publications

2024

1. Unsupervised Human Activity Recognition Learning for Disassembly Tasks

   Xinyao Zhang, Daiyao Yi, Sara Behdad, and 1 more author

   IEEE Transactions on Industrial Informatics, 2024

2023

1. Disentangled multi-subject and social behavioral representations through a constrained subspace variational autoencoder (CS-VAE)

   Daiyao Yi, Simon Musall, Anne Churchland, and 2 more authors

   Jul 2023

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2. Harmonizing across datasets to improve the transferability of drug combination prediction

   Hanrui Zhang, Ziyan Wang, Yiyang Nan, and 4 more authors

   Communications Biology, Apr 2023
3. Motor deficit and lack of overt dystonia in Dlx conditional Dyt1 knockout mice

   David Berryman, Jake Barrett, Canna Liu, and 7 more authors

   Behavioural Brain Research, Apr 2023

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   DYT1 or DYT-TOR1A dystonia is early-onset generalized dystonia caused by a trinucleotide deletion of GAG in the TOR1A or DYT1 gene leads to the loss of a glutamic acid residue in the resulting torsinA protein. A mouse model with overt dystonia is of unique importance to better understand the DYT1 pathophysiology and evaluate preclinical drug efficacy. DYT1 dystonia is likely a network disorder involving multiple brain regions, particularly the basal ganglia. Tor1a conditional knockout in the striatum or cerebral cortex leads to motor deficits, suggesting the importance of corticostriatal connection in the pathogenesis of dystonia. Indeed, corticostriatal long-term depression impairment has been demonstrated in multiple targeted DYT1 mouse models. Pappas and colleagues developed a conditional knockout line (Dlx-CKO) that inactivated Tor1a in the forebrain and surprisingly displayed overt dystonia. We set out to validate whether conditional knockout affecting both cortex and striatum would lead to overt dystonia and whether machine learning-based video behavioral analysis could be used to facilitate high throughput preclinical drug screening. We generated Dlx-CKO mice and found no overt dystonia or motor deficits at 4 months. At 8 months, retesting revealed motor deficits in rotarod, beam walking, grip strength, and hyperactivity in the open field; however, no overt dystonia was visually discernible or through the machine learning-based video analysis. Consistent with other targeted DYT1 mouse models, we observed age-dependent deficits in the beam walking test, which is likely a better motor behavioral test for preclinical drug testing but more labor-intensive when overt dystonia is absent.

4. Neural Correlations Across Mice During Spontaneous and Task-Related Behaviors

   Daiyao Yi, and Shreya Saxena

   In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), Apr 2023

2022

1. Modeling the behavior of multiple subjects using a Cauchy-Schwarz regularized Partitioned Subspace Variational AutoEncoder (CS-PS-VAE)

   Daiyao Yi, and Shreya Saxena

   In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Apr 2022
2. Micro-dissection and integration of long and short reads to create a robust catalog of kidney compartment-specific isoforms

   Hongyang Li, Ridvan Eksi, Daiyao Yi, and 7 more authors

   PLOS Computational Biology, Apr 2022

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   Studying isoform expression at the microscopic level has always been a challenging task. A classical example is kidney, where glomerular and tubulo-interstitial compartments carry out drastically different physiological functions and thus presumably their isoform expression also differs. We aim at developing an experimental and computational pipeline for identifying isoforms at microscopic structure-level. We microdissected glomerular and tubulo-interstitial compartments from healthy human kidney tissues from two cohorts. The two compartments were separately sequenced with the PacBio RS II platform. These transcripts were then validated using transcripts of the same samples by the traditional Illumina RNA-Seq protocol, distinct Illumina RNA-Seq short reads from European Renal cDNA Bank (ERCB) samples, and annotated GENCODE transcript list, thus identifying novel transcripts. We identified 14,739 and 14,259 annotated transcripts, and 17,268 and 13,118 potentially novel transcripts in the glomerular and tubulo-interstitial compartments, respectively. Of note, relying solely on either short or long reads would have resulted in many erroneous identifications. We identified distinct pathways involved in glomerular and tubulo-interstitial compartments at the isoform level, creating an important experimental and computational resource for the kidney research community.

2021

1. Representation of molecules for drug response prediction

   Xin An, Xi Chen, Daiyao Yi, and 2 more authors

   Briefings in Bioinformatics, Sep 2021

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   The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure–activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.

2. A survival model generalized to regression learning algorithms

   Yuanfang Guan, Hongyang Li, Daiyao Yi, and 4 more authors

   Nature Computational Science, Jun 2021
3. Deep learning identifies erroneous microarray-based, gene-level conclusions in literature

   Yanan Qin, Daiyao Yi, Xianghao Chen, and 1 more author

   NAR Genomics and Bioinformatics, Oct 2021

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   More than 110 000 publications have used microarrays to decipher phenotype-associated genes, clinical biomarkers and gene functions. Microarrays rely on digital assaying the fluorescence signals of arrays. In this study, we retrospectively constructed raw images for 37 724 published microarray data, and developed deep learning algorithms to automatically detect systematic defects. We report that an alarming amount of 26.73\% of the microarray-based studies are affected by serious imaging defects. By literature mining, we found that publications associated with these affected microarrays have reported disproportionately more biological discoveries on the genes in the contaminated areas compared to other genes. 28.82\% of the gene-level conclusions reported in these publications were based on measurements falling into the contaminated area, indicating severe, systematic problems caused by such contaminations. We provided the identified published, problematic datasets, affected genes and the imputed arrays as well as software tools for scanning such contamination that will become essential to future studies to scrutinize and critically analyze microarray data.

4. Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records

   Hanrui Zhang, Daiyao Yi, and Yuanfang Guan

   STAR Protocols, Oct 2021

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   Summary The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pipeline which predicts outcomes from real-time sequential clinical data. The strategy implemented in Timesias is the first-place solution in the crowd-sourcing DII (discover, innovate, impact) National Data Science Challenge involving more than 100,000 patients, achieving 0.85 as evaluated by AUROC (area under receiver operator characteristic curve) in predicting the early onset of sepsis status. Timesias is freely available via PyPI and GitHub. For complete details on the use and execution of this protocol, please refer to Guan et al. (2021).