Survey of interactive evolutionary decomposition-based multiobjective optimization methods.

Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference information iteratively during the solution process to find solutions of interest, allowing them to learn about the trade-offs in the problem and the feasibility of the preferences. Several interactive evolutionary multiobjective optimization methods have been proposed in the literature.

Structure-Agnostic Bioactivity-Driven Combinatorial Biosynthesis Reveals New Antidiabetic and Anticancer Triterpenoids.

Although combinatorial biosynthesis can dramatically expand the chemical structures of bioactive natural products to identify molecules with improved characteristics, progress in this direction has been hampered by the difficulty in isolating and characterizing the numerous produced compounds. This challenge could be overcome with improved designs that enable the analysis of the bioactivity of the produced metabolites ahead of the time-consuming isolation procedures. Herein, we showcase a structure-agnostic bioactivity-driven combinatorial biosynthesis workflow that introduces bioactivity assessment as a selection-driving force to guide iterative combinatorial biosynthesis rounds towards enzyme combinations with increasing bioactivity.

Celastrol: A century-long journey from the isolation to the biotechnological production and the development of an antiobesity drug.

Celastrol, a triterpenoid found in the root of the traditional medicinal plant Tripterygium wilfordii, is a potent anti-inflammatory and antiobesity agent. However, pharmacological exploitation of celastrol has been hindered by the limited accessibility of plant material, the co-existence of other toxic compounds in the same plant tissue, and the lack of an efficient chemical synthesis method. In this review, we highlight recent progress in elucidating celastrol biosynthesis and discuss how this knowledge can facilitate its scalable bioproduction using cell factories and its further development as an antiobesity and anti-inflammatory drug.

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Introduction: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals' work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD.

Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study.

Background: Health care budgets are limited, requiring the optimal use of resources. Machine learning (ML) methods may have an enormous potential for effective use of health care resources.

Objective: We assessed the applicability of selected ML tools to evaluate the contribution of known risk markers for prognosis of coronary artery disease to predict health care costs for all reasons in patients with a recent acute coronary syndrome (n = 65, aged 65 ± 9 years) for 1-year follow-up.