Ahmad Shamail | Searcharxiv

arXiv2025

A General Algorithm for Detecting Higher-Order Interactions via Random Sequential Additions

Many systems exhibit complex interactions between their components: some features or actions amplify each other's effects, others provide redundant information, and some contribute independently. We present a simple geometric method for discovering interactions and redundancies: when elements are added in random sequential orders and their contributions plotted over many trials, characteristic L-shaped patterns emerge that directly reflect interaction structure. The approach quantifies how the contribution of each element depends on those added before it, revealing patterns that distinguish interaction, independence, and redundancy on a unified scale. When pairwise contributions are visualized as two--dimensional point clouds, redundant pairs form L--shaped patterns where only the first-added element contributes, while synergistic pairs form L--shaped patterns where only elements contribute together. Independent elements show order--invariant distributions. We formalize this with the L--score, a continuous measure ranging from $-1$ (perfect synergy, e.g. $Y=X_1X_2$) to $0$ (independence) to $+1$ (perfect redundancy, $X_1 \approx X_2$). The relative scaling of the L--shaped arms reveals feature dominance in which element consistently provides more information. Although computed only from pairwise measurements, higher--order interactions among three or more elements emerge naturally through consistent cross--pair relationships (e.g. AB, AC, BC). The method is metric--agnostic and broadly applicable to any domain where performance can be evaluated incrementally over non-repeating element sequences, providing a unified geometric approach to uncovering interaction structure.

cs.LG↗

arXiv2025

Automated Protein Motif Localization using Concept Activation Vectors in Protein Language Model Embedding Space

We present an automated approach for identifying and annotating motifs and domains in protein sequences, using pretrained Protein Language Models (PLMs) and Concept Activation Vectors (CAVs), adapted from interpretability research in computer vision. We treat motifs as conceptual entities and represent them through learned CAVs in PLM embedding space by training simple linear classifiers to distinguish motif-containing from non-motif sequences. To identify motif occurrences, we extract embeddings for overlapping sequence windows and compute their inner products with motif CAVs. This scoring mechanism quantifies how strongly each sequence region expresses the motif concept and naturally detects multiple instances of the same motif within the same protein. Using a dataset of sixty-nine well-characterized motifs with curated positive and negative examples, our method achieves over 85\% F1 Score for segments strongly expressing the concept and accurately localizes motif positions across diverse protein families. As each motif is encoded by a single vector, motif detection requires only the pretrained PLM and a lightweight dictionary of CAVs, offering a scalable, interpretable, and computationally efficient framework for automated sequence annotation.

q-bio.QM↗