pwc-archive/papers-with-abstracts huggingface dataset:
Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models
Authors: Cong Fu, Xiner Li, Blake Olson, Heng Ji, Shuiwang Ji
Date: 2024-08-19 00:00:00
Tasks: Drug Design

Structure-based drug design (SBDD) is crucial for developing specific and effective therapeutics against protein targets but remains challenging due to complex protein-ligand interactions and vast chemical space. Although language models (LMs) have excelled in natural language processing, their application in SBDD is underexplored. To bridge this gap, we introduce a method, known as Frag2Seq, to apply LMs to SBDD by generating molecules in a fragment-based manner in which fragments correspond to functional modules. We transform 3D molecules into fragment-informed sequences using SE(3)-equivariant molecule and fragment local frames, extracting SE(3)-invariant sequences that preserve geometric information of 3D fragments. Furthermore, we incorporate protein pocket embeddings obtained from a pre-trained inverse folding model into the LMs via cross-attention to capture protein-ligand interaction, enabling effective target-aware molecule generation. Benefiting from employing LMs with fragment-based generation and effective protein context encoding, our model achieves the best performance on binding vina score and chemical properties such as QED and Lipinski, which shows our model's efficacy in generating drug-like ligands with higher binding affinity against target proteins. Moreover, our method also exhibits higher sampling efficiency compared to atom-based autoregressive and diffusion baselines with at most ~300x speedup.
Probing leukemia cells behavior under starvation
Authors: Simone Scalise, Giorgio Gosti, Giancarlo Ruocco, Giovanna Peruzzi, Mattia Miotto
Date: 2024-08-17 00:00:00

The ability of a cancer cell population to achieve heterogeneity in their phenotype distributions offers advantages in tumor invasiveness and drug resistance. Studying the mechanisms behind such observed heterogeneity in mammalian cells presents challenges due for instance to the prolonged proliferation times compared to widely studied unicellular organisms like bacteria and yeast. Here, we studied the response of leukemia cell populations to serum starvation via a protocol, we recently developed, that makes use of live cell fluorescence and flow cytometry in combination with a quantitative analytical model to follow the population proliferation while monitoring the dynamics of its phenotype distributions. We found that upon switching between a serum-rich to a serum-poor media, leukemia cells (i) maintain a memory of the previous environment up to one generation even in the presence of severe medium-depletion, before (ii) adapting their growth and division rates to the novel environment while preserving a sizer-like division strategy. Finally, looking at the mitochondria content of the proliferating vs non-proliferating cells, we found that the latter is characterized by a higher number of older mitochondria, suggesting a possible functional role of the observed asymmetric partitioning of (aged) mitochondria in leukemia cells.
Predicting the genetic component of gene expression using gene regulatory networks
Authors: Gutama Ibrahim Mohammad, Tom Michoel
Date: 2024-08-16 00:00:00
Tasks: parameter estimation, Prediction

Gene expression prediction plays a vital role in transcriptome-wide association studies (TWAS), which seek to establish associations between tissue gene expression and complex traits. Traditional models rely on genetic variants in close genomic proximity to the gene of interest to predict the genetic component of gene expression. In this study, we propose a novel approach incorporating distal genetic variants acting through gene regulatory networks (GRNs) into gene expression prediction models, in line with the omnigenic model of complex trait inheritance. Using causal and coexpression GRNs reconstructed from genomic and transcriptomic data and modeling the data as a Bayesian network jointly over genetic variants and genes, inference of gene expression from observed genotypic data is achieved through a two-step process. Initially, the expression level of each gene in the network is predicted using its local genetic variants. The residuals, calculated as the differences between the observed and predicted expression levels, are then modeled using the genotype information of parent and/or grandparent nodes in the GRN. The final predicted expression level of the gene is obtained by summing the predictions from the local variants model and the residual model, effectively incorporating both local and distal genetic influences. Using various regularized regression techniques for parameter estimation, we found that GRN-based gene expression prediction outperformed the traditional local-variant approach on simulated data from the DREAM5 Systems Genetics Challenge and real data from the Geuvadis study and an eQTL mapping study in yeast. This study provides important insights into the challenge of gene expression prediction for TWAS. It reaffirms the importance of GRNs for understanding the genetic effects on gene expression and complex traits more generally.
Computational strategies for cross-species knowledge transfer and translational biomedicine
Authors: Hao Yuan, Christopher A. Mancuso, Kayla Johnson, Ingo Braasch, Arjun Krishnan
Date: 2024-08-16 00:00:00
Tasks: Transfer Learning

