Design and analysis of classifier learning experiments in bioinformatics: survey and case studies

PubMed ID: 22908127In many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design of such experiments and the analysis of the resulting data using statistical tests should be done carefully for the results to carry significance. In this paper, we first review the performance measures used in classification, the basics of experiment design and statistical tests. We then give the results of our survey over 1,500 papers published in the last two years in three bioinformatics journals (including this one). Although the basics of experiment design are well understood, such as resampling instead of using a single training set and the use of different performance metrics instead of error, only 21 percent of the papers use any statistical test for comparison. In the third part, we analyze four different scenarios which we encounter frequently in the bioinformatics literature, discussing the proper statistical methodology as well as showing an example case study for each. With the supplementary software, we hope that the guidelines we discuss will play an important role in future studies.The authors would like to thank the editor and the reviewers for their constructive comments, suggestions, pointers to related literature, and pertinent questions which allowed us to better situate our work as well as organize the manuscript and improve the presentation. This work has been supported by the Turkish Scientific Technical Research Council (TUBITAK) EEEAG 109E186 and Bogazici University Research Funds BAP 5701Publisher's VersionAuthor Post Prin

AI in Bioinformatics

In bioinformatics science and computational molecular biology, artificial intelligence (AI) has rapidly gained interest. With the availability of numerous types of AI algorithms, it has become prevalent for researchers to use off-shelf programmes to identify their datasets and mine them. At present, researchers are facing difficulties in selecting the right approach that could be extended to a given data collection, with numerous intelligent approaches available in the literature. Researchers need instruments that present the data in an intuitive manner, annotated with meaning, precision estimates, and description. In the fields of bioinformatics and computational molecular biology (DNA sequencing), this article seeks to review the use of AI. These fields have evolved from the needs of biologists to use the large volumes of data continuously obtained in genomic science and to better understand them. For several approaches to bioinformatics and DNA sequencing, the fundamental impetus is the evolution of species and the difficulty of dealing with incorrect results. The type of software programmes developed by the scientific community to search, identify and mine numerous usable biological databases are also mentioned in this article, simulating biological experiments with and without mistakes. The review of antibody-antigen interactions and their diversity, and the study of epidemiological evidence that can help forecast antibody-antigen interactions and the induction of broadly neutralising antibodies are important questions to be answered in the field of vaccinology

Inspiring the Next Generation of Humanitarian Mine Action Researchers

Humanitarian mine action (HMA) is a critically under-researched field when compared to other hazards fields of similar societal impact. A potential solution to this problem is early exposure to and engagement in the HMA field in undergraduate education. Early undergraduate education emphasizing technical and social aspects of HMA can help protect lives by building a robust pipeline of passionate researchers who will find new solutions to the global explosive ordnance (EO) crisis. Early engagement of the next generation of HMA researchers and policy makers can occur through various classroom experiences, undergraduate research projects, and public outreach events. These include but are not limited to course-based undergraduate research experiences (CUREs); presenting research results at local, national, and international conferences; dissemination in edited and peer-reviewed publications; local community events; and through social media outreach. Early engagement, active guidance, and mentorship of such students by mid-career and experienced HMA scholars and practitioners could dramatically reduce the learning curve associated with entry into the HMA sector and allow for more fruitful long-term collaboration between academic institutions, private industry, and leading nongovernmental organizations (NGOs) operating across different facets of HMA

An Accessible Seeded Field for Humanitarian Mine Action Research

The detection of buried and surface explosive remnants of war (ERW) is a critical task in the land release process.[1] The goal of this project is to create a long-term study site and benchmark to accelerate humanitarian mine action (HMA) research for the detection of buried ERW, including unexploded ordnance (UXO), landmines, and improvised explosive devices (IEDs). A crucial step in transitioning experimental detection techniques from the lab to the field is conducting rigorous field testing in a realistic and safe environment.[2],[3],[4] With most academic institutions lacking access to stockpiles of inert ERW to conduct testing and prioritizing scientific publications over real-world field applicability, this step is too often neglected. The result is that most HMA studies lack sufficient benchmarking among detection variables such as depth of burial, size and diversity of ERW, and environmental context, making it nearly impossible to objectively compare the effectiveness of different instruments and sensors. Consequently, the humanitarian demining community is less willing to accept novel methods and instead relies largely on traditional approaches. To address this issue, the Demining Research Community, (a US-based non-profit organization whose mission is to advance the field of HMA though bridging academic research in accordance with demining organizations), in partnership with the Global Consortium for Explosive Hazard Mitigation at Oklahoma State University (OSU), have seeded a comprehensive field with 143 diverse items including landmines, submunitions, UXO, and IEDs located at OSU’s Center for Fire and Explosives, Forensic Investigation, Training and Research (CENFEX) range in Pawnee, Oklahoma

Bioinformatics analysis of mitochondrial disease

PhD thesisSeveral bioinformatic methods have been developed to aid the identification of novel nuclear-mitochondrial genes involved in disease. Previous research has aimed to increase the sensitivity and specificity of these predictions through a combination of available techniques. This investigation shows the optimum sensitivity and specificity can be achieved by carefully selecting seven specific classifiers in combination. The results also show that increasing the number of classifiers even further can paradoxically decrease the sensitivity and specificity of a prediction. Additionally, text mining applications are playing a huge role in disease candidate gene identification providing resources for interpreting the vast quantities of biomedical literature currently available. A workflow resource was developed identifying a number of genes potentially associated with Lebers Hereditary Optic Neuropathy (LHON). This included specific orthologues in mouse displaying a potential association to LHON not annotated as such in humans. Mitochondrial DNA (mtDNA) fragments have been transferred to the human nuclear genome over evolutionary time. These insertions were compared to an existing database of 263 mtDNA deletions to highlight any associated mechanisms governing DNA loss from mitochondria. Flanking regions were also screened within the nuclear genome that surrounded these insertions for transposable elements, GC content and mitochondrial genes. No obvious association was found relating NUMTs to mtDNA deletions. NUMTs do not appear to be distributed throughout the genome via transposition and integrate predominantly in areas of low %GC with low gene content. These areas also lacked evidence of an elevated number of surrounding nuclear-mitochondrial genes but a further genome-wide study is required