Objective: Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them.
Materials And Methods: Using the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we train deep neural network models to predict the onset of 6 endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single-task (ST) models with naive multitask and SeqSNR in terms of discriminative performance and label efficiency.
Results: SeqSNR showed a modest yet statistically significant performance boost across 4 of 6 tasks compared with ST and naive multitasking. When the size of the training dataset was reduced for a given task (label efficiency), SeqSNR outperformed ST for all cases showing an average area under the precision-recall curve boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels, respectively.
Conclusions: The SeqSNR architecture shows superior label efficiency compared with ST and naive multitasking, suggesting utility in scenarios in which endpoint labels are difficult to ascertain.
To better understand host cell protein (HCP) retention in adeno-associated virus (AAV) downstream processes, sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS) was used to quantitatively profile residual HCPs for four AAV serotypes (AAV2, -5, -8, and -9) produced with HEK293 cells and purified using POROS CaptureSelect AAVX affinity chromatography. A broad range of residual HCPs were detected in affinity eluates after purification ( = 2,746), and HCP profiles showed universally present species ( = 1,117) and species unique to one or more AAV serotype. SWATH-MS revealed that HCP persistence was dominated by high-abundance conserved species (HACS), which appeared across all serotype conditions studied.
Neural circuits in the brain perform a variety of essential functions, including input classification, pattern completion, and the generation of rhythms and oscillations that support processes such as breathing and locomotion [51]. There is also substantial evidence that the brain encodes memories and processes information via of neural activity. In this dissertation, we are focused on the general problem of how neural circuits encode rhythmic activity, as in central pattern generators (CPGs), as well as the encoding of sequences.
* A comprehensive analysis, including differential gene expression and network construction, identified a key cluster of 57 genes that significantly impacts PDAC, which was validated for its potential diagnostic use and immune modulation effects.
* Findings indicate that BBR likely targets several important genes, primarily affecting epithelial cells and macrophages, with mechanisms involving immune responses, particularly the polarization of macrophages and interactions with specific immune cell types.
All mammals exhibit flexible decision policies that depend, at least in part, on the cortico-basal ganglia-thalamic (CBGT) pathways. Yet understanding how the complex connectivity, dynamics, and plasticity of CBGT circuits translates into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we used a computational approach to address this problem.
Deep learning models have achieved remarkable success in medical image classification. These models are typically trained once on the available annotated images and thus lack the ability of continually learning new tasks (i.e.
