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Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and is underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here we developed a phenotypic stratification model that makes highly accurate (96-98%) out-of-sample SC=RRB, SC>RRB, and RRB>SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n=509), we find replicable somatomotor-perisylvian hypoconnectivity in the SC>RRB subtype versus a typically-developing (TD) comparison group. In contrast, replicable motor-anterior salience hyperconnectivity is apparent in the SC=RRB subtype versus TD. Autism-associated genes affecting astrocytes, excitatory, and inhibitory neurons are highly expressed specifically within SC>RRB hypoconnected networks, but not SC=RRB hyperconnected networks. SC-RRB balance subtypes may indicate different paths individuals take from genome, neural circuitry, to the clinical phenotype.

This work aims at developing a novel machine learning method to investigate heterogeneity in neurodevelopmental disorders, with a focus on autism spectrum conditions (ASCs). In ASCs, heterogeneity is shown at several levels of analysis, e.g., genetic, behavioral, throughout developmental trajectories, which hinders the development of effective treatments and the identification of biological pathways involved in gene-cognition-behavior links. ASC diagnosis comes from behavioral observations, which determine the cohort composition of studies in every scientific field (e.g., psychology, neuroscience, genetics). Thus, uncovering behavioral subtypes can provide stratified ASC cohorts that are more representative of the true population. Ideally, behavioral stratification can (1) help to revise and shorten the diagnostic process highlighting the characteristics that best identify heterogeneity; (2) help to develop personalized treatments based on their effectiveness for subgroups of subjects; (3) investigate how the longitudinal course of the condition might differ (e.g., divergent/convergent developmental trajectories); (4) contribute to the identification of genetic variants that may be overlooked in case-control studies; and (5) identify possible disrupted neuronal activity in the brain (e.g., excitatory/inhibitory mechanisms). The characterization of the temporal aspects of heterogeneous manifestations based on their multi-dimensional features is thus the key to identify the etiology of such disorders and establish personalized treatments. Features include trajectories described by a multi-modal combination of electronic health records (EHRs), cognitive functioning and adaptive behavior indicators. This thesis contributes in particular to a data-driven discovery of clinical and behavioral trajectories of individuals with complex disorders and ASCs. Machine learning techniques, such as deep learning and word embedding, that proved successful for e.g., natural language processing and image classification, are gaining ground in healthcare research for precision medicine. Here, we leverage these methods to investigate the feasibility of learning data-driven pathways that have been difficult to identify in the clinical practice to help disentangle the complexity of conditions whose etiology is still unknown. In Chapter 1, we present a new computational method, based on deep learning, to stratify patients with complex disorders; we demonstrate the method on multiple myeloma, Alzheimer?s disease, and Parkinson?s disease, among others. We use clinical records from a heterogeneous patient cohort (i.e., multiple disease dataset) of 1.6M temporally-ordered EHR sequences from the Mount Sinai health system?s data warehouse to learn unsupervised patient representations. These representations are then leveraged to identify subgroups within complex condition cohorts via hierarchical clustering. We investigate the enrichment of terms that code for comorbidities, medications, laboratory tests and procedures, to clinically validate our results. A data analysis protocol is developed in Chapter 2 that produces behavioral embeddings from observational measurements to represent subjects with ASCs in a latent space able to capture multiple levels of assessment (i.e., multiple tests) and the temporal pattern of behavioral-cognitive profiles. The computational framework includes clustering algorithms and state-of-the-art word and text representation methods originally developed for natural language processing. The aim is to detect subgroups within ASC cohorts towards the identification of possible subtypes based on behavioral, cognitive, and functioning aspects. The protocol is applied to ASC behavioral data of 204 children and adolescents referred to the Laboratory of Observation Diagnosis and Education (ODFLab) at the University of Trento. In Chapter 3 we develop a case study for ASCs. From the learned representations of Chapter 1, we select 1,439 individuals with ASCs and investigate whether such representations generalize well to any disorder. Specifically, we identify three subgroups within individuals with ASCs that are further clinically validated to detect clinical profiles based on different term enrichment that can inform comorbidities, therapeutic treatments, medication side effects, and screening policies. This work has been developed in partnership with ODFLab (University of Trento) and the Predictive Models for Biomedicine and Environment unit at FBK. The study reported in Chapter 1 has been conducted at the Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai (NY).

