Identification of Tumor Evolution Patterns by Means of Inductive Logic Programming

In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an inductive logic programming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elucidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained on a real-world dataset, we try to give some hints about further approach to the knowledge-driven validations of such hypotheses

A knowledge engineering approach to the recognition of genomic coding regions

ได้ทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนารี ปีงบประมาณ พ.ศ.2556-255

Computational Logic for Biomedicine and Neurosciences

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

Wake-sleep Bayesian program synthesis applications in bioinformatics

Program synthesis is the process of learning mappings between sets of inputs and outputs in a way that generalizes to new inputs. Contrary to deep learning in the gradient descent sense, the goal of program induction is not to 'converge' to a correct solution by performing gradient descent on millions of parameters - rather it is to generate and search for discrete programs that are expressed as combinations of a library of known 'concepts' that will solve the given problem. The goal of this thesis is to explore the portability of program induction onto the bioinformatics domain – specifically the problem of tumor grade prediction. Programs enumerated to predict tumor grade from a data set of colon cancer were up to 76% accurate when the library of primitives was limited to arithmetic, exponential and logarithmic operations. Further work will involve building in models for solving differential equations (another success was to induce Dreamcoder to discover the forward Euler method for solving PDEs), as well as building conceptual representations of n-dimensional spatial data such as images. data such as images.Ope

Computational Logic for Biomedicine and Neuroscience

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis.Nous pr{\^o}nons ici l'utilisation d'une logique calculatoire pour la biologie des systèmes, en tant que cadre \emph{unifié et sûr}, bien adapté à la fois à la modélisation du comportement dynamique des systèmes biologiques,à l'expression de leurs propriétés, et à la vérification de ces propriétés.Les logiques candidates potentielles doivent avoir un pedigree traditionnel en théorie de la preuve (y compris, soit l'induction, soit une présentation en calcul des séquents, avec l'élimination des coupures et des règles ``focales''), et doivent être accompagnées d'outils de preuves certifiés.En plus de fournir un cadre fiable, cela nous permet d'encoder de manière correcte nos systèmes biologiques. Pour la biologie des systèmes en général et la biomédecine en particulier, nous avons jusqu'à présent, pour la partie modélisation, trois logiques candidates : toutes basées sur la logique linéaire.Les propriétés étudiées et leurs preuves sont formalisées dans une logique inductive (non linéaire) très expressive : le Calcul des Constructions Inductives (CIC).Les exemples que nous avons étudiés jusqu'à présent sont relativement simples. Cependant, ils sont tous accompagnés de preuves formelles semi-automatiques dans le système Coq, qui implémente CIC. En neurosciences, nous utilisons directement CIC et Coq pour modéliser les neurones et certains circuits neuronaux simples et prouver certaines de leurs propriétés dynamiques.En biomédecine, l'étude des interactions entre des voies multiomiques,ainsi que les études cliniques et les données des dossiers médicaux électroniques devraient aider à la découverte de médicaments et au diagnostic des maladies.Les travaux futurs portent notamment sur l'utilisation de systèmes de preuves plus automatiques.Cela devrait nous permettre de modéliser et d'étudier des exemples plus réalistes,et à terme de fournir un système pour le diagnostic des maladies et le pronostic thérapeutique