International Journal of Bioinformatics and Intelligent Computing

Revolutionizing Anti-Cancer Drug Discovery: The Role of Artificial Intelligence

2025-02-28T22:25:17-06:00

This comprehensive review explores the integration of Artificial Intelligence (AI) in anticancer drug discovery, highlighting its transformative impact on streamlining the identification, design, and synthesis of novel drug molecules. Leveraging expansive datasets, AI and machine learning technologies enhance the understanding of cancer biology, facilitate target identification, and accelerate the design of molecules with desirable pharmacological properties. Despite promising advancements, challenges persist, including issues related to data quality, model interpretability, and the practical application of AI-generated findings in clinical settings. This review critically examines these challenges, proposes advanced AI models for drug combination predictions, and advocates for collaborative efforts to refine and implement AI methodologies in clinical oncology. The potential of AI to revolutionize anticancer drug discovery is immense, providing a new paradigm that merges precision with efficiency to push the boundaries of therapeutic innovation. Through rigorous validation and interdisciplinary cooperation, AI-driven strategies hold the promise to significantly shorten drug development timelines and improve clinical outcomes, ushering in a new era of personalized medicine in cancer treatment.

A Study on Repurposing of Antibiotic Drugs for Human MMPs Enzyme: A Possible Hope for Arthritis Drug

2025-02-28T22:25:17-06:00

Arthritis is a prevalent condition that primarily affects elderly individuals, especially women. Matrix Metallo proteinases (MMPs), specifically types 1,2,3,9 and 13 are key players in the progression of arthritis and represent promising drug targets for treatment. Despite this, there is a significant gap in research aimed at targeting human MMPs (hMMPs) with therapeutic agents. This computational study confidently proposes the repurposing of existing twenty antibiotic drugs to combat hMMPs 1,2,3,9 and 13. Through comprehensive molecular docking analysis, four critical binding sites (BS): BS1 (catalytic Zn2+ ion), BS2 (R2 site), BS3 (R3 site), and BS4 (R4 site) are investigated. Computational studies reveal that the leading candidates—(i) Tedizolid, (ii) Ceftobiprole, (iii) Mupirocin, and (iv) Delafloxacin—exhibit strong binding affinities based on both binding energy and average binding energy. Given the current lack of experimental data, present study assert that Tedizolid, Ceftobiprole, and Mupirocin are highly promising options for arthritis treatment due to their robust interactions with specific hMMP binding sites. Delafloxacin, with its favorable QSAR and ADMET properties, also demands further investigation. In summary, these four antibiotic drugs present excellent opportunities for advancing experimental and pre-clinical studies aimed at developing effective treatments for arthritis.

Handling Missing Data in Real-World Evidence Studies Managing Missing Data in RWE Studies

2025-02-28T22:25:17-06:00

Missing data is a pervasive challenge in real-world evidence (RWE) studies, arising from incomplete or inconsistent data collection. Proper handling of missing data is critical to ensure the validity and reliability of study outcomes. This paper explores strategies to address missingness, focusing on mechanisms, methods, and tools available to researchers.

Understanding the underlying mechanism of missingness—Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR)—is the foundation of effective data management. Methods such as complete case analysis and single imputation are suitable for MCAR scenarios but may introduce bias or underestimate variability. Advanced approaches, like multiple imputation and maximum likelihood estimation, better address MAR data, preserving uncertainty and improving robustness. For MNAR cases, sensitivity analyses are essential to evaluate the impact of missingness on study conclusions.

Innovative tools in R, including mice, missForest, VIM, and naniar, enable effective imputation, visualization, and modeling of missing data. Machine learning techniques and Bayesian approaches offer promising alternatives for complex datasets. Combining methods, such as multiple imputation followed by sensitivity analysis, ensures more reliable inferences.

Best practices emphasize assessing missingness patterns, transparent documentation of assumptions, and thorough reporting of strategies used to address missing data. Adopting these approaches minimizes bias and enhances the credibility of RWE studies, ultimately supporting better-informed healthcare decisions. This paper underscores the importance of a systematic, informed approach to handling missing data in RWE.

Variation in Genes and the Demography of Giberellin Producer Fusarium fujikuroi, a Pathogen that Triggers Bakanae Disease and its Industrial Importance

2025-02-28T22:25:17-06:00

The review paper delves into the genetic variability, population dynamics, and commercial relevance of Fusarium fujikuroi, a type of fungus that is recognized for its participation in the synthesis of Gibberellin, a set of phytohormones that exert a wide range of effects on plant growth and development. This review aims to explicate the mechanisms that underlie the involvement of F. fujikuroi in the production of Gibberellin and its consequential effects on agriculture and associated industries. This article presents a comprehensive literature review of the genetic diversity in F. fujikuroi, explores the determinants that shape its demographic patterns, and scrutinizes its correlation with Bakanae disease. Furthermore, the industrial importance of F. fujikuroi in synthesizing Gibberellins and its possible utilization in diverse industries are thoroughly examined. The fungus F. fujikuroi, which exists in a multicellular form, is responsible for the onset of Bakanae disease, posing a significant risk to a wide range of crop plants. This review accentuates the chronological advancements in comprehending F. fujikuroi and Bakanae ailment, underscoring the paramount importance of Gibberellin biosynthesis by the fungal pathogen. This review delves into the genetic diversity present in F. fujikuroi and the pivotal genes implicated in the biosynthesis of Gibberellin. Additionally, the mechanisms governing the regulation of gene expression are examined. The results of this review enhance comprehension of the genetic diversity, demographic attributes, and industrial relevance of F. fujikuroi, thereby facilitating forthcoming investigations and progressions in this diverse fungal species.