pwc-archive/papers-with-abstracts huggingface dataset:
Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models
Authors: Hongsong Feng, GuoWei Wei
Date: 2022-11-02 00:00:00
Tasks: Drug Discovery

The human {\it ether-a-go-go} (hERG) potassium channel (K$_\text{v}11.1$) plays a critical role in mediating cardiac action potential. The blockade of this ion channel can potentially lead fatal disorder and/or long QT syndrome. Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. It is crucial to assess the hERG blockade activity in the early stage of drug discovery. We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs. Machine learning-based in silico tools offer a rapid and economical platform to virtually screen DrugBank compounds. We design accurate and robust classifiers for blockers/non-blockers and then build regressors to quantitatively analyze the binding potency of the DrugBank compounds on the hERG channel. Molecular sequences are embedded with two natural language processing (NPL) methods, namely, autoencoder and transformer. Complementary three-dimensional (3D) molecular structures are embedded with two advanced mathematical approaches, i.e., topological Laplacians and algebraic graphs. With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBank compounds are potential hERG blockers, suggesting serious drug safety problems. Our predictions provide guidance for the further experimental interrogation of DrugBank compounds' hERG-cardiotoxicity .
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
Authors: Yuancheng Sun, Yimeng Chen, Weizhi Ma, Wenhao Huang, Kang Liu, ZhiMing Ma, Wei-Ying Ma, Yanyan Lan
Date: 2022-10-18 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, Multi-Task Learning, Prediction, Property Prediction, Transfer Learning

Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. Specifically, we enhance the training of the chemical and physiological property predictors with related physics property prediction tasks. We design two different methods for PEMP, respectively based on multi-task learning and transfer learning. Both methods include a model-agnostic molecule representation module and a property prediction module. In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively. Experimental results on public benchmark MoleculeNet show that the proposed methods have the ability to outperform corresponding state-of-the-art models.
Macrophage anti-inflammatory behaviour in a multiphase model of atherosclerotic plaque development
Authors: Ishraq U. Ahmed, Helen M. Byrne, Mary R. Myerscough
Date: 2022-11-03 00:00:00

Atherosclerosis is an inflammatory disease characterised by the formation of plaques, which are deposits of lipids and cholesterol-laden macrophages that form in the artery wall. The inflammation is often non-resolving, due in large part to changes in normal macrophage anti-inflammatory behaviour that are induced by the toxic plaque microenvironment. These changes include higher death rates, defective efferocytic uptake of dead cells, and reduced rates of emigration. We develop a free boundary multiphase model for early atherosclerotic plaques, and we use it to investigate the effects of impaired macrophage anti-inflammatory behaviour on plaque structure and growth. We find that high rates of cell death relative to efferocytic uptake results in a plaque populated mostly by dead cells. We also find that emigration can potentially slow or halt plaque growth by allowing material to exit the plaque, but this is contingent on the availability of live macrophage foam cells in the deep plaque. Finally, we introduce an additional bead species to model macrophage tagging via microspheres, and we use the extended model to explore how high rates of cell death and low rates of efferocytosis and emigration prevent the clearance of macrophages from the plaque.
Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion
Authors: Joel Grodstein, Michael Levin
Date: 2022-11-02 00:00:00
Tasks: Anatomy

Morphogenesis, the establishment and repair of emergent complex anatomy by groups of cells, is a fascinating and biomedically-relevant problem. One of its most fascinating aspects is that a developing embryo can reliably recover from disturbances, such as splitting into twins. While this reliability implies some type of goal-seeking error minimization over a morphogenic field, there are many gaps with respect to detailed, constructive models of such a process being used to implement the collective intelligence of cellular swarms. We describe a closed-loop negative-feedback system for creating reaction-diffusion (RD) patterns with high reliability. It uses a cellular automaton to characterize a morphogen pattern, then compares it to a goal and adjusts accordingly, providing a framework for modeling anatomical homeostasis and robust generation of target morphologies. Specifically, we create a RD pattern with N repetitions, where N is easily changeable. Furthermore, the individual repetitions of the RD pattern can be easily stretched or shrunk under genetic control to create, e.g., some morphological features larger than others. Finally, the cellular automaton uses a computation wave that scans the morphogen pattern unidirectionally to characterize the features that the negative feedback then controls. By taking advantage of a prior process asymmetrically establishing planar polarity (e.g., head vs. tail), our automaton is greatly simplified. This work contributes to the exciting effort of understanding design principles of morphological computation, which can be used to understand evolved developmental mechanisms, manipulate them in regenerative medicine settings, or embed a degree of synthetic intelligence into novel bioengineered constructs.
Detectability of Varied Hybridization Scenarios using Genome-Scale Hybrid Detection Methods
Authors: Marianne Bjorner, Erin K. Molloy, Colin N. Dewey, Claudia Solis-Lemus
Date: 2022-11-01 00:00:00

