is best after using multimedia technology to model the data center-driven FSS platform. Then, according to the survey on work continuity, the work continuity of the five enterprises has been improved to varying degrees after the establishment of the FSS platform. Among them, the work continuity of Enterprise 2 has been improved by 11%, with the best effect. The multimedia modeling in the health cloud biological authentication and data management system plays a role in promoting the development of enterprises.

Using machine learning to develop a global coral bleaching predictor

Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

GEN Interview—Overcoming Challenges in Biological Data Management

GEN Interview—Overcoming Challenges in Biological Data Management

A Bibliometric Analysis of Mexican Bioinformatics: A Portrait of Actors, Structure, and Dynamics.

Simple SummaryThis work analyzes the development of Bioinformatics research in Mexico through articles published in the last 25 years, as they have been stored in a field-specific database (PubMed). The main findings reveal that the main themes identified are strongly related to the research of outstanding researchers, the outstanding collaborations of Mexican institutions with foreign countries and institutions are influenced by the geographic proximity and binational agreements as well as philanthropic and academic programs that promote collaborations, and there is an inclination for health issues promoted by public health financing and philanthropic organizations. It is identified that publications in the field had an explosion since 2012 due to the maturity of nucleic acid sequencing technologies and the high availability of this information in public databases. Overall, this work suggests both the necessity to improve funding in this informatic discipline and promote academic collaboration inside and outside the country. Finally, the strategy used in this work allows us to make a diagnostic of the strengths and weaknesses of bioinformatics research development that can be applied in other countries. This is worth it because bioinformatics is a tool that can boost several economic sectors.Bioinformatics is a very important informatics tool for health and biological sciences, focusing on biological data management. The objective of this work was to perform a bibliometric analysis regarding the development of Mexican bioinformatics. An exhaustive revision of the literature associated with Mexican bioinformatics in a period of 25-years was performed. Bibliometric tools, such as performance analysis and science mapping were included in the analysis. We identified the main actors as well as the structure and dynamics of Mexican bioinformatics. Some of the main findings were as follows: the thematic structure in the field is defined by the research lines of outstanding authors; the outstanding collaborations of Mexican institutions with foreign countries and institutions are influenced by the geographic proximity and binational agreements, as well as philanthropic and academic programs that promote collaborations, and there is an inclination for health issues promoted by public health financing and philanthropic organizations. It is identified that publications had an explosion since 2012, we consider that this growth may be influenced by the democratization of data, derived from the mass sequencing of biological molecules stored in public databases.

ITIS and the Global Taxonomic Backbone

The Integrated Taxonomic Information System (ITIS) provides a regularly updated, global database that currently contains over 868,000 scientific names and their hierarchy. The program exists to communicate a comprehensive taxonomy of global species across 7 kingdoms that enables biodiversity information to be discovered, indexed, and connected across all human endeavors. ITIS partners with taxonomists and experts across the world to assemble scientific names and their taxonomic relationships, and then distributes that data through publicly available software. A single taxon may be represented by multiple scientific names, so ITIS makes it a priority to provide synonymy. Linking valid or accepted names with their subjective and objective synonyms is a key component of name translation and increases the precision of searches and organization of information. ITIS and its partner Species2000 create the Catalogue of Life (CoL) checklist that provides quality scientific name data for over 2.2M species. The CoL is the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Providing automated open access to complete, current, literature-referenced, and expert-validated taxonomic information enables biological data management systems, and is elemental to enhancing the utility of the amassed scientific data across the world. Fully leveraging this information for the public good is crucial for empowering the global digital society to confront the most pressing social and environmental challenges.

A Case Study of Publishing Internal APIs to External Users

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

Content-Based Image Retrieval (CBIR) in Big Histological Image Databases

Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge.
 Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management.
 Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests.
 Conclusion: This research effort significant contributions are twofold. 1) Defining a comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods.

Enabling Massive XML-Based Biological Data Management in HBase.

