A5059970218- Autism Spectrum Disorder Research
- Child Development and Digital Technology
- Virology and Viral Diseases
- Genomics and Phylogenetic Studies
- Gut microbiota and health
- Mobile Crowdsensing and Crowdsourcing
- Mobile Health and mHealth Applications
- Emotion and Mood Recognition
- Genetics and Neurodevelopmental Disorders
- Cytomegalovirus and herpesvirus research
- Assistive Technology in Communication and Mobility
- Viral-associated cancers and disorders
- Machine Learning in Bioinformatics
- Genomic variations and chromosomal abnormalities
- Domain Adaptation and Few-Shot Learning
- SARS-CoV-2 and COVID-19 Research
- Infant Health and Development
- RNA and protein synthesis mechanisms
- Cytokine Signaling Pathways and Interactions
- Tracheal and airway disorders
- Digital Mental Health Interventions
- HIV Research and Treatment
- Probiotics and Fermented Foods
- Cancer Genomics and Diagnostics
- COVID-19 diagnosis using AI
Stanford University
2018-2023
University of Nevada, Reno
2022-2023
Bioengineering Center
2020-2022
Palo Alto University
2019-2020
Yale University
2020
Abstract The unmapped readspace of whole genome sequencing data tends to be large but is often ignored. We posit that it contains valuable signals both human infection and contamination. Using poorly aligned reads from sequences (WGS) over 1000 families nearly 5000 individuals, we present insights into common viral, bacterial, computational contamination plague studies. several notable results: (1) In addition known contaminants such as Epstein-Barr virus phiX, blood lymphocyte cell lines...
Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health medicine. In this study, we evaluate the capability potential crowd virtual workers—defined as vetted members popular crowdsourcing platforms—to aid in task diagnosing autism. We workers when providing categorical ordinal behavioral ratings to unstructured public YouTube videos children with autism neurotypical controls. To emerging patterns that are consistent across independent crowds, target from...
Activity recognition computer vision algorithms can be used to detect the presence of autism-related behaviors, including what are termed "restricted and repetitive behaviors", or stimming, by diagnostic instruments. Examples stimming include hand flapping, spinning, head banging. One most significant bottlenecks for implementing such classifiers is lack sufficiently large training sets human behavior specific pediatric developmental delays. The data that do exist usually recorded with a...
Background Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires clinical observation, often result in long waiting times results. Recent advances computer vision mobile technology hold speeding up the process by enabling computational analysis behavioral social impairments from home videos. Such techniques can improve objectivity contribute...
Background A formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies detect the presence of behaviors related scale access pediatric diagnoses. strong indicator is self-stimulatory such as hand flapping. Objective This study aims demonstrate feasibility deep learning detection flapping from unstructured...
Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision recognition models are trained on adult and therefore underperform when applied child faces.
Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed year and be prohibitively expensive. Crowdsourcing approaches may provide scalable alternative accelerate general access to care permit underserved populations obtain an accurate diagnosis.
The evolutionary dynamics of SARS-CoV-2 have been carefully monitored since the COVID-19 pandemic began in December 2019. However, analysis has focused primarily on single nucleotide polymorphisms and largely ignored role insertions deletions (indels) as well recombination evolution. Using sequences from GISAID database, we catalogue over 100 consensus sequences. We hypothesize that these indels are artifacts events between replicates whereby RNA-dependent RNA polymerase (RdRp) re-associates...
Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd non-experts can efficiently annotate behavioral features needed for accurate machine learning detection common childhood developmental disorder Autism Spectrum Disorder (ASD) children under 8 years old. implement novel process identifying and certifying distributed workforce video feature...
Abstract Background Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups variants working concert produce phenotype. CGT has been applied associate likely-gene-disrupting encoded from whole genome sequence data ASD;...
Artificial Intelligence (A.I.) solutions are increasingly considered for telemedicine. For these methods to serve children and their families in home settings, it is crucial ensure the privacy of child parent or caregiver. To address this challenge, we explore potential global image transformations provide while preserving quality behavioral annotations. Crowd workers have previously been shown reliably annotate features unstructured videos, allowing machine learning classifiers detect...
While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due differential bias among profiling techniques. By simultaneously integrating multiple methods, multi-omic analysis can define generalizable processes, and is especially useful in understanding complex conditions such as Autism. Challenges with heterogeneous data produced by methods be overcome using Latent Dirichlet Allocation (LDA), a promising natural language...
Most patients who develop heart failure are unable to elevate their cardiac output on demand due impaired contractility and/or reduced ventricular filling. Despite decades of research, few effective therapies for have been developed. In part, this may reflect the difficulty predicting how perturbations molecular-level mechanisms that induced by drugs will scale up modulate system-level properties such as blood pressure. Computer modeling might help with process and thereby accelerate...
Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision recognition models are trained on adult and therefore underperform when applied child faces. Objective: We designed a strategy gamify the collection labeling of emotion-enriched images boost performance automatic level closer what will be needed for digital health care approaches. Methods:...
Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of error rates is essential if they are to be used guide patient care. However, platforms and variant-calling pipelines continuously evolving, making it difficult accurately quantify for particular combination assay software parameters on each sample. Family data provide a unique opportunity estimating since allows us observe fraction errors as Mendelian in family, which we can then use...
ABSTRACT Standard medical diagnosis of mental health conditions often requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd non-experts can efficiently label features needed for accurate machine learning detection common childhood developmental disorder autism. implement novel process creating distributed workforce video feature extraction, selecting 102 workers from pool 1,107. Two previously validated...
Autism spectrum disorder (ASD) has a strong male bias, with four times as many affected males females. ASD is hypothesized to follow polygenic disease model. While prior literature linked several genes the disorder, specific genetic causes and inheritance methods underlying condition are still widely unknown. Here, we investigate two popular theories of that could account for preponderance ASD: multiple-threshold model in which females must have higher burden order be affected, sex-specific...
Large, whole-genome sequencing (WGS) data sets containing families provide an important opportunity to identify crossovers and shared genetic material in siblings. However, the high variant calling error rates of WGS some areas genome can result spurious crossover calls, special inheritance status X Chromosome presents challenges. We have developed a hidden Markov model that addresses these issues by modeling variants presence error-prone regions inherited deletions. call our method...