Special Issue "Explainable Artificial Intelligence (XAI) in Biomedical Research and Clinical Practice"

A special issue of BioMedInformatics (ISSN 2673-7426).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Jörn Lötsch
E-Mail Website
Guest Editor
Institute of Clinical Pharmacology, Goethe-University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany
Interests: pharmacological data science; applied artificial intelligence; statistical parametric mapping; nonlinear-mixed effects modeling
Prof. Dr. Alfred Ultsch
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Philipps University of Marburg, Hans-Meerwein-Strasse, 35032 Marburg, Germany
Interests: databionics; neural networks and artificial intelligence; data science; information extraction

Special Issue Information

Dear Colleagues,

Advanced computational methods of machine learning and related artificial intelligence are increasingly entering biomedical research and clinical practice. These processes are bidirectional. Computational methods are used to solve biomedical problems and biological systems are studied to develop and improve artificial intelligence methods, enabling a paradigm shift from hypothesis-driven research and clinical decision-making to data-driven approaches to discovering knowledge from biomedical data.

The shift from therapy-relevant decisions based on biomedical knowledge to black-box-like computer algorithms makes the decision-making increasingly incomprehensible to medical staff and patients. This has been recognized in the issuance of guidelines, e.g., by the European Union or DARPA (USA), which emphasize the need for computer-based decisions to be transparent and in a form that can be communicated in an understandable way to medical personnel and patients. To address this problem, the concept of explainable artificial intelligence (XAI) is attracting scientific interest. XAI uses a representation of human knowledge, usually (a subset of) predicate logic, for its reasoning, deduction, and classification (diagnosis).

In this Special Issue of Biomedinformatics, we invite contributions on the development and implementation of explainable artificial intelligence (XAI) algorithms in biomedical research and practice, focusing on, but not limited to, original research reports.

Prof. Dr. Jörn Lötsch
Prof. Alfred Ultsch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. BioMedInformatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Computer-aided classification and subgroup detection
  • Personalized and precision medicine
  • Supervised and unsupervised machine learning
  • Symbolic machine learning
  • Understandable data mining
  • Explainable artificial intelligence
  • Biomedical knowledge representation
  • Biomedical knowledge discovery
  • Controlled hypothesis generation

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


High Expression of Caspase-8 Associated with Improved Survival in Diffuse Large B-Cell Lymphoma: Machine Learning and Artificial Neural Networks Analyses
BioMedInformatics 2021, 1(1), 18-46; https://0-doi-org.brum.beds.ac.uk/10.3390/biomedinformatics1010003 - 21 Apr 2021
Cited by 1 | Viewed by 808
High expression of the anti-apoptotic TNFAIP8 is associated with poor survival of the patients with diffuse large B-cell lymphoma (DLBCL), and one of the functions of TNFAIP8 is to inhibit the pro-apoptosis Caspase-8. We aimed to analyze the immunohistochemical expression of Caspase-8 (active [...] Read more.
High expression of the anti-apoptotic TNFAIP8 is associated with poor survival of the patients with diffuse large B-cell lymphoma (DLBCL), and one of the functions of TNFAIP8 is to inhibit the pro-apoptosis Caspase-8. We aimed to analyze the immunohistochemical expression of Caspase-8 (active subunit p18; CASP8) in a series of 97 cases of DLBCL from Tokai University Hospital, and to correlate with other Caspase-8 pathway-related markers, including cleaved Caspase-3, cleaved PARP, BCL2, TP53, MDM2, MYC, Ki67, E2F1, CDK6, MYB and LMO2. After digital image quantification, the correlation with several clinicopathological characteristics of the patients showed that high protein expression of Caspase-8 was associated with a favorable overall and progression-free survival (Hazard Risks = 0.3; p = 0.005 and 0.03, respectively). Caspase-8 also positively correlated with cCASP3, MDM2, E2F1, TNFAIP8, BCL2 and Ki67. Next, the Caspase-8 protein expression was modeled using predictive analytics, and a high overall predictive accuracy (>80%) was obtained with CHAID decision tree, Bayesian network, discriminant analysis, C5 tree, logistic regression, and Artificial Intelligence Neural Network methods (both Multilayer perceptron and Radial basis function); the most relevant markers were cCASP3, E2F1, TP53, cPARP, MDM2, BCL2 and TNFAIP8. Finally, the CASP8 gene expression was also successfully modeled in an independent DLBCL series of 414 cases from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). In conclusion, high protein expression of Caspase-8 is associated with a favorable prognosis of DLBCL. Predictive modeling is a feasible analytic strategy that results in a solution that can be understood (i.e., explainable artificial intelligence, “white-box” algorithms). Full article
Show Figures

Graphical abstract

Back to TopTop