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, and summary methods that predict individual hybridization events. However, a lack of empirical comparisons between methods - especially pertaining to large networks with varied hybridization scenarios - hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson's D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and Patterson's D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow. We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher false negative rates across methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving ghost lineages, HyDe is the only method to be able to separate hybrid vs parent signals. Lastly, we test the methods on ultraconserved elements from the bee subfamily Nomiinae, finding the possibility of hybridization events between clades which correspond to regions of poor support in the species tree estimated in the original study.
Tuning hyperparameters of doublet-detection methods for single-cell RNA sequencing data
Authors: Nan Miles Xi, Angelos Vasilopoulos
Date: 2022-11-01 00:00:00

The existence of doublets in single-cell RNA sequencing (scRNA-seq) data poses a great challenge in downstream data analysis. Computational doublet-detection methods have been developed to remove doublets from scRNA-seq data. Yet, the default hyperparameter settings of those methods may not provide optimal performance. Here, we propose a strategy to tune hyperparameters for a cutting-edge doublet-detection method. We utilize a full factorial design to explore the relationship between hyperparameters and detection accuracy on 16 real scRNA-seq datasets. The optimal hyperparameters are obtained by a response surface model and convex optimization. We show that the optimal hyperparameters provide top performance across scRNA-seq datasets under various biological conditions. Our tuning strategy can be applied to other computational doublet-detection methods. It also offers insights into hyperparameter tuning for broader computational methods in scRNA-seq data analysis.
Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning
Authors: Tristan Lim
Date: 2022-09-28 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions
Authors: Anders Christensen, Joel Ferguson, Simón Ramírez Amaya
Date: 2022-10-06 00:00:00
Tasks: Prediction, Vocal Bursts Intensity Prediction

Recent efforts have been very successful in accurately mapping welfare in datasparse regions of the world using satellite imagery and other non-traditional data sources. However, the literature to date has focused on predicting a particular class of welfare measures, asset indices, which are relatively insensitive to short term fluctuations in well-being. We suggest that predicting more volatile welfare measures, such as consumption expenditure, substantially benefits from the incorporation of data sources with high temporal resolution. By incorporating daily weather data into training and prediction, we improve consumption prediction accuracy significantly compared to models that only utilize satellite imagery.
State-dependent Asset Allocation Using Neural Networks
Authors: Reza Bradrania, Davood Pirayesh Neghab
Date: 2022-11-02 00:00:00

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.
On The Equivalence Of The Mean Variance Criterion And Stochastic Dominance Criteria
Authors: George Samartzis, Nikitas Pittis
Date: 2022-11-02 00:00:00
Tasks: valid

We study the necessary and sufficient conditions under which the Mean-Variance Criterion (MVC) is equivalent to the Maximum Expected Utility Criterion (MEUC), for two lotteries. Based on Chamberlain (1983), we conclude that the MVC is equivalent to the Second-order Stochastic Dominance Rule (SSDR) under any symmetric Elliptical distribution. We then discuss the work of Schuhmacher et al. (2021). Although their theoretical findings deduce that the Mean-Variance Analysis remains valid under Skew-Elliptical distributions, we argue that this does not entail that the MVC coincides with the SSDR. In fact, generating multiple MV-pairs that follow a Skew-Normal distribution it becomes evident that the MVC fails to coincide with the SSDR for some types of risk-averse investors. In the second part of this work, we examine the premise of Levy and Markowitz (1979) that "the MVC deduces the maximization of the expected utility of an investor, under any approximately quadratic utility function, without making any further assumption on the distribution of the lotteries". Using Monte Carlo Simulations, we find out that the set of approximately quadratic utility functions is too narrow. Specifically, our simulations indicate that $\log{(a+Z)}$ and $(1+Z)^a$ are almost quadratic, while $-e^{-a(1+Z)}$ and $-(1+Z)^{-a}$ fail to approximate a quadratic utility function under either an Extreme Value or a Stable Pareto distribution.
Creating an Optimal Portfolio of Crops Using Price Forecasting to Increase ROI for Indian Farmers
Authors: Akshai Gaddam, Sravan Malla, Sandhya Dasari, Narayana Darapaneni, Mukesh Kumar Shukla
Date: 2022-10-24 00:00:00

The Indian agricultural sector being in a constant phase of upgradation, has been on the road to modernization for the last couple of years. The fundamental source of livelihood for over 70 percent of the population living in rural parts of the country is still agriculture. The average Indian farmer, although has access to raw and trend data pertaining to crop prices, yield and demand from Indian government and private websites, still struggles to make the right choices. They are constantly faced with the dilemma of choosing the right crop to address market demand and fetch them a decent profit. Since the process of shortlisting crops amongst the many suitable ones isn't completely scientific and usually dictated by area tradition, this project has aimed to address that issue by forecasting the price of those crops and uses that to create an optimal portfolio that the farmers can obtain to arrive at a data-driven decision for crop selection with optimal estimated ROI.
Stock Trading Volume Prediction with Dual-Process Meta-Learning
Authors: Ruibo Chen, Wei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, Xu sun
Date: 2022-10-11 00:00:00
Tasks: Algorithmic Trading, Meta-Learning, Prediction

Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a universal model for different stocks. However, this kind of practice omits the specific characteristics of individual stocks by applying the same set of parameters for different stocks. On the other hand, learning different models for each stock would face data sparsity or cold start problems for many stocks with small capitalization. To take advantage of the data scale and the various characteristics of individual stocks, we propose a dual-process meta-learning method that treats the prediction of each stock as one task under the meta-learning framework. Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters. Furthermore, we propose to mine the pattern of each stock in the form of a latent variable which is then used for learning the parameters for the prediction module. This makes the prediction procedure aware of the data pattern. Extensive experiments on volume predictions show that our method can improve the performance of various baseline models. Further analyses testify the effectiveness of our proposed meta-learning framework.
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Authors: Hongyu Zhao, Hao Tan, Hongyuan Mei
Date: 2022-10-18 00:00:00
Tasks: Language Modeling, Language Modelling, Mixture-of-Experts, Transfer Learning

Adapter-tuning is a paradigm that transfers a pretrained language model to downstream tasks by adding and tuning a small number of new parameters. Previously proposed adapter architectures are all feed-forward neural networks. In this paper, we investigate the effectiveness of using tiny-attention -- i.e., attention with extremely small per-head dimensionality -- as adapters. Our tiny-attention adapter learns to modify the hidden states at each position directly conditioned on the hidden states at all the other positions, which is missed by the previously proposed adapters. Moreover, we view its multiple attention heads as a mixture of experts and propose to average their weights during deployment, which further reduces its inference computation cost. On the GLUE benchmark, our tiny-attention adapter outperforms the other parameter-efficient transfer learning methods as well as full fine-tuning while only updating 0.05% of the parameters. On the FewGLUE benchmark, its performance is comparable to that of GPT-3 and PET.
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning
Authors: Zifeng Wang, Jimeng Sun
Date: 2022-10-11 00:00:00
Tasks: Attribute, Imputation, Privacy Preserving, Prompt Learning

Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications. Synthetic EHRs generation bypasses the need to share sensitive real patient records. However, existing methods generate single-modal EHRs by unconditional generation or by longitudinal inference, which falls short of low flexibility and makes unrealistic EHRs. In this work, we propose to formulate EHRs generation as a text-to-text translation task by language models (LMs), which suffices to highly flexible event imputation during generation. We also design prompt learning to control the generation conditioned by numerical and categorical demographic features. We evaluate synthetic EHRs quality by two perplexity measures accounting for their longitudinal pattern (longitudinal imputation perplexity, lpl) and the connections cross modalities (cross-modality imputation perplexity, mpl). Moreover, we utilize two adversaries: membership and attribute inference attacks for privacy-preserving evaluation. Experiments on MIMIC-III data demonstrate the superiority of our methods on realistic EHRs generation (53.1\% decrease of lpl and 45.3\% decrease of mpl on average compared to the best baselines) with low privacy risks. Software is available at https://github.com/RyanWangZf/PromptEHR.
Efficiently Trained Low-Resource Mongolian Text-to-Speech System Based On FullConv-TTS
Authors: ZiQi Liang
Date: 2022-10-24 00:00:00
Tasks: Data Augmentation, GPU, text-to-speech, Text to Speech

Recurrent Neural Networks (RNNs) have become the standard modeling technique for sequence data, and are used in a number of novel text-to-speech models. However, training a TTS model including RNN components has certain requirements for GPU performance and takes a long time. In contrast, studies have shown that CNN-based sequence synthesis technology can greatly reduce training time in text-to-speech models while ensuring a certain performance due to its high parallelism. We propose a new text-to-speech system based on deep convolutional neural networks that does not employ any RNN components (recurrent units). At the same time, we improve the generality and robustness of our model through a series of data augmentation methods such as Time Warping, Frequency Mask, and Time Mask. The final experimental results show that the TTS model using only the CNN component can reduce the training time compared to the classic TTS models such as Tacotron while ensuring the quality of the synthesized speech.
Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis
Authors: Florian Lux, Ching-Yi Chen, Ngoc Thang Vu
Date: 2022-10-21 00:00:00
Tasks: Emotion Classification

Embedding paralinguistic properties is a challenging task as there are only a few hours of training data available for domains such as emotional speech. One solution to this problem is to pretrain a general self-supervised speech representation model on large amounts of unlabeled speech. This pretrained model is then finetuned to a specific task. Paralinguistic properties however have notoriously high class variance, making the finetuning ineffective. In this work, we propose a two step approach to this. First we improve the embedding space, then we train an adapter to bridge the gap from the embedding space to a classification task. In order to improve the class invariance we use a combination of contrastive and non-contrastive losses to explicitly optimize for class invariant, yet discriminative features. Our approach consistently outperforms baselines that are finetuned end-to-end on multiple tasks and surpasses a benchmark on state-of-the-art emotion classification.