Journal Information
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
https://www.computer.org/csdl/journal/tb
Impact Factor:
2.896
Publisher:
IEEE/ACM
ISSN:
1545-5963
Viewed:
10244
Tracked:
12

Call For Papers
Affective computing is the field of study concerned with understanding, recognizing and utilizing human emotions in the design of computational systems. Research in the area is motivated by the fact that emotion pervades human life – emotions motivate human behavior, they promote social bonds between people and between people and artifacts, and emotional cues play an important role in forecasting human mental state and future actions. Technology is less efficient if it perturbs human emotions; more efficient if it engages with them productively; more attractive if it appeals to human emotions; and often it is primarily concerned with enabling humans to experience particular emotions (notably happiness). Since the coining of the term by Picard in 1997, affective computing has emerged as a cohesive sub-discipline in computer science with its own international conference (the International Conference on Affective Computing and Intelligent Interaction) and professional society (the HUMAINE Association).

IEEE Transactions on Affective Computing is intended to be a cross disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. The journal will publish original research on the principles and theories explaining why and how affective factors condition interaction between humans and technology, on how affective sensing and simulation techniques can inform our understanding of human affective processes, and on the design, implementation and evaluation of systems that carefully consider affect among the factors that influence their usability. Surveys of existing work will be considered for publication when they propose a new viewpoint on the history and the perspective on this domain. The journal covers but is not limited to the following topics:

Sensing and analysis

    Algorithms and features for the recognition of affective state from face and body gestures
    Analysis of text and spoken language for emotion recognition
    Analysis of prosody and voice quality of affective speech
    Recognition of auditory and visual affect bursts
    Recognition of affective state from central (e.g. fMRI, EEG) and peripheral (e.g. GSR) physiological measures
    Methods for multi-modal recognition of affective state
    Recognition of group emotion
    Methods of data collection with respect to psychological issues as mood induction and elicitation or technical methodology as motion capturing
    Tools and methods of annotation for provision of emotional corpora

(Cyber)Psychology and behavior

    Clarification of concepts related to ‘affective computing' (e.g., emotion, mood, personality, attitude) in ways that facilitate their use in computing.
    Computational models of human emotion processes (e.g., decision-making models that account for the influence of emotion; predictive models of user emotional state)
    Studies on cross-cultural, group and cross-language differences in emotional expression
    Contributions to standards and markup language for affective computing

Behavior generation and user interaction

    Computational models of visual, acoustic and textual emotional expression for synthetic and robotic agents
    Models of verbal and nonverbal expression of various forms of affect that facilitate machine implementation
    Methods to adapt interaction with technology to the affective state of users
    Computational methods for influencing the emotional state of people
    New methods for defining and evaluating the usability of affective systems and the role of affect in usability
    Methods of emotional profiling and adaptation in mid- to long-term interaction
    Application of affective computing including education, health care, entertainment, customer service, design, vehicle operation, social agents/robotics, affective ambient intelligence, customer experience measurement, multimedia retrieval, surveillance systems, biometrics, music retrieval and generation
Last updated by Dou Sun in 2020-05-07
Special Issues
Special Issue on Computational Genomics and Molecular Medicine For Emerging COVID-19
Submission Date: 2020-06-30

