AIMC Journal:
bioRxiv

Showing 2311 to 2320 of 4979 articles

In Silico Identification of Aminoadipate Semialdehyde Synthase (AASS) as a Novel Prognostic Biomarker in Triple-Negative Breast Cancer

bioRxiv Feb 20, 2026
Triple-negative breast cancer (TNBC) is an aggressive subtype that lacks effective targeted therapies. This study aimed to identify robust prognostic biomarkers by integrating network biology with machine learning (ML) approaches. TNBC expression coh...
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Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins

bioRxiv Feb 20, 2026
Models of protein evolution are foundational to biology, underpinning essential techniques such as phylogenetic tree inference, ancestral sequence reconstruction, multiple sequence alignment, variant effect prediction, and protein design. Historicall...
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Mechanistic machine learning enables interpretable and generalizable prediction of prime editing outcomes

bioRxiv Feb 20, 2026
Although prime editing (PE) can effect virtually any specified local change to genomic DNA in living systems, its efficient application currently requires extensive optimization of prime editing guide RNA (pegRNA) sequences. We present OptiPrime, a m...
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Fast and alignment-free flavivirus classification from low-coverage genomes

bioRxiv Feb 20, 2026
High genomic variability among viral species makes sequence classification highly dependent on multiple sequence alignment (MSA) methods, which are both computationally intensive and sensitive to data quality issues. To provide a more efficient and r...
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How to gain valuable insight from scarce data with Machine Learning: a post-hoc explanation tool to identify biases in biological images classification

bioRxiv Feb 20, 2026
Machine learning (ML) models are effective at classifying images across various fields, including biology. However, their performance on biomedical images is often limited by the small size of available datasets that are constrained by the time-consu...
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Foundation Models Improve Perturbation Response Prediction

bioRxiv Feb 19, 2026
Predicting cellular responses to genetic or chemical perturbations has been a long-standing goal in biology. Recent applications of foundation models to this task have yielded contradictory results regarding their superiority over simple baselines. W...
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Benchmarking Large Language Models for Predicting Therapeutic Antisense Oligonucleotide Efficacy

bioRxiv Feb 19, 2026
Antisense oligonucleotides (ASOs) are a promising class of therapeutic agents capable of selectively modulating gene expression and treating a wide range of genetic and neurological disorders. Accurate prediction of ASO efficacy is essential for acce...
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Integrated Molecular and AI-Based Diagnostics for Banana Diseases: Development, Optimization, and Field Deployment of LAMP and Computer Vision Technologies

bioRxiv Feb 19, 2026
Banana and plantain (Musa spp.) production in Sub-Saharan Africa is severely constrained by multiple diseases, with Banana bunchy top virus (BBTV) representing the most devastating viral threat. Inadequate diagnostic infrastructure limits effective m...
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GFMBench-API: A Standardized Interface for Benchmarking Genomic Foundation Models

bioRxiv Feb 19, 2026
The rapid scaling of Genomic Foundation Models (GFMs) has created a critical need for standardized evaluation frameworks. Current benchmarking practices are often fragmented, relying on model-specific preprocessing and inconsistent metric implementat...
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Pan-cell-type prediction of splicing patterns from sequence and splicing factor expression

bioRxiv Feb 19, 2026
Alternative splicing is a core determinant of cell-type-specific gene expression in humans, and its dysregulation contributes to many diseases including neurodegeneration, autoimmunity, and cancer. However, current deep learning models for predicting...
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