Machine learning classification of archaea and bacteria identifies novel predictive genomic features.

Background: Archaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria ( ) and archaea ( ).

TumFlow: An AI Model for Predicting New Anticancer Molecules.

Melanoma is the fifth most common cancer in the United States. Conventional drug discovery methods are inherently time-consuming and costly, which imposes significant limitations. However, the advent of Artificial Intelligence (AI) has opened up new possibilities for simulating and evaluating numerous drug candidates, thereby mitigating the requisite time and resources.

Transcriptomics Studies Reveal Functions of Transglutaminase 2 in Breast Cancer Cells Using Membrane Permeable and Impermeable Inhibitors.

Transglutaminase 2 (TG2) performs many functions both under physiological and pathological conditions. In cancer, its expression is associated with aggressiveness, propensity to epithelial-mesenchymal transition, and metastasis. Since TG2 performs key functions both outside and inside the cell, using inhibitors with different membrane permeability we analyzed the changes in the transcriptome induced in two triple-negative cell lines (MDA-MB-436 and MDA-MB-231) with aggressive features.

State-of-the-art in transposable element modulation affected by drugs in malignant prostatic cancer cells.

Over recent years, the investigation of transposable elements (TEs) has granted researchers a deeper comprehension of their characteristics and functions, particularly regarding their significance in the mechanisms contributing to cancer development. This manuscript focuses on prostate carcinoma cell lines and offers a comprehensive review intended to scrutinize the associations and interactions between TEs and genes, as well as their response to treatment using various chemical drugs, emphasizing their involvement in cancer progression. We assembled a compendium of articles retrieved from the PubMed database to construct networks demonstrating correlations with genes and pharmaceuticals.