Predicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes. In this paper, we propose a novel graph mining algorithm (PEWCC) to identify such protein complexes. Firstly, the algorithm assesses the reliability of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of the proposed method, the performance of PEWCC was compared to several methods. PEWCC was able to detect more matched complexes than any of the state-of-the-art methods with higher quality scores. The higher accuracy achieved by PEWCC in detecting protein complexes is a valid argument in favor of the proposed method. The datasets and programs are freely available at http://faculty.uaeu.ac.ae/nzaki/Research.htm .

Experimental measurements on a cm-scale replica structure of a gecko foot-hair where magnets are used in place of (the usual) van der Waals force are reported. We conduct naked-eye experiments and investigate the mechanical properties of such hair structure and shapes that constitute it. Links between shapes and mechanical properties (functions) useful in geckos for clinging onto walls and adhering to rough surfaces are explained in terms of energy efficiency.

We introduce an emoticon-bin, a recycle bin that rewards users with smiles and sounds. We show that by exploiting human responsiveness to emoticons, recycling rates increase by a factor of x3.

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. Observations show that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. This paper introduces a novel method for detecting protein-complexes from PPI by using a protein ranking algorithm (ProRank) and incorporating an evolutionary relationships between proteins in the network. The method successfully predicted 57 out of 81 benchmarked protein complexes created from the Munich Information Center for Protein Sequence (MIPS). The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of our proposed method. Datasets, programs and results are available at http://faculty.uaeu.ac.ae/nzaki/ProRank.htm.

Large Language Models (LLMs) are predictive probabilistic models capable of passing several professional tests at a level comparable to humans. However, these capabilities come with ethical concerns. Ethical oversights in several LLM-based products include: (i) a lack of content or source attribution, and (ii) a lack of transparency in what was used to train the model. This paper identifies four touchpoints where ethical safeguards can be applied to realize a more responsible AI in LLMs. The key finding is that applying safeguards before the training occurs aligns with established engineering practices of addressing issues at the source. However, this approach is currently shunned. Finally, historical parallels are drawn with the US automobile industry, which initially resisted safety regulations but later embraced them once consumer attitudes evolved.