Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation

Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with limited observational datasets. To address this issue, we propose a novel deep learning-based framework to train CATE learners that facilitates dynamic end-to-end information sharing among treatment groups. The framework is based on \textit{soft weight sharing} of \textit{hypernetworks}, which offers advantages such as parameter efficiency, faster training, and improved results. The proposed framework complements existing CATE learners and introduces a new class of uncertainty-aware CATE learners that we refer to as \textit{HyperCATE}. We develop HyperCATE versions of commonly used CATE learners and evaluate them on IHDP, ACIC-2016, and Twins benchmarks. Our experimental results show that the proposed framework improves the CATE estimation error via counterfactual inference, with increasing effectiveness for smaller datasets

A brief review of hypernetworks in deep learning

Hypernetworks, or hypernets in short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression etc. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning etc. Despite their success across different problem settings, currently, there is no review available to inform the researchers about the developments and to help in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example to train deep neural networks using hypernets and propose categorizing hypernets based on five design criteria as inputs, outputs, variability of inputs and outputs, and architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain under-explored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks

A brief review of hypernetworks in deep learning

Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hypernets have shown promising results in a variety of deep learning problems, includ- ing continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. Despite their success across different problem settings, there is currently no comprehensive review available to inform researchers about the latest developments and to assist in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria: inputs, outputs, variability of inputs and outputs, and the architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain underexplored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks

A brief review of hypernetworks in deep learning

Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. Despite their success across different problem settings, there is currently no comprehensive review available to inform researchers about the latest developments and to assist in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria: inputs, outputs, variability of inputs and outputs, and the architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain underexplored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks

GAGrank: Software for Glycosaminoglycan Sequence Ranking using a Bipartite Graph Model

The Sulfated Glycosaminoglycans (GAGs) Are Long, Linear Polysaccharide Chains that Are Typically Found as the Glycan Portion of Proteoglycans. These GAGs Are Characterized by Repeating Disaccharide Units with Variable Sulfation and Acetylation Patterns Along the Chain. GAG Length and Modification Patterns Have Profound Impacts on Growth Factor Signaling Mechanisms Central to Numerous Physiological Processes. Electron Activated Dissociation Tandem Mass Spectrometry is a Very Effective Technique for Assigning the Structures of GAG Saccharides; However, Manual Interpretation of the Resulting Complex Tandem Mass Spectra is a Difficult and Time-Consuming Process that Drives the Development of Computational Methods for Accurate and Efficient Sequencing. We Have Recently Published GAGfinder, the First Peak Picking and Elemental Composition Assignment Algorithm Specifically Designed for GAG Tandem Mass Spectra. Here, We Present GAGrank, a Novel Network-Based Method for Determining GAG Structure using Information Extracted from Tandem Mass Spectra using GAGfinder. GAGrank is based on Google\u27s PageRank Algorithm for Ranking Websites for Search Engine Output. in Particular, It is an Implementation of BiRank, an Extension of PageRank for Bipartite Networks. in Our Implementation, the Two Partitions Comprise Every Possible Sequence for a Given GAG Composition and the Tandem MS Fragments Found using GAGfinder. Sequences Are Given a Higher Ranking If They Link to Many Important Fragments. using the Simulated Annealing Probabilistic Optimization Technique, We Optimized GAGrank\u27s Parameters on Ten Training Sequences. We Then Validated GAGrank\u27s Performance on Three Validation Sequences. We Also Demonstrated GAGrank\u27s Ability to Sequence Isomeric Mixtures using Two Mixtures at Five Different Ratios

Evolution of the Surface Structures on SrTiO3_3(110) Tuned by Ti or Sr Concentration

The surface structure of the SrTiO$_3$(110) polar surface is studied by scanning tunneling microscopy and X-ray photoelectron spectroscopy. Monophased reconstructions in (5$\times$1), (4$\times$1), (2$\times$8), and (6$\times$8) are obtained, respectively, and the evolution between these phases can be tuned reversibly by adjusting the Ar$^{+}$ sputtering dose or the amount of Sr/Ti evaporation. Upon annealing, the surface reaches the thermodynamic equilibrium that is determined by the surface metal concentration. The different electronic structures and absorption behaviors of the surface with different reconstructions are investigated.Comment: 10 pages, 14 figure

Characterization and Quantification of Highly Sulfated Glycosaminoglycan Isomers by Gated-Trapped Ion Mobility Spectrometry Negative Electron Transfer Dissociation MS/MS

Glycosaminoglycans (GAGs) Play Vital Roles in Many Biological Processes and Are Naturally Present as Complex Mixtures of Polysaccharides with Tremendous Structural Heterogeneity, Including Many Structural Isomers. Mass Spectrometric Analysis of GAG Isomers, in Particular Highly Sulfated Heparin (Hep) and Heparan Sulfate (HS), is Challenging Because of their Structural Similarity and Facile Sulfo Losses during Analysis. Herein, We Show that Highly Sulfated Hep/HS Isomers May Be Resolved by Gated-Trapped Ion Mobility Spectrometry (Gated-TIMS) with Negligible Sulfo Losses. Subsequent Negative Electron Transfer Dissociation (NETD) Tandem Mass Spectrometry (MS/MS) Analysis of TIMS-Separated Hep/HS Isomers Generated Extensive Glycosidic and Cross-Ring Fragments for Confident Isomer Differentiation and Structure Elucidation. the High Mobility Resolution and Preservation of Labile Sulfo Modifications Afforded by Gated-TIMS MS Analysis Also Allowed Relative Quantification of Highly Sulfated Heparin Isomers. These Results Show that the Gated-TIMS-NETD MS/MS Approach is Useful for Both Qualitative and Quantitative Analysis of Highly Sulfated Hep/HS Compounds in a Manner Not Possible with Other Techniques

Temporal order of RNase IIIb and loss-of-function mutations during development determines phenotype in DICER1 syndrome: a unique variant of the two-hit tumor suppression model [v1; ref status: approved with reservations 1, http://f1000r.es/5l9]

Pleuropulmonary blastoma (PPB) is the most frequent pediatric lung tumor and often the first indication of a pleiotropic cancer predisposition, DICER1 syndrome, comprising a range of other individually rare, benign and malignant tumors of childhood and early adulthood. The genetics of DICER1-associated tumorigenesis are unusual in that tumors typically bear neomorphic missense mutations at one of five specific “hotspot” codons within the RNase IIIb domain of DICER 1, combined with complete loss of function (LOF) in the other allele. We analyzed a cohort of 124 PPB children for predisposing DICER1 mutations and sought correlations with clinical phenotypes. Over 70% have inherited or de novo germline LOF mutations, most of which truncate the DICER1 open reading frame. We identified a minority of patients who have no germline mutation, but are instead mosaic for predisposing DICER1 mutations. Mosaicism for RNase IIIb domain hotspot mutations defines a special category of DICER1 syndrome patients, clinically distinguished from those with germline or mosaic LOF mutations by earlier onsets and numerous discrete foci of neoplastic disease involving multiple syndromic organ sites. A final category of patients lack predisposing germline or mosaic mutations and have disease limited to a single PPB tumor bearing tumor-specific RNase IIIb and LOF mutations. We propose that acquisition of a neomorphic RNase IIIb domain mutation is the rate limiting event in DICER1-associated tumorigenesis, and that distinct clinical phenotypes associated with mutational categories reflect the temporal order in which LOF and RNase IIIb domain mutations are acquired during development

Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification

Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available