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.
NP-TCMtarget: a network pharmacology platform for exploring mechanisms of action of Traditional Chinese medicine
Authors: Aoyi Wang, Yingdong Wang, Haoyang Peng, Haoran Zhang, Caiping Cheng, Jinzhong Zhao, Wuxia Zhang, Jianxin Chen, Peng Li
Date: 2024-08-17 00:00:00
Tasks: Binary Classification

The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput drug-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between drug/disease inducible gene expression profiles and specific gene signatures for 8,233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal components-direct targets-indirect targets by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget.
Quantifying Signal-to-Noise Ratio in Neural Latent Trajectories via Fisher Information
Authors: Hyungju Jeon, Il Memming Park
Date: 2024-08-16 00:00:00

Spike train signals recorded from a large population of neurons often exhibit low-dimensional spatio-temporal structure and modeled as conditional Poisson observations. The low-dimensional signals that capture internal brain states are useful for building brain machine interfaces and understanding the neural computation underlying meaningful behavior. We derive a practical upper bound to the signal-to-noise ratio (SNR) of inferred neural latent trajectories using Fisher information. We show that the SNR bound is proportional to the overdispersion factor and the Fisher information per neuron. Further numerical experiments show that inference methods that exploit the temporal regularities can achieve higher SNRs that are proportional to the bound. Our results provide insights for fitting models to data, simulating neural responses, and design of experiments.
GitHub is an effective platform for collaborative and reproducible laboratory research
Authors: Katharine Y. Chen, Maria Toro-Moreno, Arvind Rasi Subramaniam
Date: 2024-08-18 00:00:00
Tasks: Experimental Design, Transfer Learning

Laboratory research is a complex, collaborative process that involves several stages, including hypothesis formulation, experimental design, data generation and analysis, and manuscript writing. Although reproducibility and data sharing are increasingly prioritized at the publication stage, integrating these principles at earlier stages of laboratory research has been hampered by the lack of broadly applicable solutions. Here, we propose that the workflow used in modern software development offers a robust framework for enhancing reproducibility and collaboration in laboratory research. In particular, we show that GitHub, a platform widely used for collaborative software projects, can be effectively adapted to organize and document all aspects of a research project's lifecycle in a molecular biology laboratory. We outline a three-step approach for incorporating the GitHub ecosystem into laboratory research workflows: 1. designing and organizing experiments using issues and project boards, 2. documenting experiments and data analyses with a version control system, and 3. ensuring reproducible software environments for data analyses and writing tasks with containerized packages. The versatility, scalability, and affordability of this approach make it suitable for various scenarios, ranging from small research groups to large, cross-institutional collaborations. Adopting this framework from a project's outset can increase the efficiency and fidelity of knowledge transfer within and across research laboratories. An example GitHub repository based on this approach is available at https://github.com/rasilab/github_demo and a template repository that can be copied is available at https://github.com/rasilab/github_template.
Carbon Dioxide as a Pollutant. The Risks of Rising Atmospheric CO$_2$ Levels on Human Health and on the Stability of the Biosphere
Authors: Ugo Bardi
Date: 2024-08-15 00:00:00
Tasks: Management

Carbon dioxide is a chemically active molecule that plays a vital role in Earth's ecosphere. CO$_2$ affects the acidity of seawater and has multiple negative effects on marine organisms. It is also a fundamental component of the photosynthesis and respiration reactions. There is evidence that higher CO$_2$ concentration can make the photosynthetic reaction faster in some plants, but also negatively impact the respiration reaction in aerobic lifeforms. The effects of this chemical and biochemical perturbation on the biosphere and on human health may be more important than generally highlighted in the discussion on CO$_2$, usually focused on thermal effects only. These considerations stress the importance of rapidly reducing CO$_2$ emissions and, whenever possible, remove the excess from the atmosphere. They also show that geoengineering technologies based on Solar Radiation Management (SRM) alone cannot be sufficient to contrast the negative effects of CO$_2$ anthropogenic emissions.
Predicting potential SARS-CoV-2 spillover and spillback in animals
Authors: Zi Hian Tan, Kian Yan Yong, Jian-Jun Shu
Date: 2024-08-18 00:00:00

