Journal Information
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Impact Factor:

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 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 ( 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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