Department of Computer Science

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Subject: search.filters.subject.Bioinformatics

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    Enhancing Automated Question Tagging: Integrating Large Language Models with Traditional Retrieval Methods
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Bilal Hussian; CIIT/SP24-RCS-016/LHR; Dr. Ghulam Rasool; LHR TP 10038
    When people ask questions on platforms like Stack Overflow or Quora, the system needs to automatically assign relevant tags to help others find and answer those questions quickly. This is called automatic question tagging and is quite essential since it directly influences the efficiency with which the user can receive help. The majority of existing tagging systems just examine the words of the question itself and the tags accessible in the database. They are deprived of expansive real-world information that can guide them in making superior tagging decisions. Large language models such as GPT-4 are very good at context, real-world understanding, and this may be invaluable in this case. The issue is, these models cannot be confident in selecting tags out of a list of predefined tags of a particular site that they may propose tags that do not exist in the system at all. To address this problem, I created EAQTILLM (Enhancing Automated Question Tagging Integrating Large Language Model). My method is in two steps: I will first run a tagging model that will give me a shortlist of possible tags based on the database of the website. I then run the question through a large language model with prompts that are designed to explore the question in more depth and choose the most suitable tags using that shortlist. By doing this we get the advantage of both worlds the ability of the LLM to understand and the restriction of just being able to pick out of valid and existing tags. I applied EAQTILLM (Test) to two real-life datasets, achieving a precision of 0.4952, recall of 0.6667, and accuracy of 0.9842, which demonstrated remarkable performance. The algorithm was always ahead of current state-of-the-art algorithms, demonstrating that the combination of the traditional ones and large language models is a good direction to enhance automatic question tagging in online communiti
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    Histone Explorer: AI-Enhanced Identification and Characterization of Histone Modification
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Umar Farooq; CIIT/SP24-RCS-017/LHR; Dr. Farooq Ahmad; LHR TP 10039
    Histone modifications are one of the key epigenetic mechanisms to control the structure of chromatids and gene expression and play a pivotal role in cellular differentiation, development, and pathogenesis. In spite of the fact that Chromatin Immunoprecipitation Sequencing (ChIP-seq) is an experimental methodology that allows identifying histone marks through the use of high-resolution sequencing, it is expensive, labor-intensive and has low scalability, preventing its general use in a wide range of biological studies. To overcome these difficulties, this research suggests to consider Histone Explorer as an AI-enhanced computational system of the correct recognition and description of sites of histone modifications directly on the basis of genomic sequence information. The suggested framework will consist of a bioinformatics preprocessing pipeline that is reproducible, and sophisticated artificial intelligence models. Peaks of histone modification genomic intervals were obtained at the public repositories of epigenomic repositories such as ENCODE and Roadmap Epigenomics and mapped to a reference genome, hg38, to produce a curated FASTA sequence dataset. An extensive feature extraction scheme was used in order to obtain local and global sequence features, such as nucleotide frequency patterns, k-mer representations, positional features, pairwise interaction features, and higher-order statistical moments. It was trained and tested on the following models: CNN, BiLSTM, GRU, and attention- based transformer models among various models trained using machine learning, deep learning, and transformer-based architectures. There were 100,000 positive and negative samples of Histone sequences in the dataset. The performance of the models was strictly tested with independent test sets and using the k-fold cross-validation protocols. The experimental findings show that the transformer-based Histone Explorer model performs better than both classical and deep learning baselines with an accuracy of 91.5, precision of 90.2, recall of 92.8 and F1-score of 91.5 and AUC- ROC of 95.0. Such findings demonstrate that there is high generalization properties and strength over a wide spectrum of genomic regions. All in all, Histone Explorer offers a scalable, precise, and biologically meaningful AI-based framework of histone modifications identification. The proposed system can provide a useful computational resource in epigenetics studies, biomarker discovery, and precision-medicine studies because it lowers reliance on expensive experimental studies and allows analyzing epigenomics on a high-throughput.