23 papers · ranked by Valyu relevance
Fengming Chen, Ranran Zhao, Xingxing Han, Huan Li + 1 more
Computational models integrating large-scale gene expression profiles provide a powerful approach for predicting multi-target drug interactions (DTIs). Unlike traditional experimental and computational methods that often require detailed structural or target-specific information, gene expression-based models leverage…
Jeon, Youngseung, Hwang, Christopher + 14 more
Below, we summarize the Target ID process and criteria, current challenges, and possible solutions that emerged from the interviews. 4.2.1 Target ID process and criteria. Our findings identified the Target ID process of searching for possible protein-protein interactions (PPIs) consisting of a target protein and an…
Sérgio Assunção Monteiro, Luís Alfredo Vidal de Carvalho, Fabricio Alves Barbosa da Silva
Triple-negative breast cancer (TNBC) represents a significant clinical challenge due to the absence of well-established molecular targets and resistance to conventional therapies. Identifying synergistic combinations of therapeutic targets requires computational approaches that integrate topological network analysis…
Tenzing Thiley Bhutia, Subash Kumaraguru, Devaprakash Muniraj
Timely classification and intent prediction of aerial targets is crucial for a combat aircraft to make informed tactical decisions. The prevailing approach for aerial target classification relies on data-driven models using time-series data. These models perform well with long-duration data; however, this is…
Aohua Li, Yuanshuo Zhang, Ge Gao, Bo Chen + 1 more
Current stance detection research typically relies on predicting stance based on given targets and text. However, in real-world social media scenarios, targets are neither predefined nor static but rather complex and dynamic. To address this challenge, we propose a novel task: zero-shot stance detection in the wild…
Authors not listed
The WRN helicase has recently emerged as a promising therapeutic target for microsatellite instability (MSI)-high cancers. Here, we report LXW-P1, a potent WRN degrader derived from marine bromotyrosine alkaloids. Its molecular target was identified using an AI-guided, pathway-informed perturbation transcriptomics…
Qiyuan Pan, Xiao Yuan, Jinmei Jin, Xin Luan + 4 more
Natural products, owing to their unique biological activities, possess the ability to interact with specific target proteins or regulatory networks, representing a valuable source of innovative drug candidates. However, target identification remains a major bottleneck in natural product-based drug discovery, largely…
Daniel R. Wong, Mary Piper, Jiao Qiao, Max Russo + 4 more
The identification of genetic perturbations that can reverse disease-associated cellular phenotypes toward a healthy state is a central challenge in early drug discovery. We present a proof-of-concept framework leveraging single-cell foundation models (scFMs) and a large-scale Perturb-seq dataset to prioritize targets…
Yu Pan, Jingjing Dong, Junpeng Zhang
Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges…
Alves, Gonçalo Gaspar, Zadeh, Shekoufeh Gorgi + 4 more
Combining open-source datasets can introduce data leakage if the same subject appears in multiple sets, leading to inflated model performance. To address this, we explore subject fingerprinting, mapping all images of a subject to a distinct region in latent space, to enable subject re-identification via similarity…
Authors not listed
Target-aware molecular generation models have emerged as promising tools for structure-based drug discovery, yet it remains unclear whether they genuinely exploit target information or merely resemble the Texas Sharpshooter fallacy by retrospectively rationalizing outputs. To address this, we introduce TarPass, a…
Ben Wang, Zhiyuan Cheng, Chengying She, Jiahui Zhang + 5 more
Protein phosphorylation is a key regulator of signaling, with mass spectrometry (MS) based phosphoproteomics serving as the premier technology for its analysis. However, phosphorylation profiling is hindered by acquisition biases: Data-Dependent Acquisition (DDA) suffers from stochastic undersampling and missing…
Muhammed Esad Oztemel, Ömer Muhammet Soysal, Loris Nanni
Individual brain activity patterns derived from electroencephalogram (EEG) data offer a unique source for personal identification, introducing a novel approach to the field. Autoencoders are well-known machine learning models that automate feature extraction, which is a crucial step in biometric identification. Among…
IO Butenko, NA Kitsilovskaya, IK Chudinov, AV Vakaryuk + 13 more
In bottom-up proteomics peptide it was early shown that despite a certain protein is present in a sample, only a subset of it’s proteolytic peptide products will be detected with LC-MS analysis. Property of peptide being frequently detected given its source protein’s identification was called proteotypicity. Much…
Authors not listed
DNA-encoded libraries (DELs) have emerged as a powerful platform for screening ultra-large chemical spaces by leveraging DNA barcodes to tag and track individual small molecules. Recent work has shown that machine learning can enhance DEL based hit discovery by denoising sequencing artifacts and improving binder…
Cai Chen, Jiazheng Sun, Danyang Lv, Chongxuan Tian + 4 more
Electroencephalogram (EEG)-based biometric sensing provides a promising pathway for secure and user-specific human authentication, but practical deployment remains limited by cross-session non-stationarity and potentially optimistic evaluation protocols caused by window- or event-level leakage. This study developed a…
Nikolaos Tzanakis, Alexandros Barberis, Mario Alexios Savaglio, Ioanna Chourdaki + 2 more
Understanding how the primary visual cortex of mice represents the external sensory input separately from the internal states is a fundamental challenge in systems neuroscience. Our work contributes to the problem of decoupling the stimulus-driven and internally generated components of neural activity in the primary…
Suyash Agarwal, Wentao Qiu, Kenneth D Harris, Enny van Beest + 1 more
To understand neural processes such as learning or memory, we need to track the activity of populations of neurons at the level of single spikes and across days. Here, we leverage deep neural networks to build DeepUnitMatch, a software that reliably tracks individual neurons in high-density electrophysiological…
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Comprehensive characterisation of monoclonal antibody (mAb) charge heterogeneity is essential for ensuring product quality, maintaining batch consistency, and supporting biosimilar development. Charge variant analysis (CVA) is widely used to separate acidic and basic proteoforms from the main species. However…
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The analysis of metabolic profiles using high resolution mass spectrometry (MS) data gives deep insights into the biological processes. In metabolomics, MS generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major…
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LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to the case and, if so, quantify it. Acquiring reference material for quantification is costly and time-consuming.…
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Infrared (IR) spectroscopy provides rich structural information but interpreting spectra at scale remains challenging. Here we introduce j-IR-vis, a vision-based neural model that learns chemically interpretable representations directly from IR spectra for functional-group prediction and downstream molecular…
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Sequence-defined oligomers offer programmable molecular architectures with potential in data storage, authentication, and anticounterfeiting. However, their deployment in real-world materials has been constrained by their low scale, limited thermal resilience and the need for specialized analytical methods. Here we…