By controlling devastating and emerging coronavirus disease (COVID)-19, new drug molecules, vaccines, and antibiotics from microbiological to sequence-based approaches have had an enormous impact on world health. Healthcare researchers have always been a leader in innovation. Virus diseases make it challenging to stay ahead of the curve, but with the application and utility of artificial intelligence and machine-learning algorithms, it continues to advance, creating new therapeutics for healthier lives. Intelligent algorithms for machine learning, as well as “big data,” are seen as calculation and support for COVID-19, but never as an objective in and of themselves. The improvements and recent growth that the use of pharmacogenomics and drug designing has seen in the past few years can cover fields that are directly or indirectly, or even be implemented to aid in the development of pharmaceutical products or industrial applications. As such, it is time to create a “research topic” where advances in this field, linked directly to COVID-19, are presented. Currently, the People’s Republic of China and a number of Western countries are challenged with a huge burden of coronavirus infection to emerging COVID-19. The fact that a COVID-19 has emerged indicates immune naivety in the infected population, or altered virulence potential or an increase in the pathogen population. The rapid development of vaccines and therapeutics that target these pathogens is therefore essential to limit their spread. Genome sequences provide a previously unattainable route to investigate the mechanisms that underpin pathogenesis. Besides, genomics, transcriptomics, structural genomics, proteomics, and immunomics are being exploited to perfect the identification of targets, to design new vaccines and drugs, and to predict their effects in patients. Furthermore, human genomics and related studies are providing insights into aspects of host biology that are important in infectious disease. Now, with the advent of the “machine learning on omics era,” a paradigm shift is occurring in the development of novel drug molecules, vaccines, and potential antibiotics are given that new motivation to this field. High-quality research related to drug design with genome engineering through artificial intelligence is an emerging field of study. It is concerned with the design and testing of molecular properties, behavior, and interactions to assemble better materials, systems, and processes of COVID-19. Computational artificial intelligence and chemistry advances are parallel with the rapid progress in drug designing methods. This technique is becoming a powerful tool in COVID-19 to identify the starting points as hit molecules. Advances in artificial intelligence and small molecule chemistry are becoming the benchmark of the 21st century, opening up new avenues for drug discovery. New approaches are needed as the cost of COVID-19 drug development is increasing with decreasing investment returns. This ever-growing body of genomic data and machine learning-based approaches will play a critical role in the future to enable the timely development of vaccines and therapeutics to control emerging COVID-19. Many challenges remain identification targets and in the processes needed to bring a new vaccine or drug to the market. Understanding the molecular nature of genomic segments/epitopes, the mechanisms of action of targets and drugs, and cell-mediated immunity are key priorities to be tackled in the coming years. These issues can be addressed by improved structural genomic studies of antigens and the compilation of databases containing information on structure, immunogenicity, and B and T cell epitope predictions through the advancement of machine learning. Numerous FDA-approved drugs are declining with the number of new molecular entities (NMEs). The reasons noted are adverse side effects and reduced efficiency of many potential compounds. Genome-based development of effective small molecule therapeutics and vaccines is still largely dependent on the availability of valid computational models to measure efficacy and protection against COVID-19. Artificial intelligence and small molecule chemistry provide a new direction to the system-centric idea for R&D leading to future drugs, which starts with the identification of the scope of a new drug. This special issue welcomes research that yields novel breakthroughs towards artificial intelligence and genome-based precision medicines for COVID-19. All the manuscripts submitted to this special issue will be peer-reviewed. Pertinent topics may include: Artificial intelligence in drug discovery and target identification for COVID-19 Data mining and network analysis in COVID-19 Structure, function, evolution, and mapping of COVID-19 Microarrays and next-generation sequencing in COVID-19 Single cell transcriptomics of COVID-19 Structural and target information for COVID-19 Drug design and dynamics simulation for COVID-19 Epitopes identification and vaccine design for COVID-19 Human genomics and COVID-19 IMPORTANT DATES Open for abstract submission: February 20, 2020 Open for submissions in ScholarOne Manuscripts: March 15, 2020 Closed for submissions: June 30, 2020 Results of first round of reviews: August 15, 2020 Submission of revised manuscripts: October 15, 2020 Results of second round of reviews: November 15, 2020 Publication materials due: December 01, 2020 SUBMISSION GUIDELINES Prospective authors are invited to submit their manuscripts electronically after