The COVID-19 pandemic is spreading rapidly around the world, causing countries to impose lockdowns and efforts to develop vaccines on a global scale. However, human-to-animal and animal-to-human transmission cannot be ignored, as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread rapidly in farmed and wild animals. This could create a worrying cycle of SARS-CoV-2 spillover from humans to animals and spillback of new strains back into humans, rendering vaccines ineffective. This study provides a key indicator of animals that may be potential susceptible hosts for SARS-CoV-2 and coronavirus infections by analysing the phylogenetic distance between host angiotensin-converting enzyme 2 and the coronavirus spike protein. Crucially, our analysis identifies animals that are at elevated risk from a spillover and spillback incident. One group of animals has been identified as potentially susceptible to SARS-CoV-2 by harbouring a parasitic coronavirus spike protein similar to the SARS-CoV-2 spike protein. These animals may serve as amplification hosts in spillover events from zoonotic reservoirs. Tracing interspecies transmission in multi-host environments based solely on in vitro and in vivo examinations of animal susceptibility or serology is a time-consuming task. This approach allows rapid identification of high-risk animals to prioritize research and assessment of the risk of zoonotic disease transmission in the environment. It is a tool to rapidly identify zoonotic species that may cause outbreaks or participate in expansion cycles of coexistence with their hosts. This prevents the spread of coronavirus infections between species, preventing spillover and spillback incidents from occurring.
Constructing the Molecular Tree of Life using Assembly Theory and Mass Spectrometry
Authors: Amit Kahana, Alasdair Macleod, Hessam Mehr, Abhishek Sharma, Emma Carrick, Michael Jirasek, Sara Walker, Leroy Cronin
Date: 2024-08-17 00:00:00

Here we demonstrate the first biochemistry-agnostic approach to map evolutionary relationships at the molecular scale, allowing the construction of phylogenetic models using mass spectrometry (MS) and Assembly Theory (AT) without elucidating molecular identities. AT allows us to estimate the complexity of molecules by deducing the amount of shared information stored within them when . By examining 74 samples from a diverse range of biotic and abiotic sources, we used tandem MS data to detect 24102 analytes (9262 unique) and 59518 molecular fragments (6755 unique). Using this MS dataset, together with AT, we were able to infer the joint assembly spaces (JAS) of samples from molecular analytes. We show how JAS allows agnostic annotation of samples without fingerprinting exact analyte identities, facilitating accurate determination of their biogenicity and taxonomical grouping. Furthermore, we developed an AT-based framework to construct a biochemistry-agnostic phylogenetic tree which is consistent with genome-based models and outperforms other similarity-based algorithms. Finally, we were able to use AT to track colony lineages of a single bacterial species based on phenotypic variation in their molecular composition with high accuracy, which would be challenging to track with genomic data. Our results demonstrate how AT can expand causal molecular inference to non-sequence information without requiring exact molecular identities, thereby opening the possibility to study previously inaccessible biological domains.
Long-term tracking of social structure in groups of rats
Authors: Mate Nagy, Jacob D. Davidson, Gabor Vasarhelyi, Daniel Abel, Eniko Kubinyi, Ahmed El Hady, Tamas Vicsek
Date: 2024-08-16 00:00:00

Rodents serve as an important model for examining both individual and collective behavior. Dominance within rodent social structures can determine access to critical resources, such as food and mating opportunities. Yet, many aspects of the intricate interplay between individual behaviors and the resulting group social hierarchy, especially its evolution over time, remain unexplored. In this study, we utilized an automated tracking system that continuously monitored groups of male rats for over 250 days to enable an in-depth analysis of individual behavior and the overarching group dynamic. We describe the evolution of social structures within a group and additionally investigate how past behaviors influence the emergence of new social hierarchies when group composition and experimental area changes. Notably, we find that conventional individual and pairwise tests exhibit a weak correlation with group behavior, highlighting their limited accuracy in predicting behavioral outcomes in a collective context. These results emphasize the context-dependence of social behavior as an emergent property of interactions within a group and highlight the need to measure and quantify social behavior in more naturalistic environments.
SepAl: Sepsis Alerts On Low Power Wearables With Digital Biomarkers and On-Device Tiny Machine Learning
Authors: Marco Giordano, Kanika Dheman, Michele Magno
Date: 2024-07-31 00:00:00
Tasks: Disease Prediction, Photoplethysmography (PPG)

