pwc-archive/papers-with-abstracts huggingface dataset:
Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification
Authors: Zicheng Liu, Siyuan Li, ZhiYuan Chen, Fang Wu, Chang Yu, Qirong Yang, Yucheng Guo, Yujie Yang, XiaoMing Zhang, Stan Z. Li
Date: 2025-02-11 00:00:00
Tasks: Knowledge Distillation

The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. Although modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains underexplored. This paper follows the guidance of the central dogma to redesign both the data and model pipeline and offers a comprehensive framework, Life-Code, that spans different biological functions. As for data flow, we propose a unified pipeline to integrate multi-omics data by reverse-transcribing RNA and reverse-translating amino acids into nucleotide-based sequences. As for the model, we design a codon tokenizer and a hybrid long-sequence architecture to encode the interactions between coding and non-coding regions through masked modeling pre-training. To model the translation and folding process with coding sequences, Life-Code learns protein structures of the corresponding amino acids by knowledge distillation from off-the-shelf protein language models. Such designs enable Life-Code to capture complex interactions within genetic sequences, providing a more comprehensive understanding of multi-omics with the central dogma. Extensive experiments show that Life-Code achieves state-of-the-art results on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation.
GENERator: A Long-Context Generative Genomic Foundation Model
Authors: Wei Wu, Qiuyi Li, Mingyang Li, Kun fu, Fuli Feng, Jieping Ye, Hui Xiong, Zheng Wang
Date: 2025-02-11 00:00:00
Tasks: model

Advancements in DNA sequencing technologies have significantly improved our ability to decode genomic sequences. However, the prediction and interpretation of these sequences remain challenging due to the intricate nature of genetic material. Large language models (LLMs) have introduced new opportunities for biological sequence analysis. Recent developments in genomic language models have underscored the potential of LLMs in deciphering DNA sequences. Nonetheless, existing models often face limitations in robustness and application scope, primarily due to constraints in model structure and training data scale. To address these limitations, we present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters. Trained on an expansive dataset comprising 386B bp of eukaryotic DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks. The model adheres to the central dogma of molecular biology, accurately generating protein-coding sequences that translate into proteins structurally analogous to known families. It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles. These capabilities position the GENERator as a pivotal tool for genomic research and biotechnological advancement, enhancing our ability to interpret and predict complex biological systems and enabling precise genomic interventions. Implementation details and supplementary resources are available at https://github.com/GenerTeam/GENERator.
Find Central Dogma Again: Leveraging Multilingual Transfer in Large Language Models
Authors: Wang Liang
Date: 2025-02-10 00:00:00
Tasks: Binary Classification, Experimental Design

In recent years, large language models (LLMs) have achieved state-of-the-art results in various biological sequence analysis tasks, such as sequence classification, structure prediction, and function prediction. Similar to advancements in AI for other scientific fields, deeper research into biological LLMs has begun to focus on using these models to rediscover important existing biological laws or uncover entirely new patterns in biological sequences. This study leverages GPT-like LLMs to utilize language transfer capabilities to rediscover the genetic code rules of the central dogma. In our experimental design, we transformed the central dogma into a binary classification problem of aligning DNA sequences with protein sequences, where positive examples are matching DNA and protein sequences, and negative examples are non-matching pairs. We first trained a GPT-2 model from scratch using a dataset comprising protein sequences, DNA sequences, and sequences from languages such as English and Chinese. Subsequently, we fine-tuned the model using the natural language sentences similarity judgment dataset from PAWS-X. When tested on a dataset for DNA and protein sequence alignment judgment, the fine-tuned model achieved a classification accuracy of 81%. The study also analyzed factors contributing to this zero-shot capability, including model training stability and types of training data. This research demonstrates that LLMs can, through the transfer of natural language capabilities and solely relying on the analysis of sequences themselves, rediscover the central dogma without prior knowledge of it. This study bridges natural language and genetic language, opening a new door for AI-driven biological research.
HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational Pharmacovigilance
Authors: Zhaoying Wang, Yingdan Shi, Xiang Liu, Can Chen, Jun Wen, Ren Wang
Date: 2025-02-10 00:00:00
Tasks: Pharmacovigilance

Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress in this field. Existing resources such as TWOSIDES primarily focus on pairwise interactions. To fill this critical gap, we introduce HODDI, the first Higher-Order Drug-Drug Interaction Dataset, constructed from U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) records spanning the past decade, to advance computational pharmacovigilance. HODDI contains 109,744 records involving 2,506 unique drugs and 4,569 unique side effects, specifically curated to capture multi-drug interactions and their collective impact on adverse effects. Comprehensive statistical analyses demonstrate HODDI's extensive coverage and robust analytical metrics, making it a valuable resource for studying higher-order drug relationships. Evaluating HODDI with multiple models, we found that simple Multi-Layer Perceptron (MLP) can outperform graph models, while hypergraph models demonstrate superior performance in capturing complex multi-drug interactions, further validating HODDI's effectiveness. Our findings highlight the inherent value of higher-order information in drug-side effect prediction and position HODDI as a benchmark dataset for advancing research in pharmacovigilance, drug safety, and personalized medicine. The dataset and codes are available at https://github.com/TIML-Group/HODDI.
From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production
Authors: Mingfang Zhang, Jarod Lévy, Stéphane d'Ascoli, Jérémy Rapin, F. -Xavier Alario, Pierre Bourdillon, Svetlana Pinet, Jean-Rémi King
Date: 2025-02-11 00:00:00
Tasks: EEG

Humans effortlessly communicate their thoughts through intricate sequences of motor actions. Yet, the neural processes that coordinate language production remain largely unknown, in part because speech artifacts limit the use of neuroimaging. To elucidate the unfolding of language production in the brain, we investigate with magnetoencephalography (MEG) and electroencephalography (EEG) the neurophysiological activity of 35 skilled typists, while they typed sentences on a keyboard. This approach confirms the hierarchical predictions of linguistic theories: the neural activity preceding the production of each word is marked by the sequential rise and fall of context-, word-, syllable-, and letter-level representations. Remarkably, each of these neural representations is maintained over long time periods within each level of the language hierarchy. This phenomenon results in a superposition of successive representations that is supported by a hierarchy of dynamic neural codes. Overall, these findings provide a precise computational breakdown of the neural dynamics that coordinate the production of language in the human brain.
Understanding and controlling the geometry of memory organization in RNNs
Authors: Udith Haputhanthri, Liam Storan, Yiqi Jiang, Tarun Raheja, Adam Shai, Orhun Akengin, Nina Miolane, Mark J. Schnitzer, Fatih Dinc, Hidenori Tanaka
Date: 2025-02-11 00:00:00

Training recurrent neural networks (RNNs) is a high-dimensional process that requires updating numerous parameters. Therefore, it is often difficult to pinpoint the underlying learning mechanisms. To address this challenge, we propose to gain mechanistic insights into the phenomenon of \emph{abrupt learning} by studying RNNs trained to perform diverse short-term memory tasks. In these tasks, RNN training begins with an initial search phase. Following a long period of plateau in accuracy, the values of the loss function suddenly drop, indicating abrupt learning. Analyzing the neural computation performed by these RNNs reveals geometric restructuring (GR) in their phase spaces prior to the drop. To promote these GR events, we introduce a temporal consistency regularization that accelerates (bioplausible) training, facilitates attractor formation, and enables efficient learning in strongly connected networks. Our findings offer testable predictions for neuroscientists and emphasize the need for goal-agnostic secondary mechanisms to facilitate learning in biological and artificial networks.
A Relativistic Theory of Consciousness (shortened version)
Authors: Nir Lahav, Zachariah A. Neemeh
Date: 2025-02-11 00:00:00

This paper is a shortened version of the full paper that was published in the journal Frontiers of Psychology in May 2022. In recent decades, the scientific study of consciousness has significantly increased our understanding of this elusive phenomenon. Yet, despite critical development in our understanding of the functional side of consciousness, we still lack a fundamental theory regarding its phenomenal aspect. The phenomenal aspect of consciousness is the first-person answer to what it is like question, and it has thus far proved recalcitrant to direct scientific investigation. The question of how the brain, or any cognitive system, can create conscious experience out of neural representations poses a great conundrum to science. Naturalistic dualists argue that it is composed of a primitive, private, nonreductive element of reality. Illusionists, on the other hand, argue that it is merely a cognitive illusion. We contend that both the dualist and illusionist positions are flawed because they tacitly assume consciousness to be an absolute property that does not depend on the observer. We developed a conceptual and a mathematical argument for a relativistic theory of consciousness in which a system either has or does not have phenomenal consciousness with respect to some observer. According to the theory, Phenomenal consciousness is neither private nor delusional, just relativistic. In the frame of reference of the cognitive system, it will be observable (first-person perspective) and in other frame of reference it will not (third-person perspective). These two cognitive frames of reference are both correct, just as in the case of an observer that claims to be at rest while another will claim that the observer has constant velocity. Neither observer position can be privileged, as they both describe the same underlying reality.
A state-space framework for causal detection of hippocampal ripple-replay events
Authors: Sirui Zeng, Uri T. Eden
Date: 2025-02-08 00:00:00

