arXiv subjects

Mehrdad Jalali

Publications and source records attributed to Mehrdad Jalali.

10 recordsLinked to original sources

arXiv2026

ECHO-PPI: Evidence-Bundled Overlapping Protein Module Detection with Hierarchical Assignment Confidence for Network Biology

Identifying protein modules in protein-protein interaction (PPI) networks is central to understanding cellular organisation, yet many community-detection methods treat module membership as a binary output with limited assignment-level justification. Proteins that participate in multiple complexes as shared subunits, peripheral interactors, or context-dependent bridges can be overlooked when networks are forced into hard partitions, and even overlapping methods rarely provide traceable evidence for individual protein-module assignments. We present ECHO-PPI, a framework for overlapping protein-module detection that combines competitive module discovery with structured assignment-level interpretation. For every protein-module assignment, ECHO-PPI exports an evidence bundle combining weighted topology, semantic functional similarity, Gene Ontology support, provenance fields, and hierarchical confidence labels: Core, Inner, Outer, and Uncertain. This makes each assignment inspectable and reproducible rather than an opaque membership claim. We benchmark ECHO-PPI on two yeast PPI resources, the Gavin socioaffinity network and the Krogan 2006 dataset, against MCL, MCL+overlap, ClusterONE, and SLPA. ECHO-PPI achieves predictive parity with overlap-aware baselines while being the only evaluated method to provide complete required-field evidence bundles. Core assignments show the strongest gold-standard support and consistent multi-channel evidence across both datasets. By separating predictive clustering from evidence-bundled interpretation, ECHO-PPI provides computational biologists with a path from cluster lists to defensible, reproducible protein-module hypotheses suitable for curator-facing network-biology workflows.

cs.SI↗

arXiv2026

Beyond Network Topology: Biological Evidence Integration and Reproducible Benchmarking for Protein Complex Detection

Protein complexes are molecular assemblies that coordinate cellular regulation, signaling, metabolism, and disease-relevant protein function. Detecting such assemblies from protein-protein interaction (PPI) networks remains challenging because network topology is an incomplete abstraction: an edge may represent direct binding, functional association, co-complex evidence, co-expression, co-localization, or a computationally predicted interaction. This focused critical methodological review examines how biological evidence can improve protein-complex detection beyond dense-subgraph discovery. We consider Gene Ontology, expression, localization, domains and motifs, sequence and structure, interface evidence, temporal context, RNA or regulatory evidence, and representation learning, while retaining classical graph-clustering methods as historical baselines. Interpretable evidence-aware graph methods currently provide a strong balance between biological plausibility and reproducibility, whereas structure-aware, temporal, heterogeneous, and hypergraph models offer greater biological realism but require stronger independent benchmarking. Reported F-measures cannot be directly compared across incompatible PPI releases, reference sets, matching thresholds, preprocessing pipelines, and metric implementations. Progress therefore requires fixed dataset versions, explicit controls for Gene Ontology circularity, overlap-aware metrics, uncertainty estimates, and executable software packages. Reliable protein-complex detection ultimately depends on connecting graph-based predictions to molecular structure, interaction mechanisms, cellular context, and functional assembly.

cs.SI↗

arXiv2026

CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity

Editors and reviewers are expected to ensure that manuscripts cite relevant, accurate, current, and ethically appropriate literature, yet manuscript-level citation auditing remains largely manual, fragmented, and difficult to scale. Citation context, metadata quality, self-citation patterns, and bibliographic integrity all affect whether a reference appropriately supports a local claim. We present CitePrism, a transparent hybrid decision-support framework for editorial citation auditing that combines LLM-assisted contextual reasoning, embedding-based semantic similarity, metadata verification, integrity-oriented flags, and human-in-the-loop analyst review. CitePrism extracts citation neighborhoods, enriches reference metadata, computes fused relevance scores, surfaces metadata and self-citation review prompts, and supports configurable threshold-based triage. In a preliminary validation on a single case-study manuscript with 104 references from pavement engineering, agreement with human binary relevance labels reached Cohen's kappa = 0.429. At operating threshold tau = 17, CitePrism flagged all human-labeled irrelevant citations, while also producing false positives requiring analyst review. These results suggest that CitePrism may support conservative editorial screening and citation-quality triage, but they do not establish general editorial performance. CitePrism is intended as pilot-stage decision support, not as an autonomous misconduct detector or automated editorial decision system. Broader validation across manuscripts, domains, annotators, baselines, and deployment settings is required before operational use.

cs.SI↗

arXiv2026

Astro Generative Network: A Variational Framework for Controlled Node Insertion in Incomplete Complex Networks

Empirical networked systems are often only partially observed: sampling frames, crawling policies, privacy constraints, and temporal gaps can leave actors and edges unobserved. This complicates robustness and sensitivity analysis because many graph-learning pipelines implicitly treat the observed node set as exhaustive. Link prediction and graph completion repair structure among known vertices, whereas full-graph generators synthesize new graphs rather than extending an observed one as a fixed backbone. We study the complementary task of controlled node insertion: generating plausible new actors and attaching them to an existing graph while preserving interpretable global topology. We introduce the Astro Generative Network (AGN), a variational graph autoencoder that samples latent vectors to decode node features and then integrates new vertices through similarity-based attachment to the observed backbone. We distinguish the recommended configuration, AGN, from AGN-original, a diagnostic baseline that permits generated-generated edges. Across three synthetic regimes, AGN-original forms dense generated-generated subgraphs that artificially inflate clustering and density. Disabling those edges removes this artifact while preserving degree and path-length behavior. In our experiments, AGN keeps clustering and modularity changes modest relative to pre-insertion values, while novelty diagnostics show non-trivial separation from existing nodes without claiming domain-grounded identities. Our contribution is methodological: a reproducible insertion protocol and evaluation lens for incomplete network science and engineering