Sepsis is a lethal syndrome of organ dysfunction that is triggered by an infection and claims 11 million lives per year globally. Prognostic algorithms based on deep learning have shown promise in detecting the onset of sepsis hours before the actual event but use a large number of bio-markers, including vital signs and laboratory tests. The latter makes the deployment of such systems outside hospitals or in resource-limited environments extremely challenging. This paper introduces SepAl, an energy-efficient and lightweight neural network, using only data from low-power wearable sensors, such as photoplethysmography (PPG), inertial measurement units (IMU), and body temperature sensors, designed to deliver alerts in real-time. SepAl leverages only six digitally acquirable vital signs and tiny machine learning algorithms, enabling on-device real-time sepsis prediction. SepAl uses a lightweight temporal convolution neural network capable of providing sepsis alerts with a median predicted time to sepsis of 9.8 hours. The model has been fully quantized, being able to be deployed on any low-power processors, and evaluated on an ARM Cortex-M33 core. Experimental evaluations show an inference efficiency of 0.11MAC/Cycle and a latency of 143ms, with an energy per inference of 2.68mJ. This work aims at paving the way toward accurate disease prediction, deployable in a long-lasting multi-vital sign wearable device, suitable for providing sepsis onset alerts at the point of care. The code used in this work has been open-sourced and is available at https://github.com/mgiordy/sepsis-prediction
Causality-Inspired Models for Financial Time Series Forecasting
Authors: Daniel Cunha Oliveira, Yutong Lu, Xi Lin, Mihai Cucuringu, Andre Fujita
Date: 2024-08-19 00:00:00
Tasks: Causal Discovery, feature selection, Time Series, Time Series Forecasting

We introduce a novel framework to financial time series forecasting that leverages causality-inspired models to balance the trade-off between invariance to distributional changes and minimization of prediction errors. To the best of our knowledge, this is the first study to conduct a comprehensive comparative analysis among state-of-the-art causal discovery algorithms, benchmarked against non-causal feature selection techniques, in the application of forecasting asset returns. Empirical evaluations demonstrate the efficacy of our approach in yielding stable and accurate predictions, outperforming baseline models, particularly in tumultuous market conditions.
High-Frequency Options Trading | With Portfolio Optimization
Authors: Sid Bhatia
Date: 2024-08-16 00:00:00
Tasks: Portfolio Optimization

This paper explores the effectiveness of high-frequency options trading strategies enhanced by advanced portfolio optimization techniques, investigating their ability to consistently generate positive returns compared to traditional long or short positions on options. Utilizing SPY options data recorded in five-minute intervals over a one-month period, we calculate key metrics such as Option Greeks and implied volatility, applying the Binomial Tree model for American options pricing and the Newton-Raphson algorithm for implied volatility calculation. Investment universes are constructed based on criteria like implied volatility and Greeks, followed by the application of various portfolio optimization models, including Standard Mean-Variance and Robust Methods. Our research finds that while basic long-short strategies centered on implied volatility and Greeks generally underperform, more sophisticated strategies incorporating advanced Greeks, such as Vega and Rho, along with dynamic portfolio optimization, show potential in effectively navigating the complexities of the options market. The study highlights the importance of adaptability and responsiveness in dynamic portfolio strategies within the high-frequency trading environment, particularly under volatile market conditions. Future research could refine strategy parameters and explore less frequently traded options, offering new insights into high-frequency options trading and portfolio management.
Hydrogen Development in China and the EU: A Recommended Tian Ji's Horse Racing Strategy
Authors: Hong Xu
Date: 2024-08-01 00:00:00

The global momentum towards establishing sustainable energy systems has become increasingly prominent. Hydrogen, as a remarkable carbon-free and renewable energy carrier, has been endorsed by 39 countries at COP28 in the UAE, recognizing its essential role in global energy transition and industry decarbonization. Both the European Union (EU) and China are at the forefront of this shift, developing hydrogen strategies to enhance regional energy security and racing for carbon neutrality commitments by 2050 for the EU and 2060 for China. The wide applications of hydrogen across hard-to-abate sectors and the flexibility of decentralized production and storage offer customized solutions utilizing local resources in a self-paced manner. To unveil the trajectory of hydrogen development in China and the EU, this paper proposes a comparative analysis framework employing key factors to investigate hydrogen developments in both economic powerhouses. Beyond country-wise statistics, it dives into representative hydrogen economic areas in China (Inner Mongolia, Capital Economic Circle, Yangtze River Delta) and Europe (Delta Rhine Corridor) for understanding supply and demand, industrial synergy, and policy incentives for local hydrogen industries. The derived implications offer stakeholders an evolving hydrogen landscape across the Eurasian continent and insights for future policy developments facilitating the global green transition.