Numerous studies have been performed over the last decade to exploit the complexity of genomic and transcriptomic lesions driving the initiation of acute myeloid leukemia (AML). These studies have helped improve risk classification and treatment options. Detailed molecular characterization of longitudinal AML samples is sparse, however; meanwhile, relapse and therapy resistance represent the main challenges in AML care. To this end, we performed transcriptome-wide RNA sequencing of longitudinal diagnosis, relapse, and/or primary resistant samples from 47 adult and 23 pediatric AML patients with known mutational background. Gene expression analysis revealed the association of short event-free survival with overexpression of GLI2 and IL1R1, as well as downregulation of ST18. Moreover, CR1 downregulation and DPEP1 upregulation were associated with AML relapse both in adults and children. Finally, machine learning–based and network-based analysis identified overexpressed CD6 and downregulated INSR as highly copredictive genes depicting important relapse-associated characteristics among adult patients with AML. Our findings highlight the importance of a tumor-promoting inflammatory environment in leukemia progression, as indicated by several of the herein identified differentially expressed genes. Together, this knowledge provides the foundation for novel personalized drug targets and has the potential to maximize the benefit of current treatments to improve cure rates in AML.

Background: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. Results: We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA. To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case–control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. Conclusions: R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

Single-cell data is increasingly used for gene regulatory network (GRN) inference, and benchmarks for this have been developed based on simulated data. However, existing single-cell simulators cannot model the effects of gene perturbations. A further challenge lies in generating large-scale GRNs that often struggle with computational and stability issues. We present GeneSPIDER2, an update of the GeneSPIDER MATLAB toolbox for GRN benchmarking, inference, and analysis. Several software modules have improved capabilities and performance, and new functionalities have been added. A major improvement is the ability to generate large GRNs with biologically realistic topological properties in terms of scale-free degree distribution and modularity. Another major addition is a simulation of single-cell data, which is becoming increasingly popular as input for GRN inference. Specifically, we introduced the unique feature to generate single-cell data based on genetic perturbations. Finally, the simulated single-cell data was compared to real single-cell Perturb-seq data from two cell lines, showing that the synthetic and real data exhibit similar properties.

Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.

Motivation: For machine learning to matter beyond intellectual curiosity, the models developed therefrom must be adopted within the greater scientific community. In this study, we developed an interpretable machine learning framework that allows identification of semantics from various datatypes. Our package can analyze and illuminate co-predictive mechanisms reflecting biological processes. Results: We present R.ROSETTA, an R package for building and analyzing interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. Investigating case-control studies of autism, we showed that our tool provided hypotheses for potential interdependencies among features that discerned phenotype classes. These interdependencies regarded neurodevelopmental and autism-related genes. Although our sample application of R.ROSETTA was used for transcriptomic data analysis, R.ROSETTA works perfectly with any decision-related omics data. Availability: The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . Contact: mateusz.garbulowski@icm.uu.se (Mateusz Garbulowski), jan.komorowski@icm.uu.se (Jan Komorowski)