cs.SI↗

arXiv2023

MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterization

This paper introduces a new ontology for Materials Science Laboratory Equipment, termed MSLE. A fundamental issue with materials science laboratory (hereafter lab) equipment in the real world is that scientists work with various types of equipment with multiple specifications. For example, there are many electron microscopes with different parameters in chemical and physical labs. A critical development to unify the description is to build an equipment domain ontology as basic semantic knowledge and to guide the user to work with the equipment appropriately. Here, we propose to develop a consistent ontology for equipment, the MSLE ontology. In the MSLE, two main existing ontologies, the Semantic Sensor Network (SSN) and the Material Vocabulary (MatVoc), have been integrated into the MSLE core to build a coherent ontology. Since various acronyms and terms have been used for equipment, this paper proposes an approach to use a Simple Knowledge Organization System (SKOS) to represent the hierarchical structure of equipment terms. Equipment terms were collected in various languages and abbreviations and coded into the MSLE using the SKOS model. The ontology development was conducted in close collaboration with domain experts and focused on the large-scale devices for materials characterization available in our research group. Competency questions are expected to be addressed through the MSLE ontology. Constraints are modeled in the Shapes Query Language (SHACL); a prototype is shown and validated to show the value of the modeling constraints.

cs.AI↗

arXiv2021

Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering

Data mining techniques can be used to discover useful patterns by exploring and analyzing data and it's feasible to synergitically combine machine learning tools to discover fuzzy classification rules.In this paper, an adaptive Neuro fuzzy network with TSK fuzzy type and an improved quantum subtractive clustering has been developed. Quantum clustering (QC) is an intuition from quantum mechanics which uses Schrodinger potential and time-consuming gradient descent method. The principle advantage and shortcoming of QC is analyzed and based on its shortcomings, an improved algorithm through a subtractive clustering method is proposed. Cluster centers represent a general model with essential characteristics of data which can be use as premise part of fuzzy rules.The experimental results revealed that proposed Anfis based on quantum subtractive clustering yielded good approximation and generalization capabilities and impressive decrease in the number of fuzzy rules and network output accuracy in comparison with traditional methods.

cs.AI↗

arXiv2020

A new method for community detection in social networks based on message distribution

Social networks are the social structures which are composed of people and their relationships and nowadays, play an important role in data extension. In such networks, the communities are recognized as the groups of users who are often interacting with each other. In this article, a method will be introduced for community detection, which has the capability of adoption with different kinds of social networks and also is synchronized with the actual world. One of the most important defined parameters in this paper is the rate of the transferred messages between the nodes of the network, this parameter would be dynamically investigated. In this strategy, the network is reviewed in different time intervals, and the inter-node relations are enhanced or weakened. Therefore, the topology of the network is continuously changing in response to the behavior of the users. The defined parameters in the proposed algorithm are capable of adopting with different types of the social networks and a weight will be assigned to every parameter which is indicative of the relative importance of that parameter in comparison with the other ones. The obtained results show that this method, in comparison with the similar methods, leads to achievement of the desirable results.

cs.SI↗

arXiv2019

A decentralized trust-aware collaborative filtering recommender system based on weighted items for social tagging systems

Recommender systems are used with the purpose of suggesting contents and resources to the users in a social network. These systems use ranks or tags each user assign to different resources to predict or make suggestions to users. Lately, social tagging systems, in which users can insert new contents, tag, organize, share, and search for contents are becoming more popular. These systems have a lot of valuable information, but data growth is one of its biggest challenges and this has led to the need for recommender systems that will predict what each user may like or need. One approach to the design of these systems which uses social environment of users is known as collaborative filtering (CF). One of the problems in CF systems is trustworthy of users and their tags. In this work, we consider a trust metric (which is concluded from users tagging behavior) beside the similarities to give suggestions and examine its effect on results. On the other hand, a decentralized approach is introduced which calculates similarity and trust relationships between users in a distributed manner. This causes the capability of implementing the proposed approach among all types of users with respect to different types of items, which are accessed by unique id across heterogeneous networks and environments. Finally, we show that the proposed model for calculating similarities between users reduces the size of the user-item matrix and considering trust in collaborative systems can lead to a better performance in generating suggestions.

cs.SI↗

arXiv2018

Applying an Ensemble Learning Method for Improving Multi-label Classification Performance

In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and combines their outcomes to make an estimation. The simplest form of ensemble learning is to train the base-level algorithms on random subsets of data and then let them vote for the most popular classifications or average the predictions of the base-level algorithms. In this study, an ensemble learning method is proposed for improving multi-label classification evaluation criteria. We have compared our method with well-known base-level algorithms on some data sets. Experiment results show the proposed approach outperforms the base well-known classifiers for the multi-label classification problem.

cs.LG↗

arXiv2014

An Optimized Semantic Web Service Composition Method Based on Clustering and Ant Colony Algorithm

In today's Web, Web Services are created and updated on the fly. For answering complex needs of users, the construction of new web services based on existing ones is required. It has received a great attention from different communities. This problem is known as web services composition. However, it is one of big challenge problems of recent years in a distributed and dynamic environment. Web services can be composed manually but it is a time consuming task. The automatic web service composition is one of the key features for future the semantic web. The various approaches in field of web service compositions proposed by the researchers. In this paper, we propose a novel architecture for semantic web service composition using clustering and Ant colony algorithm.

cs.SE↗