Publishing biological data in XML formats is attractive for organizations who would like to provide their bioinformatics resources in an extensible and machine-readable format. In the era of big data, massive XML-based biological data management is emerged as a challengeable issue. With the continuous growth of the XML-based biological data sets, it is usually frustrating to use traditional declarative query languages to provide efficient query capabilities in terms of processing speed and scale. In this study, we report a novel platform to store and query massive XML-based biological data collections. A prototype tool for constructing HBase tables from XML-based biological data collections is first developed, and then a formal approach to transform the XML query model into the MapReduce query model is proposed. Finally, an evaluation of the query performance of the proposed approach on the existing XML-based biological databases is presented, showing that the performance advantages of the proposed solution. The source code of the massive XML-based biological data management platform is freely available at https://github.com/lyotvincent/X2H.

The grant is dead, long live the data - migration as a pragmatic exit strategy for research data preservation.

Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering. PlaSMo stands for 'Plant Systems-biology Modelling' and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting until 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative, public repository with secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in a similar situation. We share our reflections on the sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies.

The grant is dead, long live the data - migration as a pragmatic exit strategy for research data preservation

Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering. PlaSMo stands for ‘Plant Systems-biology Modelling’ and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting until 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative, public repository with secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in a similar situation. We share our reflections on the sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies.

The grant is dead, long live the data - migration as a pragmatic exit strategy for research data preservation.

Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering. PlaSMo stands for 'Plant Systems-biology Modelling' and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting till 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative public repository of secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in similar situation. We share our reflections on sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies.

Biodiversity Informatics in Brazil: A personal perspective

Biodiversity informatics (BI) plays an important role in helping us know, protect and use biodiversity sustainably. It encompasses activities from data digitization, standardization, sharing and aggregation, to supporting decision and policy making. In a country like Brazil, with a large continental geographic area containing ca. 15% of the planet’s biodiversity, the challenge is even greater: stakeholders are widely distributed over a large country and the amount of data is huge. Brazil has been a part of the international BI community, including Biodiversity Information Standards (TDWG), for around two decades. Initially represented solely by the Centro de Referência em Informação Ambiental (CRIA), gradually other groups from universities, museums and institutions joined the arena. Despite the broader group of stakeholders now involved, the local community is not strong enough. From a human resources point of view, the country has very good universities that train competent professionals both in information technology (IT) and in biology or related fields. Concerning the IT professionals, not surprisingly, other industries and job opportunities are usually more attractive and few people even know about BI. This is probably not unique to Brazil. Biological sciences professionals, for their part, usually have little literacy in computing and are equally unaware of BI as a field. On the institutional level, museums, universities and other biological data owners often lack IT support for biological data management, including digitization, and systems development/maintenance. This may reflect the lack of appreciation of the importance of data and of BI as a foundation for good biodiversity science and management. The same happens when it comes to funding. Biological collections are not adequately funded and lack more than a few episodic programs to support collection and museum maintenance and digitization. This lack of infrastructural funding highlights the tragedies of the fires at the Butantan Museum in 2010 (80,000 snakes, 180,000 spiders) and the Museu Nacional in 2018, (20 million biological specimens and objects of the Brazilian and world history and art were lost). The exception is the São Paulo Research Foundation (FAPESP), which has been supporting projects since 1999 on biodiversity and BI via its successful Biota-FAPESP program, the first to tie biodiversity projects to data digitization and sharing in Brazil. The lack of institutional engagement and support, and funding affects the sustainability of many initiatives and puts at risk the long term data availability. Due to political reasons, Brazil only joined the Global Biodiversity Information Facility (GBIF) in 2012 as an associate (non-contributing financially and non-voting) member with a commitment to become a voting member within five years. Until recently, the Brazilian Biodiversity Information System (SiBBr), Brazil’s GBIF node, was also hindered by politics from having a solid, stable national governance and funding to help “consolidate a solid national infrastructure on biodiversity data”, and to unite the growing Brazilian BI community around it. In the international scenario, while political, cultural and funding reasons may have hindered more equitable collaborations (e.g., tools development and sharing) with countries in the Global South, competing Global North-centric projects have prevailed. Although most remaining biodiversity is in the Global South, where local engagement is crucial, in many cases southern partners still often only act as data providers. Collaborative work is required with genuine co-creation, empowering all parties. Initiatives like the Living Atlases community must be recognized and welcomed as a positive shift. Despite all of these challenges, it can be surprising how much Brazilian biodiversity science has achieved throughout the years, and it gives us hope that in the future, if some of these issues are addressed, a lot more can be done. Education and training, continued funding and institutional support, governance, and international collaboration are essential.