Hippocampal ripple-replay events are typically identified using a two-step process that at each time point uses past and future data to determine whether an event is occurring. This prevents researchers from identifying these events in real time for closed-loop experiments. It also prevents the identification of periods of nonlocal representation that are not accompanied by large changes in the spectral content of the local field potentials (LFPs). In this work, we present a new state-space model framework that is able to detect concurrent changes in the rhythmic structure of LFPs with nonlocal activity in place cells to identify ripple-replay events in a causal manner. The model combines latent factors related to neural oscillations, represented space, and switches between coding properties to explain simultaneously the spiking activity from multiple units and the rhythmic content of LFPs recorded from multiple sources. The model is temporally causal, meaning that estimates of the switching state can be made at each instant using only past information from the spike and LFP signals, or can be combined with future data to refine those estimates. We applied this model framework to simulated and real hippocampal data to demonstrate its performance in identifying ripple-replay events.
Spaces and sequences in the hippocampus: a homological perspective
Authors: Andrey Babichev, Vladimir Vashin, Yuri Dabaghian
Date: 2025-02-08 00:00:00
Tasks: Hippocampus

Topological techniques have become a popular tool for studying information flows in neural networks. In particular, simplicial homology theory is used to analyze how cognitive representations of space emerge from large conglomerates of independent neuronal contributions. Meanwhile, a growing number of studies suggest that many cognitive functions are sustained by serial patterns of activity. Here, we investigate stashes of such patterns using path homology theory--an impartial, universal approach that does not require a priori assumptions about the sequences' nature, functionality, underlying mechanisms, or other contexts. We focus on the hippocampus -- a key enabler of learning and memory in mammalian brains -- and quantify the ordinal arrangement of its activity similarly to how its topology has previously been studied in terms of simplicial homologies. The results reveal that the vast majority of sequences produced during spatial navigation are structurally equivalent to one another. Only a few classes of distinct sequences form an ordinal schema of serial activity that remains stable as the pool of sequences consolidates. Importantly, the structure of both maps is upheld by combinations of short sequences, suggesting that brief activity motifs dominate physiological computations. This ordinal organization emerges and stabilizes on timescales characteristic of spatial learning, displaying similar dynamics. Yet, the ordinal maps generally do not reflect topological affinities -- spatial and sequential analyses address qualitatively different aspects of spike flows, representing two complementary formats of information processing.
Multi-Site rs-fMRI Domain Alignment for Autism Spectrum Disorder Auxiliary Diagnosis Based on Hyperbolic Space
Authors: Yiqian Luo, Qiurong Chen, Fali Li, Peng Xu, Yangsong Zhang
Date: 2025-02-08 00:00:00
Tasks: Diagnostic, Domain Adaptation

Increasing the volume of training data can enable the auxiliary diagnostic algorithms for Autism Spectrum Disorder (ASD) to learn more accurate and stable models. However, due to the significant heterogeneity and domain shift in rs-fMRI data across different sites, the accuracy of auxiliary diagnosis remains unsatisfactory. Moreover, there has been limited exploration of multi-source domain adaptation models on ASD recognition, and the interpretability of models is often poor. To address these challenges, we proposed a domain-adaptive algorithm based on hyperbolic space embedding. Hyperbolic space is naturally suited for representing the topology of complex networks such as brain functional networks. Therefore, we embedded the brain functional network into hyperbolic space and constructed the corresponding hyperbolic space community network to effectively extract latent representations. To address the heterogeneity of data across different sites and the issue of domain shift, we introduce a constraint loss function, Hyperbolic Maximum Mean Discrepancy (HMMD), to align the marginal distributions in the hyperbolic space. Additionally, we employ class prototype alignment to to mitigate discrepancies in conditional distributions across domains. Experimental results demonstrated that the proposed algorithm shows superior classification performance for ASD compared with baseline models, and is more robust to multi-site heterogeneity.
Emergence of Self-Awareness in Artificial Systems: A Minimalist Three-Layer Approach to Artificial Consciousness
Authors: Kurando IIDA
Date: 2025-02-04 00:00:00

