bioRxiv

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Advancing Target Discovery Through Disease-Specific Integration of Multi-Modal Target Identification Models and Comprehensive Target Benchmarking System

bioRxiv Jan 1, 2025
Target identification is crucial for drug development. AI-driven approaches leveraging multi-omics and computational modeling can accelerate this process. However, integrating multi-modal data for disease-specific target identification and predicting...
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Deep Generative Models for Discrete Genotype Simulation

bioRxiv Jan 1, 2025
Deep generative models open new avenues for simulating realistic genomic data while preserving privacy and addressing data accessibility constraints. While previous studies have primarily focused on generating gene expression or haplotype data, this ...
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Large-Scale Neural Network Compensation Underlying Camouflaging in Trait Autism and Its Potential Mental Health Costs

bioRxiv Jan 1, 2025
Social camouflaging refers to strategies to hide or compensate for social difficulties, often at a significant mental health cost, and is particularly prevalent in autism. The large-scale neural network mechanisms underlying this adaptation remain po...
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Scratcher: An automated machine-vision tool for dissecting the neural basis of itch

bioRxiv Jan 1, 2025
Itch or pruritus invokes a specific reflexive and repetitive directed nocifensive behavioural response, known as scratching. Recent decades have revealed neural circuits that are involved in the sensory and affective-motivational aspects of itch-indu...
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Low-dimensional brain-symptom associations delineate depression phenotypes with distinct connectivity biomarkers and symptom profiles

bioRxiv Jan 1, 2025
Depression is neurobiologically and clinically heterogeneous. New approaches using resting-state functional MRI (rs-fMRI) functional connectivity (FC) data have modeled the neural basis of depression heterogeneity and revealed unique neural phenotype...
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RNA-seq derived sequence variations are excellent features for cell line identification

bioRxiv Jan 1, 2025
Cell lines are indispensable models for analyzing molecular mechanisms underlying human diseases. However, incorrect annotation and cross-contamination can introduce severe bias in respective studies. Accordingly, various publishers request authentic...
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Characterization of metabolic phenotypes in breast cancer through the integration of genome-scale metabolic models and machine learning

bioRxiv Jan 1, 2025
The metabolic heterogeneity of breast cancer represents a significant challenge for the identification of biomarkers and therapeutic targets. To address this problem, we integrated genome-scale metabolic models with machine learning algorithms, aimin...
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Frequency bands EEG Biomarkers for Dementia using Graph Neural Networks

bioRxiv Jan 1, 2025
We introduce a simple and interpretable model for classification of electroencephalography (EEG) signals. Our focus essentially is on using deep learning to study how connectivity patterns that are integrated to classify the EEG signals and highlight...
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Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces

bioRxiv Jan 1, 2025
The rise of antimicrobial resistance, especially among gram-negative ESKAPE pathogens, presents an urgent global health threat. However, the discovery of new antibiotics is hampered by sparse publicly available antibacterial data, complex bacterial d...
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TCUP – An Open Access Tool to Predict Tissue of Origin and Cancer of Unknown Primary (CUP)

bioRxiv Jan 1, 2025
Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We present TCUP, an ensemble learning framework that combines Contrastive Autoencoders (CAE) and Siamese...
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