Alison’s scientific background is in biomedical sciences and bioinformatics. She read for a degree in Biomedical Sciences at the University of Durham, UK. Having become interested in the then new field of bioinformatics, she then decided to do a Masters in Information Technology at Teesside University, UK. She went on to study at the University of Reading, UK completing a bioinformatics-based PhD on investigating the effects of mutations on the structure of p53, before working for a time in academia, including 8 years as the curator of the CATH protein classification database. After eventually deciding to pursue a career in scientific publishing, she joined BioMed Central in 2014, initially as a Database Editor for the ISRCTN clinical trial registry. Alison joined the BMC series in 2016 and became the Editor for BMC Bioinformatics in 2017. Alison also writes on the BioMed Central blog network and is an Editor for the On Medicine blog.
Lukasz Kurgan is the Qimonda Endowed Professor in the Department of Computer Science at the Virginia Commonwealth University. He received Ph.D. in Computer Science from the University of Colorado at Boulder in 2003. Dr. Kurgan joined the editorial board of BMC Bioinformatics in 2010 and currently he is the Section Editor for structural bioinformatics. Dr. Kurgan’s research interest are in structural bioinformatics of proteins and small RNAs, from single molecules through entire proteomes/genomes to projects that span thousands of proteomes/genomes. Highlights of recent research coming from his lab include release of widely used computational tools for high-throughput prediction of functional residues in protein sequences, tools that support target selection for structural genomics and methods for functional characterization of intrinsic disorder in proteins. More details are available on the web site of his lab at http://biomine.cs.vcu.edu /
Imaging, image analysis and data visualization
Hanchuan Peng (http://penglab.com) leads a Brain Big Data research group at the Allen Institute for Brain Science, with the goal to develop revolutionary technologies to generate, manage, visualize, analyze, and understand massive-scale structure and function data related to brains, especially human brains, for both basic research and medical applications. Before joining the Allen Institute, Peng was the head of a Big Image Mining group at Janelia Research Campus of the Howard Hughes Medical Institute. He is also an affiliate/adjunct faculty member/professor with University of Washington (USA), University of Georgia (USA), etc. Peng was the founder of the annual Bioimage Informatics conferences (http://bioimageinformatics.org). He serves as the steering committee chairs for both the Bioimage Informatics conferences and the Brain Informatics conferences.
Peng is the inventor and a highly cited author of a number of algorithms and software/hardware systems, including Vaa3D (Nature Biotechnology 2010; Nature Protocols, 2014), BrainAligner (Nature Methods, 2011), UltraTracer (Nature Methods, 2017), SmartScope (Nature Communications, 2014), mRMR (IEEE-TPAMI, 2005), 3D Virtual Finger (Nature Biotechnology 2010; Nature Communications, 2014), SmartACT (Scientific Reports, 2015), TeraFly (Nature Methods, 2016), etc. Peng's recent work includes developing novel algorithms for 3D+ image analysis and data mining, building single-neuron whole-brain level 3D digital maps for model animals, and Vaa3D (http://vaa3d.org), which is a high-performance visualization-assisted analysis system for very large multi-dimensional images. He built the first neuron stereotypy map of a fruit fly brain (Nature Methods, 2011), co-developed the first single cell resolution 3D digital maps of C. elegans (Nature Methods, 2009; Cell, 2009), led several largest studies to-date on 3D brain image registration and standardization (Nature Methods, 2011; Cell Reports, 2012; Nature, 2014), and is the lead for the “BigNeuron” initiative (http://bigneuron.org; Neuron, 2015). He was also the inventor of the widely cited minimum-Redundancy Maximum-Relevance (mRMR) feature/variable selection methods in machine learning and data mining (a top-10 popular IEEE-TPAMI papers since 2005).
Peng was a co-recipient of USA National Academy of Sciences’ Cozzarelli Prize (2013), etc. His work has been featured in Nature News, Science News, Science Magazine, NPR, NBC, etc.
Sequence analysis (methods)
Sequence analysis (applications)
João Setubal has been a full professor in the Biochemistry Department of the Institute of Chemistry at the University of São Paulo, Brazil since 2011. Setubal has a PhD in Computer Science (1992) from the University of Washington (USA). He was a faculty member at the University of Campinas (Unicamp, Brazil) (1992-2004), then Associate Professor at the Biocomplexity Institute of Virginia Tech, USA (formerly the Virginia Bioinformatics Institute) (2004-2011), where he still is an Adjunct Faculty. His research interests are in computational tools for genomics, metagenomics, and transcriptomics, and applications of such tools primarily in microbiology and microbial ecology. Setubal joined the Editorial Board of BMC Bioinformatics in 2010 as Associate Editor, and since 2017 he has been a Section Editor. More information at http://www.iq.usp.br/setubal/index-en.html
Hagit Shatkay is an Associate Professor and Director of the Computational Biomedicine and Machine Learning Lab at the Dept. of Computer and Information Sciences, University of Delaware, with cross-appointments at the Dept. of Biomedical Engineering, and at the Delaware Biotechnology Institute.
Prior to joining the University of Delaware (2010) she was an Associate Professor and Director of the Computational Biology and Machine Learning Lab at Queen¹s University, Kingston, Ontario. Before moving to academia, she was an Informatics Research Scientist with the Informatics Research group of Celera Genomics, and a post-doctoral fellow at NCBI. She holds a PhD in Computer Science from Brown University, and an MSc and BSc in Computer Science from the Hebrew University of Jerusalem.
She is an active member of the biomedical informatics and of the bio-text research community since its early days (1999), has presented many invited talks and multiple international tutorials in these areas, and together with Mark Craven has authored the first comprehensive book in this area, Mining the Biomedical Literature (MIT Press, 2012). Among her many organizational and editorial roles, she is a Board Member of the International Society for Computational Biology (ISCB), Section Editor for Knowledge Based Analysis at BMC Bioinformatics, associate editor in several journals, has been an Area Chair of the Text Mining and/or Databases area at the International Conference on Intelligent Systems for Molecular Biology
(ISMB) continuously since 2008, co-chair of the BioLINK SIG (the Special Interest Group on linking biology and literature) at ISMB since 2005, on the advisory board of BioASQ (A Challenge on Large Scale Biomedical Semantic Indexing and Question Answering) since its inception in 2012, the steering committee of for TREC Genomics 2005-2008, and many others.
Her research aims to develop and to employ machine learning and data-mining methods for addressing data-intensive problems in biology and medicine. She has been among the first, leading researchers in the area of biological text mining, working in the area for about 20 years now, and has co-authored (with Mark Craven) the book ³Mining the Biomedical Literature² (MIT Press, 2012). She also works along with her colleagues and graduate students toward developing computational tools that can utilize data from diverse sources, including sequence, image and time-series data, in order to gain better understanding of protein location and function, and to better identify/predict certain medical conditions and disease.
Jean-Philippe Vert is a Professor at the Department of Mathematics and Applications of ENS Paris, director of the Centre for Computational Biology at MINES ParisTech, and team leader at Institut Curie, Paris, France. He graduated from Ecole Polytechnique, Ecole des Mines, and obtained his PhD in mathematics from ENS Paris and Paris 6 University. He is interested in statistical machine learning and its applications in computational biology and chemistry. With his lab he develops new methods and studies theoretical properties of techniques for modeling and learning from high-dimensional, structured data such as genomic profiles of chemical structures. He works on various applications including virtual screening, chemogenomics, precision medicine, systems biology or genome architecture. He joined the editorial board of BMC Bioinformatics in 2010, and became Section Editor for Networks Analysis in 2015.