Convolutional Neural Networks (CNNs) are a popular deep learning architecture widely applied in different domains, in particular in classifying over images, for which the concept of convolution with a filter comes naturally. Unfortunately, the requirement of a distance (or, at least, of a neighbourhood function) in the input feature space has so far prevented its direct use on data types such as omics data. However, a number of omics data are metrizable, i.e., they can be endowed with a metric structure, enabling to adopt a convolutional based deep learning framework, e.g., for prediction. We propose a generalized solution for CNNs on omics data, implemented through a dedicated Keras layer. In particular, for metagenomics data, a metric can be derived from the patristic distance on the phylogenetic tree. For transcriptomics data, we combine Gene Ontology semantic similarity and gene co-expression to define a distance; the function is defined through a multilayer network where 3 layers are defined by the GO mutual semantic similarity while the fourth one by gene co-expression. As a general tool, feature distance on omics data is enabled by OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a metagenomics collection of gut microbiota of 222 IBD patients., Comment: 7 pages, 3 figures. arXiv admin note: text overlap with arXiv:1709.02268

Convolutional Neural Networks (CNNs) are a popular deep learning architecture widely applied in different domains, in particular in classifying over images, for which the concept of convolution with a filter comes naturally. Unfortunately, the requirement of a distance (or, at least, of a neighbourhood function) in the input feature space has so far prevented its direct use on data types such as omics data. However, a number of omics data are metrizable, i.e., they can be endowed with a metric structure, enabling to adopt a convolutional based deep learning framework, e.g., for prediction. We propose a generalized solution for CNNs on omics data, implemented through a dedicated Keras layer. In particular, for metagenomics data, a metric can be derived from the patristic distance on the phylogenetic tree. For transcriptomics data, we combine Gene Ontology semantic similarity and gene co-expression to define a distance; the function is defined through a multilayer network where 3 layers are defined by the GO mutual semantic similarity while the fourth one by gene co-expression. As a general tool, feature distance on omics data is enabled by OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a metagenomics collection of gut microbiota of 222 IBD patients., 7 pages, 3 figures. arXiv admin note: text overlap with arXiv:1709.02268

ObjectiveTo externally validate Yonsei nomogram. MethodsFrom 2000 through 2018, 3526 consecutive patients underwent on-clamp PN for cT1 renal masses at 23 centers were included. All patients had two kidneys, preoperative eGFR >= 60 ml/min/1.73 m2, and a minimum follow-up of 12 months. New-onset CKD was defined as upgrading from CKD stage I or II into CKD stage >= III. We obtained the CKD-free progression probabilities at 1, 3, 5, and 10 years for all patients by applying the nomogram found at . Thereafter, external validation of Yonsei nomogram for estimating new-onset CKD stage >= III was assessed by calibration and discrimination analysis. Results and limitationMedian values of patients' age, tumor size, eGFR and follow-up period were 47 years (IQR: 47-62), 3.3 cm (IQR: 2.5-4.2), 90.5 ml/min/1.73 m2 (IQR: 82.8-98), and 47 months (IQR: 27-65), respectively. A total of 683 patients (19.4%) developed new-onset CKD. The 5-year CKD-free progression rate was 77.9%. Yonsei nomogram demonstrated an AUC of 0.69, 0.72, 0.77, and 0.78 for the prediction of CKD stage >= III at 1, 3, 5, and 10 years, respectively. The calibration plots at 1, 3, 5, and 10 years showed that the model was well calibrated with calibration slope values of 0.77, 0.83, 0.76, and 0.75, respectively. Retrospective database collection is a limitation of our study. ConclusionsThe largest external validation of Yonsei nomogram showed good calibration properties. The nomogram can provide an accurate estimate of the individual risk of CKD-free progression on long-term follow-up.