This paper proposes a minimalist three-layer model for artificial consciousness, focusing on the emergence of self-awareness. The model comprises a Cognitive Integration Layer, a Pattern Prediction Layer, and an Instinctive Response Layer, interacting with Access-Oriented and Pattern-Integrated Memory systems. Unlike brain-replication approaches, we aim to achieve minimal self-awareness through essential elements only. Self-awareness emerges from layer interactions and dynamic self-modeling, without initial explicit self-programming. We detail each component's structure, function, and implementation strategies, addressing technical feasibility. This research offers new perspectives on consciousness emergence in artificial systems, with potential implications for human consciousness understanding and adaptable AI development. We conclude by discussing ethical considerations and future research directions.
The devasting economic impact of Callinectes sapidus on the clam fishing in the Po Delta (Italy): Striking evidence from novel field data
Authors: Francesco Tiralongo, Emanuele Mancini, Raffaele Gattelli, Costanza di Pasquale, Emanuele Rossetti, Riccardo Martellucci, Alberto Felici, Giorgio Mancinelli, Christian Mulder
Date: 2025-02-10 00:00:00
Tasks: Management

Invasive species are a growing threat to marine ecosystems, and the recent proliferation of the Atlantic blue crab (Callinectes sapidus) in the Po Delta (Italy) has had significant ecological and economic impacts, particularly on clam farming. This study explores the influence of C. sapidus on clam production in the Po Delta, combining biological and ecological data with socio-economic analysis. Field data collected between August and December 2023 from the Canarin and Scardovari Lagoons revealed seasonal fluctuations in crab abundance, with a peak in captures during the warmer months. The predatory behaviour of C. sapidus has led to a sharp decline in clam production, reaching near-zero levels in early 2024. Statistical analysis confirmed a strong correlation between the increase of the invasive crab population and the decrease in clam yields. This study also explores potential management strategies, including the economic valorisation of C. sapidus as a commercial resource, turning an ecological challenge into an opportunity. These findings highlight the urgent need for targeted management interventions to mitigate the impact of this invasive species on local fisheries and ecosystems.
Integrating epidemiological and economic models to estimate the cost of simulated foot-and-mouth disease outbreaks in Brazil
Authors: Nicolas C. Cardenas, Taís C. de Menezes, Amanda M. Countryman, Francisco P. N. Lopes, Fernando H. S. Groff, Grazziane M. Rigon, Marcelo Gocks, Gustavo Machado
Date: 2025-02-10 00:00:00

The introduction of foot-and-mouth disease (FMD) leads to substantial economic impacts through animal loss, decreased livestock and meat production, increased government and private spending on control and eradication measures, and trade restrictions. This study evaluates the direct cost-effectiveness of four control and eradication scenarios of hypothetical FMD outbreaks in Rio Grande do Sul, Brazil. Our model simulation considered scenarios with depopulation of detected farms and emergency vaccination and two enhanced scenarios featuring increased capacity for emergency vaccination and depopulation. FMD outbreaks were simulated using a multi-host, single-pathogen Susceptible-Exposed-Infectious-Recovered model incorporating species-specific transmission probabilities, within-farm dynamics, and spatial transmission factors. The economic cost evaluation encompassed animal elimination (a.k.a. depopulation), carcass disposal, visits by animal health officials, laboratory testing, emergency vaccination, and sanitary barriers (a.k.a. traffic-control points), and movement restrictions due to control zones. Our results provided a range of predicted costs for a potential reintroduction of FMD ranging from $977,128 to $52,275,811. Depopulation was the most expensive, followed by local traffic control points and emergency vaccination. Our results demonstrated that higher rates of depopulation, or depopulation combined with vaccination, were the most effective strategies to reduce long-term economic impacts despite higher initial costs. Allocating more resources early in the outbreak was cost-effective in minimizing the overall effect and achieving faster eradication.
Flexible inference of evolutionary accumulation dynamics using uncertain observational data
Authors: Jessica Renz, Morten Brun, Iain G. Johnston
Date: 2025-02-09 00:00:00

Understanding and predicting evolutionary accumulation pathways is a key objective in many fields of research, ranging from classical evolutionary biology to diverse applications in medicine. In this context, we are often confronted with the problem that data is sparse and uncertain. To use the available data as best as possible, inference approaches that can handle this uncertainty are required. One way that allows us to use not only cross-sectional data, but also phylogenetic related and longitudinal data, is using `hypercubic inference' models. In this article we introduce HyperLAU, a new algorithm for hypercubic inference that makes it possible to use datasets including uncertainties for learning evolutionary pathways. Expanding the flexibility of accumulation modelling, HyperLAU allows us to infer dynamic pathways and interactions between features, even when large sets of particular features are unobserved across the source dataset. We show that HyperLAU is able to highlight the main pathways found by other tools, even when up to 50% of the features in the input data are uncertain. Additionally, we demonstrate how it can help to overcome possible biases that can occur then reducing the used data by excluding uncertain parts. We illustrate the approach with a case study on multidrug resistance in tuberculosis, showing that HyperLAU allows more flexible data and provides new information about evolutionary pathways compared to existing approaches.