An automatic integrative method for learning interpretable communities of biological pathways.
Ontology highlight
ABSTRACT:
SUBMITTER: Beebe-Wang N
PROVIDER: S-EPMC9228877 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIdentifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic diversity and develop human-like lung granulomas when infected with Mycobacterium tuberculosis (M.tb) .MethodsFollowing M.tb infection, a "supersusceptible" phenotype develops in approximately one-third of DO mice characterized by rapid morbidity and mortality within 8 weeks. These supersusceptible DO mice develop lung granulomas patterns akin to humans. This led us to utilize deep learning to identify supersusceptibility from hematoxylin & eosin (H&E) lung tissue sections utilizing only clinical outcomes (supersusceptible or not-supersusceptible) as labels.FindingsThe proposed machine learning model diagnosed supersusceptibility with high accuracy (91.50 ± 4.68%) compared to two expert pathologists using H&E stained lung sections (94.95% and 94.58%). Two non-experts used the imaging biomarker to diagnose supersusceptibility with high accuracy (88.25% and 87.95%) and agreement (96.00%). A board-certified veterinary pathologist (GB) examined the imaging biomarker and determined the model was making diagnostic decisions using a form of granuloma necrosis (karyorrhectic and pyknotic nuclear debris). This was corroborated by one other board-certified veterinary pathologist. Finally, the imaging biomarker was quantified, providing a novel means to convert visual patterns within granulomas to data suitable for statistical analyses.ImplicationsOverall, our results have translatable implication to improve our understanding of TB and also to the broader field of computational pathology in which clinical outcomes alone can drive automatic identification of interpretable imaging biomarkers, knowledge discovery, and validation of existing clinical biomarkers.FundingNational Institutes of Health and American Lung Association.
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Project description:In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
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Project description:BackgroundWilliams syndrome (WS) is a neurodevelopmental disorder that has been attributed to heterozygous deletions in chromosome 7q11.23 and exhibits a variety of physical, cognitive, and behavioral features. However, the genetic basis of this phenotypic variability is unclear. In this study, we identified genetic clues underlying these complex phenotypes.MethodsNeurobehavioral function was assessed in WS patients and healthy controls. Total RNA was extracted from peripheral blood and subjected to microarray analysis, RNA-sequencing, and qRT-PCR. Weighted gene co-expression network analysis was performed to identify specific alterations related to intermediate disease phenotypes. To functionally interpret each WS-related module, gene ontology and disease-related gene enrichment were examined. We also investigated the micro (mi)RNA expression profiles and miRNA co-expression networks to better explain the regulation of the transcriptome in WS.ResultsOur analysis identified four significant co-expression modules related to intermediate WS phenotypes. Notably, the three upregulated WS-related modules were composed exclusively of genes located outside the 7q11.23 region. They were significantly enriched in genes related to B-cell activation, RNA processing, and RNA transport. BCL11A, which is known for its association with speech disorders and intellectual disabilities, was identified as one of the hub genes in the top WS-related module. Finally, these key upregulated mRNA co-expression modules appear to be inversely correlated with a specific downregulated WS-related miRNA co-expression module.ConclusionsDysregulation of the mRNA/miRNA network involving genes outside of the 7q11.23 region is likely related to the complex phenotypes observed in WS patients.
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Project description:In recent years, deep learning has seen remarkable progress in many fields, especially with many excellent pre-training models emerged in Natural Language Processing(NLP). However, these pre-training models can not be used directly in music generation tasks due to the different representations between music symbols and text. Compared with the traditional presentation method of music melody that only includes the pitch relationship between single notes, the text-like representation method proposed in this paper contains more melody information, including pitch, rhythm and pauses, which expresses the melody in a form similar to text and makes it possible to use existing pre-training models in symbolic melody generation. In this paper, based on the generative pre-training-2(GPT-2) text generation model and transfer learning we propose MT-GPT-2(music textual GPT-2) model that is used in music melody generation. Then, a symbolic music evaluation method(MEM) is proposed through the combination of mathematical statistics, music theory knowledge and signal processing methods, which is more objective than the manual evaluation method. Based on this evaluation method and music theories, the music generation model in this paper are compared with other models (such as long short-term memory (LSTM) model,Leak-GAN model and Music SketchNet). The results show that the melody generated by the proposed model is closer to real music.
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Project description:Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.
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Project description:BACKGROUND:Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs. METHODS:In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori. RESULTS:We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis. CONCLUSIONS:The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.
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Project description:High-throughput technologies have led to large collections of different types of biological data that provide unprecedented opportunities to unravel molecular heterogeneity of biological processes. Nevertheless, how to jointly explore data from multiple sources into a holistic, biologically meaningful interpretation remains challenging. In this work, we propose a scalable and tuning-free preprocessing framework, Heterogeneity Rescaling Pursuit (Hetero-RP), which weighs important features more highly than less important ones in accord with implicitly existing auxiliary knowledge. Finally, we demonstrate effectiveness of Hetero-RP in diverse clustering and classification applications. More importantly, Hetero-RP offers an interpretation of feature importance, shedding light on the driving forces of the underlying biology. In metagenomic contig binning, Hetero-RP automatically weighs abundance and composition profiles according to the varying number of samples, resulting in markedly improved performance of contig binning. In RNA-binding protein (RBP) binding site prediction, Hetero-RP not only improves the prediction performance measured by the area under the receiver operating characteristic curves (AUC), but also uncovers the evidence supported by independent studies, including the distribution of the binding sites of IGF2BP and PUM2, the binding competition between hnRNPC and U2AF2, and the intron-exon boundary of U2AF2 [availability: https://github.com/younglululu/Hetero-RP].
| S-EPMC5714251 | biostudies-literature