the “open for submissions” date, adhering to the IEEE/ACM Transactions in Computational Biology and Bioinformatics guidelines. Please submit your papers through the online system (https://mc.manuscriptcentral.com/tcbb-cs) and be sure to select this special issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by email to the guest editors directly. GUEST EDITORS Dongqing Wei, PhD, has been a professor of bioinformatics at Shanghai Jiaotong University since 2006. Over the past three decades, he has made many groundbreaking contributions to the development of molecular simulation techniques and their interdisciplinary applications to systems of ever-increasing complexity. He is best known for contributions to the development of molecular simulation and, more recently, AI tools and software with applications to a wide range of chemical, physical, and biological systems–from electrolytes to polar liquids, to ferroelectric liquid crystals, to combined Quantum Mechanical/Molecular Mechanical (QM/MM) systems, to membrane proteins and protein-ligand complexes applied to computer aided drug design. His most important contributions in the sciences are exemplified by the discovery of ferroelectric nematic liquids crystals, the first complete ab initio MD simulation of explosion (nitromethane), and anti-aging and anti-AD drug candidate WGX-50. Prof. Wei has published more than 300 papers and has been cited 8,000 times. Email: dqwei@sjtu.edu.cn Aman Chandra Kaushik, PhD, is an Assistant Professor at Wuxi School of Medicine, Jiangnan University, China. His research direction focuses on drug development for Alzheimer’s Disease, cancer, and diabetes using machine learning and system biology approaches. He developed the machine learning tools Weidock, SPVec, and A-CaMP, which have had an impact in system-based medicine. Before Jiangnan University, he was a postdoctoral scholar in bioinformatics at Shanghai Jiao Tong University. He received his PhD in bioinformatics from the Gautam Buddha University, India while also researching at Ben-Gurion University of the Negev, Israel. He has published more than 50 papers and been cited 200 times. Email: amanbioinfo@sjtu.edu.cn Gurudeeban Selvaraj, PhD, is a MITACS-visiting Postdoctoral Scientist at Concordia University, Canada. He continues his research faculty position in Prof. Wei’s lab, Henan University of Technology, China. His research investigates genomic and proteomic data to develop precision medicine for NSCLC using machine-learning algorithms. He is involved in vaccine design and statistical meta-analysis of genomic and clinical data. Prior to joining CERMM, he completed his postdoctoral research at Henan University of Technology and Istanbul Medeniyet University. He received his bachelor of science degree in biochemistry from Bharathiar University and his master’s and doctorate degrees in marine biotechnology from Annamalai University. He has received research grants from different funding agencies including MITACS Global link, China Postdoctoral Science Foundation, Henan Postdoctoral Science Foundation, Henan University of Technology, The Scientific and Technological Research Council of Turkey, and University Grants Commission, India. He has published more than 50 research articles and has been cited 1,295 times in reputed journals including Current Medicinal Chemistry, Journal of Biomedical Informatics, Phytomedicine, The Journal of Physical Chemistry C and Current Pharmaceutical Design, and Current Drug Targets, and has participated in more than 25 different international and national conferences and workshops. Email: gurudeeb99@haut.edu.cn Yi Pan, PhD, is a Regents’ Professor and Chair of Computer Science at Georgia State University. He served as an Associate Dean and Chair of the Biology Department from 2013 to 2017 and as Chair of Computer Science from 2006 to 2013. Dr. Pan joined Georgia State University in 2000, was promoted to full professor in 2004, named a Distinguished University Professor in 2013, and designated a Regents Professor (the highest recognition given to a faculty member by the University System of Georgia) in 2015. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his PhD degree in computer science from the University of Pittsburgh in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan’s current research interests include parallel and cloud computing, big data, and bioinformatics. Dr. Pan has published more than 450 papers including over 250 SCI journal papers and 100 IEEE/ACM transactions/journal papers. In addition, he has edited/authored 43 books. His work has been cited more than 12,200 times based on Google Scholar and his current h-index is 56. Dr. Pan has served as an editor-in-chief or editorial board member for 20 journals, including seven IEEE transactions. Currently, he is Associate Editor-in-Chief of IEEE/ACM Transactions on Computational Biology and Bioinformatics. He is the recipient of many awards including one IEEE Transactions Best Paper Award, five IEEE and other international conference or journal Best Paper Awards, four IBM Faculty Awards, two JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, IEEE Outstanding Leadership Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship. He has organized numerous international conferences and delivered keynote speeches at over 60 international conferences around the world. Email: yipan@gsu.edu
Last updated by Dou Sun in 2020-05-07
Special Issue on Deep Learning and Graph Embeddings for Network Biology
Submission Date: 2020-08-31