THE APPLICATION OF BIOINFORMATICS IN THE MOLECULAR CHARACTERIZATION OF Bacillus licheniformis

Bioinformatics is the application of information technology in biology and includes the processes of gathering, processing and analysing experimental results. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Computers are necessary in microbiology because the manual comparison of multiple sequences has become unpractical. The research subject was the characterisation of the strain ST51 isolated from the thermal water well in Vranjska Banja, south eastern Serbia. Molecular characterisation of these three strains was performed by analysis of the tuf gene, which encodes the elongation factor Tu. The DNA sequences were compared to those deposited in GenBank. data bases using the BLAST program (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn). The biochemical characterisation was performed using the API 50 CHB system (bioMerieux) and APIWEB TM software Ver. 4.1. The molecular characterisation of the strain ST51 proved the highest level of similarity to the strain Bacillus licheniformis marked as ATCC 14580 (99% identical). The biochemical characterization confirmed that the strain ST51 belongs to the species Bacillus licheniformis.Given that all the conducted analyses yielded a substantial number of data, they were processed and compared using biostatistics methods and tools in order to achieve the highest probability of resulted taxonomic classification. Modern research contributes to the analysis of a significant number of variables which is why considerably more statistical analyses are involved in their interpretation and presentation. Our results indicate that different methods are needed for proper determination and characterisation of isolates/strains. Regarding taxonomy, molecular methods are the most precise, while for physiological specificity biochemical methods are more reliable.

The BIG Data Center's database resources

Omics data in life and health sciences are of fundamental significance for scientific research and biomedical technology development. However, there is yet to be a platform for biological data management and sharing in China, making it difficult to meet the development needs of biomedical and related fields and consequently leading to severe issues in big data management, sharing and translation. To address these issues, Beijing Institute of Genomics (BIG) of Chinese Academy of Sciences founded the BIG Data Center (BIGD) in 2016, which is dedicated to establish a biological big data management platform and multi-omics databases, with a particular focus on national population healthcare and important strategic biological resources. In this paper, we describe core database resources in BIGD, including GSA (Genome Sequence Archive), GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas), MethBank (Methylation Bank), BioCode and Science Wikis. Taken together, all these resources provide a series of services for data deposition, integration and sharing, laying solid foundations for enhancing national biological science data management and further promoting the construction of national bioinformatics center.

Korean Bio-resource Information System (KOBIS): the Nationwide Infrastructure for Collecting and Integrating Biological Resource Information in Korea

As a national center for managing biological data, the Korean Bioinformation Center (KOBIC) provides capabilities and resources to manage and standardize the explosively growing amount of biological data from national Research and Development grants by developing a systematic and integrative approach. The biological data includes biological material resource, genome, and biodiversity data, such as observation, collection, taxonomy, character, and genome information of living organisms. The Korean government has enacted legislature for the collection, management and utilization of biological data in 2009 and, as a follow-up, KOBIC has undertaken the mission to collect and integrate the scattered biological data in Korea. We first made a biological data format for exchanging data between government agencies. After that, the Korean Bio-resource Information System (KOBIS) has been developed. KOBIS is an integrated information system for efficient acquisition and systematic management of biological data. KOBIS contains more than 109,000 species and 12.1 million occurrence records from 107 collaborating institutions from four ministries. KOBIS is a system that establishes a catalog of scientific names by linking species information by ministries. The main function is integrated information search. The results of integrated information search show character information, bibliographic information, electronic book, DNA classification, gene information, photo image, and research achievement. We will continue to focus our efforts on the management of KOBIS for facilitation of information sharing, distribution, and service towards mining biological data. KOBIS is available at http://www.kobis.re.kr.

