Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 0708.0694

Reconstruction of Protein-Protein Interaction Pathways by Mining Subject-Verb-Objects Intermediates

Maurice HT Ling·Christophe Lefevre·Kevin R. Nicholas·Feng Lin
View original source ↗Read source document ↗Export metadata ↓

Abstract

The exponential increase in publication rate of new articles is limiting access of researchers to relevant literature. This has prompted the use of text mining tools to extract key biological information. Previous studies have reported extensive modification of existing generic text processors to process biological text. However, this requirement for modification had not been examined. In this study, we have constructed Muscorian, using MontyLingua, a generic text processor. It uses a two-layered generalization-specialization paradigm previously proposed where text was generically processed to a suitable intermediate format before domain-specific data extraction techniques are applied at the specialization layer. Evaluation using a corpus and experts indicated 86-90% precision and approximately 30% recall in extracting protein-protein interactions, which was comparable to previous studies using either specialized biological text processing tools or modified existing tools. Our study had also demonstrated the flexibility of the two-layered generalization-specialization paradigm by using the same generalization layer for two specialized information extraction tasks.

Explore related subjects

cs.IRcs.CLcs.DL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maurice HT Ling, Christophe Lefevre, Kevin R. Nicholas, Feng Lin. 2007-08-06. Reconstruction of Protein-Protein Interaction Pathways by Mining Subject-Verb-Objects Intermediates. https://arxiv.org/abs/0708.0694

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Offline A/B Testing of Slate Recommendation Systems with LLMs: Reducing the Dependency on Pre-Collected User Interaction Data

Slate recommender systems (RecSys) present users with ordered sets of interacting items (e.g., playlists). We investigate whether large language models (LLMs) can articulate pairwise preferences between slates for synthetic A/B testing of slate RecSys. We introduce a validation protocol measuring the alignment of synthetic preferences with classical RecSys metrics and their compliance with preference axioms, and use it to characterise how LLM pre-training and configuration affect slate preference articulation. Combined with the generalized Rao-Kupper model, synthetic LLM-based A/B testing recovers rankings that remain stable across utility weightings, whereas off-policy estimators are reliable only when the target utility matches the logged behavior. We position it as a screening stage between off-policy evaluation and live experiments: not a replacement for A/B testing, but a way to reserve its cost for the most promising candidates.

cs.IR↗
arXiv2026

MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval

Existing retrieval benchmarks primarily consist of text-based queries where keyword or semantic matching is usually sufficient. Many real-world queries contain multimodal elements, particularly, images such as diagrams, charts, and screenshots that require intensive reasoning to identify relevant documents. To address this gap, we introduce MM-BRIGHT, the first multimodal benchmark for reasoning-intensive retrieval. Our dataset consists of 2,803 real-world queries spanning 29 diverse technical domains, with four tasks of increasing complexity: text-to-text, multimodal-to-text, multimodal-to-image, and multimodal-to-multimodal retrieval. Extensive evaluation reveals that state-of-the-art models struggle across all tasks: BM25 achieves only 8.5 nDCG@10 on text-only retrieval, while the best multimodal model Nomic-Vision reaches just 27.6 nDCG@10 on multimodal-to-text retrieval actually underperforming the best text-only model (DiVeR: 32.2). These results highlight substantial headroom and position MM-BRIGHT as a testbed for next-generation retrieval models that better integrate visual reasoning. Our code and data are available at https://github.com/mm-bright/MM-BRIGHT. See also our official website: https://mm-bright.github.io/.

cs.IR↗
arXiv2026

From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search

Traditional Web search follows a human-facing paradigm in which users inspect ranked documents and synthesize information themselves. In AI Search, retrieved documents instead serve as inputs to a generation model, shifting the retrieval objective from ranking documents by Search Satisfaction to constructing reliable context for correct answer generation. We formulate this shift as answer-oriented context construction through a three-stage framework: (1) Answer Support identifies candidate documents that contribute information to answer generation; (2) Content Trustworthiness assesses whether this information provides a reliable basis for correct answers from source, temporal, and factual perspectives; and (3) Context Organization selects, consolidates, and structures retained information under a finite context budget for consistent and robust generation. We further develop an industrial workflow spanning prior and posterior optimization and establish a systematic evaluation protocol covering both retrieval-side context and final answers. Experiments show consistent improvements at both Retrieval and Answer levels, demonstrating the effectiveness of the framework and its industrial implementation.

cs.IR↗