Biological networks are powerful resources for modelling, analysis, and discovery in biological systems, ranging from molecular to epidemiological levels. In recent years, network models and algorithms have been used to represent and analyze the whole set of associations and interactions among biologically relevant molecules inside cells, (e.g., proteins, genes, transcription factors, and more recently the big class of non-coding genes), supporting the elucidation of the molecular mechanisms as well as the development of precision medicine for many relevant diseases (e.g., cancers or brain disorders). Mathematical machinery that is central to this area of research is graph theory and machine learning on graph-structured data. Recent research efforts have introduced methods and tools that can model biological phenomena and learn and reason about them through networks. Such data and models are typically stored in various databases of experimental data and repositories of biomedical knowledge. Network data extracted from these databases are often mined for knowledge about a biological system of interest (e.g., using network statistics or community detection algorithms) and to compare two or more networks (e.g., using network alignment algorithms). Current approaches may present limitations in some applications since they can fail to generalize from observed network structure to new biological phenomena, are unable to include prior knowledge in the analysis, rely on user-defined heuristics and painstaking manual feature engineering to extract features from biological networks, or fail to support researchers when limited biological data is available (e.g., small datasets with low coverage). Recent years have seen a surge in approaches, such as deep learning, that have shown broad utility in uncovering new biology and contributing to new discoveries in wet laboratory experiments. In particular, in biological and biomedical areas, deep learning has proven an efficient way to deal with data generated from modern high-throughput technologies. In parallel, the field of network science has been influenced by the development of methods that automatically learn to encode network structure into low-dimensional embeddings, using data transformation techniques based on matrix factorization, deep learning, nonlinear dimensionality reduction, and complex non-linear models. The key idea of these methods (or graph representation learning) is to automatically learn a function able to map nodes in the graph (or other graph structures) to points in a compact vector space, whose geometry is optimized to reflect topology of the input graph. The relevance and potential of graph representation learning are evidenced by the rise of approaches that are beginning to effect on the way network biology is performed today at the fundamental level. Therefore, there is strong need to discuss and foster these advances in a systematic way to give support both to researchers and practitioners. The goal of this special issue is to collect both surveys and papers describing novel methods and applications in computational biology and bioinformatics. Papers presenting applications in medicine and healthcare are also welcome. The topics of interest for this special issue include, but are not limited to: Deep learning and graph neural networks for network biology Learning meaningful representations for biomedical networks Learning node, edge, higher-order, and graph-level embeddings for biological networks Next-generation graph embedding techniques for important problems, including node classification, link prediction, graph classification, and network alignment Graph representation learning for visualizing and interpreting interaction data Next-generation network science through network embeddings Relevant benchmark datasets, initial solutions for new challenges, and new directions in network biology Applications of network embeddings broadly in computational biology, genomics, medicine, and health IMPORTANT DATES Abstract submission: August 31, 2020 Open for submissions in ScholarOne Manuscripts: August 31, 2020 Closed for submissions: August 31, 2020 Results of first round of reviews: October 15, 2020 Submission of revised manuscripts: November 15, 2020 Results of second round of reviews: December 15, 2020 Publication materials due: January 15, 2021 SUBMISSION GUIDELINES Prospective authors are invited to submit their manuscripts electronically after the “open for submissions” date, adhering to the IEEE/ACM Transactions in Computational Biology and Bioinformatics guidelines. Please submit your papers through the online system (https://mc.manuscriptcentral.com/tcbb-cs) and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by email to the guest editors directly. GUEST EDITORS Pietro H. Guzzi has been an Associate Professor of computer science and bioinformatics at the University ‘Magna Græcia’ of Catanzaro, Italy, since 2008. He received his PhD in biomedical engineering in 2008, from Magna Græcia University of Catanzaro. He received his Laurea degree in computer engineering in 2004 from the University of Calabria, Rende, Italy. His research interests comprise bioinformatics and network analysis. In network analysis, in particular, Pietro has worked on local alignment of biological networks providing some tools for network alignment. Currently, he is working on novel approaches of alignment that merge both local and global alignment and on the development of novel methods of analysis based on the integration of heterogeneous networks through embedding. Pietro is an ACM member and serves the scientific community as a reviewer for many conferences. He is an associate editor of IEEE/ACM TCBB and of SIGBioinformatics Record. Marinka Zitnik is an Assistant Professor at Harvard University. Her research investigates artificial intelligence and machine learning to advance science, medicine, and health. Her methods have had a tangible impact in biology, genomics, and drug discovery, and are used by major biomedical institutions, including Baylor College of Medicine, Karolinska Institute, Stanford Medical School, and Massachusetts General Hospital. Before Harvard, she was a postdoctoral scholar in computer science at Stanford University and a member of the Chan Zuckerberg Biohub at Stanford. She received her PhD in computer science from the University of Ljubljana while also researching at Imperial College London, University of Toronto, Baylor College of Medicine, and Stanford University. Her work received several best paper, poster, and research awards from the International Society for Computational Biology. She has recently been named a Rising Star in EECS by MIT and also a Next Generation in Biomedicine by The Broad Institute of Harvard and MIT, being the only young scientist who received such recognition in both EECS and Biomedicine.
Last updated by Dou Sun in 2020-05-07
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