Explainable AI for Bioinformatics: Methods, Tools, and Applications

Karim, Md. Rezaul; Islam, Tanjila; Beyan, Oya; Lange, Christoph; Cochez, Michael; Rebholz‐Schuhmann, Dietrich; Decker, Stefan

doi:10.48550/arxiv.2212.13261

Cited by 3 publications

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“…&& regex(?labelGene, "ˆOncogene")) } GROUP BY ?labelBiomarker Fig. 12: SPARQL query for finding list of oncogenes responsible Breast Cancer based on PubMed [38] Fig. 13: Example of question answering to infer knowledge about named bio-entities [38]…”

Section: Discussionmentioning

confidence: 99%

“…A doctor could explain the decision in natural language as well by combining constrastive explanations. Section 4.4 shows an example of decision reasoning with rules in textual and natural human language based facts from the KG [38].…”

Section: Using the Knowledge Graphmentioning

confidence: 99%

“…6: Properties of TP53 biomarker show how it is related to different types of cancer. Dashed lines indicate literals andannotations from DOID, OGG, and external sources[38]…”

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confidence: 99%

“…Performance of knowledge extraction models from scientific articles Example of explainable NER, where the model classifies and highlights relevant biomedical entities[38] the DLQ "Biomarker and causes some BRCA and isA only POTSF". Besides, we outline some SPARQL queries (ref.fig.8, 9, 10, 11, and 12) that can be used to retrieve domain specific knowledge about biomarkers and associated evidences.…”

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confidence: 99%

“…Example queries in natural language, DLx, and logic formats[38] PREFIX ono: <\protect\vrule width0pt\protect\href{http://www.example.com/ontologies/ono/ono.owl#}{http://www.example. com/ontologies/ono/ono.owl#}> PREFIX doid: <\protect\vrule width0pt\protect\href{http://purl.obolibrary.org/obo/doid#}{http://purl.obolibrary.org/ obo/doid#}> PREFIX owl: <\protect\vrule width0pt\protect\href{http://www.w3.org/2002/07/owl#}{http://www.w3.org/2002/07/owl#}> Fig.…”

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confidence: 99%

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A Biomedical Knowledge Graph for Biomarker Discovery in Cancer

Karim¹,

Comet²,

Beyan³

et al. 2023

Preprint

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Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A domain-specific knowledge graph (KG) is an explicit conceptualization of a specific subject-matter domain represented w.r.t semantically interrelated entities and relations. A KG can be constructed by integrating such facts and data and be used for data integration, exploration, and federated queries. However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies. Such a KG will not only allow deducing new knowledge and question answering (QA), but also allows domain experts in exploration. Since cross-disciplinary explanations are important for accurate diagnosis, it is important to query the KG to provide interactive explanations about learned biomarkers. Inspired by these, we construct a domain-specific KG particularly for cancer-specific biomarker discovery. The KG is constructed by integrating cancerrelated knowledge and facts from multiple sources. First, we construct a domain-specific ontology, which we call OncoNet Ontology (ONO). The ONO ontology is developed to enable semantic reasoning for verification of the predictions for relations between diseases and genes. The KG is then developed and enriched by harmonizing the ONO, additional metadata schemas, ontologies, controlled vocabularies, and additional concept from external sources using a BERT-based information extraction method. BioBERT and SciBERT are finetuned with the selected articles crawled from the PubMed. We listed down some queries and some examples of QA and deducing knowledge based on the KG.

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Section: Discussionmentioning

confidence: 99%

Section: Using the Knowledge Graphmentioning

confidence: 99%

“…6: Properties of TP53 biomarker show how it is related to different types of cancer. Dashed lines indicate literals andannotations from DOID, OGG, and external sources[38]…”

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A Biomedical Knowledge Graph for Biomarker Discovery in Cancer

Karim¹,

Comet²,

Beyan³

et al. 2023

Preprint

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Machine Learning and Artificial Intelligence in Bioinformatics

Shukla,

Singh,

Mani

2024

Unraveling New Frontiers and Advances in Bioinformatics

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Training data composition determines machine learning generalization and biological rule discovery

Ursu,

Minnegalieva,

Rawat

et al. 2024

Preprint

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Supervised machine learning models rely on training datasets with positive (target class) and negative examples. Therefore, the composition of the training dataset has a direct influence on model performance. Specifically, negative sample selection bias, concerning samples not representing the target class, presents challenges across a range of domains such as text classification and protein-protein interaction prediction. Machine-learning-based immunotherapeutics design is an increasingly important area of research, focusing on designing antibodies or T-cell receptors (TCRs) that can bind to their target molecules with high specificity and affinity. Given the biomedical importance of immunotherapeutics, there is a need to address the unresolved question of how negative training set composition impacts model generalization and biological rule discovery to enable rational and safe drug design. We set out to study this question in the context of the antibody-antigen binding prediction problem by varying the negative class, encompassing a binding affinity gradient. We based our investigation on large synthetic datasets that provide ground truth structure-based antibody-antigen binding data, allowing access to residue-wise binding energy on the binding interface. We found that both out-of-distribution generalization and binding rule discovery depended on the type of negative dataset used. Importantly, we discovered that a model’s capacity to learn the binding rules of the positive dataset is not a trivial correlate of its classification accuracy. We confirmed our findings with real-world relevant experimental data. Our work highlights the importance of considering training dataset composition for achieving optimal out-of-distribution performance and rule learning in machine-learning-based research.Significance StatementThe effectiveness of supervised machine learning models hinges on the composition of their training datasets, particularly the inclusion of negative examples. This bias in negative sample selection can greatly impact model performance. As the development of immunotherapeutic agents using machine learning is becoming increasingly crucial in biomedicine, understanding the impact of negative training set composition is imperative. Our study, focused on the antibody-antigen binding prediction problem, reveals that the choice of negative dataset significantly affects both out-of-distribution generalization and binding rule discovery across synthetic and experimental data. These findings underscore the necessity of carefully considering training dataset composition in machine-learning-driven biomedical research for optimal performance, robustness and meaningful rule acquisition.

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Explainable AI for Bioinformatics: Methods, Tools, and Applications

Cited by 3 publications

References 0 publications

A Biomedical Knowledge Graph for Biomarker Discovery in Cancer

A Biomedical Knowledge Graph for Biomarker Discovery in Cancer

Machine Learning and Artificial Intelligence in Bioinformatics

Training data composition determines machine learning generalization and biological rule discovery

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