Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

Autism spectrum disorder (ASD) research has yet to leverage big data on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data

Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations

Personalized medicine is a concept that has been subject of increasing interest in medical research and practice in the last few years. However, significant challenges stand in the way of practical implementations, namely in regard to extracting clinically valuable insights from the vast amount of biomedical knowledge generated in the last few years. Here, we describe an approach that uses Knowledge Graph Embedding (KGE) methods on a biomedical Knowledge Graph (KG) as a path to reasoning over the wealth of information stored in publicly accessible databases. We built a Knowledge Graph using data from DisGeNET and GO, containing relationships between genes, diseases and other biological entities. The KG contains 93,657 nodes of 5 types and 1,705,585 relationships of 59 types. We applied KGE methods to this KG, obtaining an excellent performance in predicting gene-disease associations (MR 0.13, MRR 0.96, HITS@1 0.93, HITS@3 0.99, and HITS@10 0.99). The optimal hyperparameter set was used to predict all possible novel gene-disease associations. An in-depth analysis of novel gene-disease predictions for disease terms related to Autism Spectrum Disorder (ASD) shows that this approach produces predictions consistent with known candidate genes and biological pathways and yields relevant insights into the biology of this paradigmatic complex disorder.Fundação para a Ciência e a Tecnologia, Grant/Award Numbers: SAICTPAC/0010/2015, POCI- 01-0145-FEDER-016428-PAC, EXPL/CCI-BIO/0126/2021, PTDC/MED-OUT/28937/2017, UIDP/04046/2020, UIDB/04046/2020; Fundo Europeu de Desenvolvimento Regional, Grant/Award Number: 022153info:eu-repo/semantics/publishedVersio

Estimating the family bias to autism: a bayesian approach

Autism is an age- and sex-related lifelong neurodevelopmental condition characterized pri marily by persistent deficits in core domains such as social communication. It is estimated that ≈ 2% of children have some ASD trait. The autism etiology is mainly due to inherited genetic factors (>80%). The importance of early diagnosis and interventions motivated several studies involving groups at high risk for ASD, those with a greater predisposition to the disorder. Such studies are characterized by evaluating some characteristics of the individual itself or the family members of diagnosed individuals, mainly aiming to predict a future diagnosis or recurrence rates. One of the primary goals of Artificial Intelligence is to create artificial agents capable of intelligent behaviors, such as prediction problems. Prediction problems usually involve reasoning with uncertainty due to some information deficiency, in which the data may be imprecise or incorrect. Such solutions may seek the application of probabilistic methods to construct inference models. In this thesis, we will discuss the development of probabilistic networks capable of estimating the risk of autism among the family members given some evidence (e.g., other family members with ASD). In particular, the main novel contributions of this thesis are as follows: the proposal of some estimates regarding parents with ASD generating children with ASD; the highlight ing regarding the decrease in the ASD prevalence sex ratio among males and females when genetic factors are taken into account; the corroboration and quantification of past evidence that the clustering of ASD in families is primarily due to genetic factors; the computation of some estimates regarding the risk of ASD for parents, grandparents, and siblings; an estimate regarding the number of ASD cases in a family sufficient to attribute the ASD occurrences to the genetic inheritance; the assessment of some estimates for males and females individuals given evidence in grandparents, aunts-or-uncles, nieces-or nephews and cousins; and the proposition of some estimates indicating risk ranges for ASD by genetic similarity

Elucidating the cellular dynamics of the brain with single-cell RNA sequencing

Single-cell RNA-sequencing (scRNA-seq) has emerged in recent years as a breakthrough technology to understand RNA metabolism at cellular resolution. In addition to allowing new cell types and states to be identified, scRNA-seq can permit cell-type specific differential gene expression changes, pre-mRNA processing events, gene regulatory networks and single-cell developmental trajectories to be uncovered. More recently, a new wave of multi-omic adaptations and complementary spatial transcriptomics workflows have been developed that facilitate the collection of even more holistic information from individual cells. These developments have unprecedented potential to provide penetrating new insights into the basic neural cell dynamics and molecular mechanisms relevant to the nervous system in both health and disease. In this review we discuss this maturation of single-cell RNA-sequencing over the past decade, and review the different adaptations of the technology that can now be applied both at different scales and for different purposes. We conclude by highlighting how these methods have already led to many exciting discoveries across neuroscience that have furthered our cellular understanding of the neurological disease

An empirical and computational investigation of variable outcomes in Autism Spectrum Disorder

This thesis had two aims. The first was to investigate variability observed in the profiles of young children with autism spectrum disorder (ASD), and our ability to predict this variability based on measures in infancy. The second aim was to identify the underlying mechanisms that generate this variability. I combined analyses of clinical data sets and data from computational models to investigate the influences shaping atypical developmental trajectories in ASD. The first aim was addressed using secondary data analysis from a prospective longitudinal dataset, the British Autism Study of Infant Siblings. Clinical, behavioural, and parental report data were collected on 54 infants at risk of ASD (by virtue of having an older sibling with the disorder) and 50 low-risk controls at 7, 14, 24 and 36 months. Chapter 2 investigates whether variability differed at a group level, evaluating whether heterogeneity was exaggerated in highrisk groups versus low-risk controls. Cognitive variability scores distinguished infants with ASD at 36 months. Intra-subject variability was then assessed. A more uneven cognitive profile at 24 months was predictive of lower cognitive abilities at 36 months in high-risk infants overall. In Chapter 3, behavioural measures at 14 months were identified as predictors of diagnostic outcome at 36 months in high-risk infants. Initial results highlighted the importance of environmental factors and social and communicative performance. The predictive power of the subsequent statistical regression equations was validated against recently available data from Phase 2 of the BASIS study, with 125 at-risk infants, demonstrating 71% specificity and 81% sensitivity in predicting ASD characteristics at 24 months. In the second half of the thesis, potential mechanisms generating variability in ASD behavioural profiles were investigated via computational modelling. Thomas, Knowland and Karmiloff-Smith (2011) developed a computational model targeting the regressive sub-type of autism based on the hypothesis that regression could be caused by Over-Pruning of brain connectivity. In Chapter 4, this model is extended to capture other observed developmental trajectories in ASD. Regressive and non-regressive subgroups were identified, and each was reliably distinguished by a distinct pattern of neurocomputational parameters. Regression and early onset of pruning were indicative of poorer developmental outcomes overall. Non-regressive subgroups, both typical and atypical, were then used to investigate response to remediation via behavioural intervention. The simulation work represents the first application of populationlevel models of atypical development to intervention. Small but reliable intervention effects were identified, following a discrete phase of intervention. However, the results indicated a limited scope to intervene, with the greater success using compensatory rather than normalisation techniques. The overall results are discussed with reference to the need for convergent methods to shed light on the constraints shaping atypical developmental trajectories in ASD