Entromics

What We Do Differently

Mon, 09 Mar 2026 07:07:07 +0100

Comparison of Personalized Network Medicine to the typical existing data-processing paradigm:

Maximal entropy principle guarantees that no other method can extract more relevant clinical information than Personalized Network Medicine.

Mathematical validation of this statement is briefly summarized here.

Key to the direct clinical relevance of PNM is the maximal compatibility of generated models with the actual observed clinical data:

Personalized Network Medicine (PNM)

Sun, 08 Mar 2026 07:07:07 +0100

Representative results for predicting complications after surgical stent insertion by Personalized Network Medicine processing of electronic medical records.

PNM provides individual risk ratio of post-procedure complications and quantitative model for “What if” simulation of personal, environmental and clinical factors impact on the complication risk.

Entromics

Sat, 07 Mar 2026 07:07:07 +0100

Genome sequence contains information which — when used as input into an exact thermodynamic formula — is recalculated into the energy needed to incorporate a variant base into any specific position in the DNA strand. This led to the discovery of coherence for these incorporation entropies along the complete genome sequence. A short explanation of this phenomenon and how it is quantitatively characterized can be found here. For more details see our paper “Entromics — thermodynamics of sequence dependent base incorporation into DNA reveals novel long-distance genome organization”.

PNM and Adverse Effects in Medicine

Fri, 06 Mar 2026 07:07:07 +0100

PNM pattern-based characterization of the clinical profiles opens the “missing half” of the information content in standard data used for clinical prognostication: the information supporting clinical decisions can be based both on the PRESENCE as well as on the ABSENCE of characteristic clinical patterns:

Comparative Efficiency in Periodontal Disease Diagnosis

Thu, 05 Mar 2026 07:07:07 +0100

Time to Disease Onset and Survival Prognosis in Cancer

Wed, 04 Mar 2026 07:07:07 +0100

This application uses results published in the book B. Roy Frieden, Science from Fisher Information. A Unification, Cambridge University Press, 2004, chapters 1, 14, 15. The complete mathematical derivation of the determination of individual times to the onset of hepatocellular cancer growth, using the tumor masses (as determined for respective patients by CT imaging at the “baseline” clinical examination), can be found in the text, linked HERE.

Fisher Information Analysis of Syntax Score

Tue, 03 Mar 2026 07:07:07 +0100

Why Fisher information processing?

For clinical-practice data we typically do not have a “control” patient cohort, so we cannot use the fact that a normal distribution describes only the fully stochastic variation in a patient’s data to extract the “true”, clinically relevant information from case/control comparisons.

Thus, if we have only one variable (such as a diagnostic score) which should support the selection of alternative treatments, and only the “case” cohort data to analyze, then it is clearly more logical to follow any NON-RANDOMNESS in the clinical factors’ manifestation in the score values. Such non-randomness is evidence that something truly observable is happening in the patient’s clinical status/system and — with the appropriate approach — we can take advantage of that.

Discussions

Mon, 02 Mar 2026 07:07:07 +0100