arXiv subjects

Lawrence KQ Yan

Publications and source records attributed to Lawrence KQ Yan.

6 recordsLinked to original sources

arXiv2026

From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings

Translating computational predictions into experimentally validated biological knowledge remains one of the central challenges in modern bioinformatics. This review critically examines how in silico findings can be prioritized, tested, and interpreted through experimental validation. We organize the validation process around three recurring challenges: the specificity gap between genome-wide computational predictions and single-target experimental assays; the reproducibility-translatability tension, in which results validated in one model system may not generalize to another; and the scale-depth trade-off between high-throughput candidate discovery and the low-throughput nature of mechanistic validation. Rather than presenting an encyclopedic catalogue of techniques, we compare the strengths, limitations, and common failure modes of major validation approaches, including qPCR, RNA-seq, Western blotting, co-immunoprecipitation, luciferase reporter assays, CRISPR perturbation, and functional phenotypic assays. We also provide structured comparison tables for gene expression, protein-protein interaction, non-coding RNA, regulatory element, and pathway validation, together with decision-making frameworks to guide method selection according to prediction type, biological context, evidence stringency, throughput, and resource constraints. Case studies from cancer genomics, drug target discovery, miRNA regulation, and neurological disease illustrate how multi-step validation workflows can strengthen causal inference and reduce false-positive interpretation. Finally, we discuss how emerging technologies, including CRISPR screens, single-cell and spatial multi-omics, and AI-assisted experimental design, may reshape validation practice by improving scalability, context specificity, and reproducibility.

q-bio.GN↗

arXiv2025

From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models

Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such as the distributional hypothesis and contextual similarity, tracing the evolution from sparse representations like one-hot encoding to dense embeddings including Word2Vec, GloVe, and fastText. We examine both static and contextualized embeddings, underscoring advancements in models such as ELMo, BERT, and GPT and their adaptations for cross-lingual and personalized applications. The discussion extends to sentence and document embeddings, covering aggregation methods and generative topic models, along with the application of embeddings in multimodal domains, including vision, robotics, and cognitive science. Advanced topics such as model compression, interpretability, numerical encoding, and bias mitigation are analyzed, addressing both technical challenges and ethical implications. Additionally, we identify future research directions, emphasizing the need for scalable training techniques, enhanced interpretability, and robust grounding in non-textual modalities. By synthesizing current methodologies and emerging trends, this survey offers researchers and practitioners an in-depth resource to push the boundaries of embedding-based language models.

cs.CL↗

arXiv2025

Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges

This comprehensive review explores the intersection of Large Language Models (LLMs) and cognitive science, examining similarities and differences between LLMs and human cognitive processes. We analyze methods for evaluating LLMs cognitive abilities and discuss their potential as cognitive models. The review covers applications of LLMs in various cognitive fields, highlighting insights gained for cognitive science research. We assess cognitive biases and limitations of LLMs, along with proposed methods for improving their performance. The integration of LLMs with cognitive architectures is examined, revealing promising avenues for enhancing artificial intelligence (AI) capabilities. Key challenges and future research directions are identified, emphasizing the need for continued refinement of LLMs to better align with human cognition. This review provides a balanced perspective on the current state and future potential of LLMs in advancing our understanding of both artificial and human intelligence.

cs.AI↗

arXiv2025

From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice

Large Language Models (LLMs) have rapidly evolved from text-based systems to multimodal platforms, significantly impacting various sectors including healthcare. This comprehensive review explores the progression of LLMs to Multimodal Large Language Models (MLLMs) and their growing influence in medical practice. We examine the current landscape of MLLMs in healthcare, analyzing their applications across clinical decision support, medical imaging, patient engagement, and research. The review highlights the unique capabilities of MLLMs in integrating diverse data types, such as text, images, and audio, to provide more comprehensive insights into patient health. We also address the challenges facing MLLM implementation, including data limitations, technical hurdles, and ethical considerations. By identifying key research gaps, this paper aims to guide future investigations in areas such as dataset development, modality alignment methods, and the establishment of ethical guidelines. As MLLMs continue to shape the future of healthcare, understanding their potential and limitations is crucial for their responsible and effective integration into medical practice.

cs.CY↗

arXiv2025

Advanced Deep Learning Methods for Protein Structure Prediction and Design

After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.

q-bio.BM↗

arXiv2025

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.

cs.AI↗