QPortal: A platform for data-driven biomedical research

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis.We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software’s strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy).qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

Sustaining Software for Biological Collections Computing

Specify is a biological collections data management platform for the digitization, curation, and dissemination of museum specimen information. The Specify Software Project and its predecessor, the MUSE Project, have been funded by the US National Science Foundation for 30 years. Specify 6, a native desktop app is used in about 500 biological collections for specimen data processing. The latest generation, Specify 7, is a web platform for hosting collections data in the Specify Cloud service or on an institutional server. During 2017 and 2018, with encouragement from its long-time funder, the Specify Project is 'transitioning-to-sustainability', in a campaign to identify an organizational structure and sources of revenue to support the Project's software engineering, help desk and data management services. A museum consortium dedicated to maintaining and evolving Specify Software is a probable outcome. Such a non-profit consortium would formally engage institution directors, collections researchers, and biodiversity informaticists in the governance of Specify. Each group would play a significant role in determining the direction and capabilities of consortium software products and services. In this paper, we will summarize our approach with the Specify transition. The Specify Project's transition to sustainability is not unique within the research museum community. In the United States, groups that produce Symbiota, Arctos, and GeoLocate, as examples, face a similar quest. As legacy funders look to data communities to underwrite more of the ongoing expense of research cyberinfrastructure, projects seeded with grant funds that contribute to the overall suite of collections digitization and management software solutions will, sooner or later, face this existential challenge. Significant questions of research community economics present themselves. How much can collections institutions, of all sizes and budgets, afford to pay for specimen data software platforms and services? Will the significant cost of collections software development and support lead to sustained, collaborative, community-wide efforts, similar to the way that members of professional societies pool resources (including endowments) to provide journal and annual meeting 'infrastructure'? Or will the high cost of software, security and systems management, etc., drive wealthier museums to outsource software development by licensing commercial products, and relegate collections unable to afford software licenses, to bespoke, simple, or no software solutions? This session will include project case studies on the economics of collections research cyberinfrastructure and present perspectives and paths to long-term sustainability.

Applications of Bioinformatics in Genomics and Proteomics

Bioinformatics is the application of statistics and computer science to the field of molecular biology. The term bioinformatics was coined by Paulien Hogeweg in 1979 for the study of bioinformatics processes in biotic systems. Its primary use since at least the late 1980s has been in genomics and proteomics, particularly in those areas of genomics involving in large-scale DNA sequencing and proteomics in protein structure prediction. Bioinformatics now entitle the creation and advancement of data bases, algorithms, computational and statistical techniques and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and proteomics. Research technologies and developments in information technologies have combined to produce tremendous amount of information related to molecular biology. It is the name given to these mathematical and computing approaches used to clear understanding of biological processes. Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning different DNA and protein sequences to compare them and creating and viewing 3-D models of protein structures. The primary goal of bioinformatics is to increase the understanding of biological processes. Bioinformatics is focus on developing and applying computationally intensive techniques (e.g., data mining, machine learning algorithms, and visualization) to achieve this goal. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, genome-wide association studies and the modeling of evolution.

Knowledge and intelligent computing techniques in bioinformatics

Knowledge-based systems (KBS) and intelligent computing techniques (ICT) are widely used in the field of bioinformatics. A number of techniques have been applied for the interpretation and analysis of biological data. KBS consists of case based reasoning (CBR) whereas ICT consists of artificial neural network (ANN), fuzzy logic (FL), data mining techniques (DMT), swarm intelligence (SI) and evolutionary algorithm (EA). The aim of this review is to introduce briefly the various KBS and ICT and their integration such as ANN-DMT, CBR-DMT, EA-ANN, EA-DMT, EA-ANN-DMT, ANN-SI, ANN-FL, ANN-SI-EA-FL, EA-FL, EA-SI etc. which are applied in the field of bioinformatics. This study is exhibited in the form of a table which includes application area, intelligent technique used, machine learning task and the database/databank used. This study will help the novice researchers to choose appropriate KBS and ICT to deal with the challenges encountered during representation, integration, analysis, interpretation and management of biological data.